CORPORATE 2 – ARCHIVED - Corporate Training

Offering The Best Solutions To Grow Your Team's Success

TECHNOLOGY | BUSINESS | HEALTHCARE | GOVERNMENT | FINANCIAL

tRAINING

ARTIFICIAL INTELLIGENCE

AI equips you with powerful tools and techniques to tackle complex problems and find innovative solutions.

Generative AI in Software Development
Python for Generative AI
Generative AI for Software Testing and Test Automation
Generative AI
Generative AI Course for Junior Engineers
Generative AI Course for Senior Engineers
Generative AI Course for Management
Python for Data Science, Machine Learning, Artificial Intelligence, and Deep Learning
PySpark and Machine Learning
Practical Machine Learning
Machine Learning with Python

Duration: 35 hours

20 Lessons

This five-day course is designed for software developers who want to explore the applications of generative artificial intelligence (AI) in software development.

Participants will learn how to leverage generative AI techniques to automate tasks, enhance creativity, and streamline software development workflows.

By the end of the course, participants will be equipped with the knowledge and skills to apply generative AI techniques effectively in their software development projects.

Day 1: Introduction to Generative AI in Software Development

Day 2: Generative Models for Data Generation

Day 3: Text and Image Generation

Day 4: Advanced Generative Models

Day 5: Deployment and Integration

Duration: 10 days

10 Lessons

Generative AI refers to a category of artificial intelligence models designed to generate new data similar to the data they were trained on. These models can create content across various domains, including text, images, music, and even code.

Day 1: Generative AI And Its Industry Applications

Day 2: Foundations of Python Programming

Day 3: Python Modules And Object-Oriented Programming

Day 4: Introduction to NumPy and Pandas For Data Manipulation

Day 5: Introduction to TensorFlow and Keras

Day 6: Understanding Generative Adversarial Networks (GANs)

Day 7: Variational Autoencoders (VAEs)

Day 8: Conditional GANs and VAEs

Day 9: Style Transfer and Image Generation

Day 10: Natural Language Processing (NLP) with Generative Models

View the Curriculum

Duration: 48 Hours

12 Lessons

By the end of this course, participants will have gained a deep understanding of how generative AI can revolutionize software testing and test automation practices, enabling them to harness the power of AI to enhance the quality, efficiency, and reliability of their software products.

Module 1: Introduction to Generative AI in Software Testing

Module 2: Fundamentals of Software Testing

Module 3: Overview of Generative AI Techniques

Module 4: Generative AI for Test Data Generation

Module 5: Test Case Generation with Generative AI

Module 6: Fault Injection and Mutation Testing with Generative AI

Module 7: Test Oracle Construction Using Generative AI

Module 8: Enhancing Test Automation with Reinforcement Learning

Module 9: Challenges and Limitations of Generative AI in Software Testing

Module 10: Best Practices and Case Studies

Module 11: Hands-on Labs and Practical Exercises

Module 12: Conclusion and Future Directions

View the Curriculum

These courses are designed to provide participants with a comprehensive understanding of generative artificial intelligence (AI) techniques and algorithms.

Version 1 – View the Curriculum

Version 2 – View the Curriculum

Version 3 – View the Curriculum

Duration: 2 days

6 Lessons

This course is designed for junior engineers who want to learn about generative artificial intelligence (AI) techniques and algorithms. Participants will gain a fundamental understanding of generative models and how they can be applied to generate new data samples, images, text, and other types of content. The course includes hands-on labs to provide practical experience in building and training generative AI models.

This two-day course provides a condensed yet comprehensive introduction to generative AI for junior engineers. Through a combination of lectures and hands-on labs, participants will gain a solid understanding of fundamental generative models and practical experience in building and training generative AI models using Python and popular deep learning frameworks.

DAY 1: INTRODUCTION TO GENERATIVE AI

Module 1: Overview of Generative AI

Module 2: Fundamentals of Probability and Statistics for Generative AI

Module 3: Introduction to GANs

Hands-on Lab: Building and Training a Simple GAN

DAY 2: ADVANCED GENERATIVE MODELS

Module 1: Variational Autoencoders (VAEs)

Module 2: Autoencoders and Variants

Module 3: Adversarial Autoencoders (AAEs)

Hands-on Lab: Training a Variational Autoencoder (VAE)

View the Curriculum

Duration: 14 hours

6 Lessons

This course is tailored for senior engineers seeking an in-depth understanding of generative artificial intelligence (AI) techniques and algorithms.

Participants will delve into advanced generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models. The course includes hands-on labs to provide practical experience in building and training sophisticated generative AI models.

This two-day course provides senior engineers with a deep dive into advanced generative AI concepts and techniques. Through a combination of lectures, hands-on labs, and discussions, participants will gain practical experience and expertise in building and deploying sophisticated generative AI models for various applications.

DAY 1: ADVANCED GENERATIVE MODELS

Module 1: Introduction to Generative AI

Module 2: Generative Adversarial Networks (GANs)

Module 3: Variational Autoencoders (VAEs)

Hands-on Lab: Building and Training GANs and VAEs

DAY 2: ADVANCED TOPICS AND APPLICATIONS

Module 1: Autoregressive Models and Beyond

Module 2: Conditional Generative Models and Style Transfer

Module 3: Advanced Applications of Generative AI

Hands-on Lab: Advanced Generative AI Applications

View the Curriculum

Duration: 1 day

4 Lessons

This one-day course is designed for managers and decision- makers who want to gain a high-level understanding of generative artificial intelligence (AI) techniques and their applications.

Participants will learn about the fundamentals of generative AI, its potential impact on business operations, and how to leverage generative AI technologies effectively in their organizations.

By the end of the course, participants will have gained a comprehensive understanding of generative AI concepts, applications, and strategic considerations.

They will be equipped with the knowledge and insights needed to effectively assess, plan, and implement generative AI initiatives within their organizations.

Module 1: Introduction to Generative AI

Module 2: Applications of Generative AI

Module 3: Generative AI Business Strategy

Module 4: Managing Risks and Ethical Considerations

View the Curriculum

Duration: 10 days

24 Lessons

This bootcamp is designed to provide participants with a comprehensive understanding of Python for various applications in data science, machine learning (ML), artificial intelligence (AI), and deep learning (DL). Participants will learn essential Python programming skills and explore libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and Keras to analyze data, build ML models, and develop AI applications.

This 10-day course outline provides a structured curriculum covering essential topics in Python for data science, machine learning, artificial intelligence, and deep learning. With a combination of lectures, hands-on exercises, and project work, participants will gain practical skills and experience to apply Python effectively in various domains.

DAY 1: INTRODUCTION TO PYTHON FOR DATA SCIENCE

Module 1: Introduction to Python Programming

Module 2: Data Manipulation with NumPy and Pandas

Module 3: Data Visualization with Matplotlib and Seaborn

Hands-on Data Analysis Project

DAY 2: ADVANCED DATA ANALYSIS TECHNIQUES

Module 1: Data Preprocessing and Cleaning

Module 2: Exploratory Data Analysis (EDA)

Module 3: Statistical Analysis with SciPy

Hands-on Data Analysis Project (Continued)

DAY 3: INTRODUCTION TO MACHINE LEARNING

Module 1: Introduction to Machine Learning

Module 2: Supervised Learning Algorithms

Module 3: Model Evaluation and Validation

Hands-on Machine Learning Project

DAY 4: ADVANCED MACHINE LEARNING TECHNIQUES

Module 1: Ensemble Learning Techniques

Module 2: Unsupervised Learning Algorithms

Module 3: Introduction to Deep Learning

Hands-on Deep Learning Project

DAY 5: INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Module 1: Introduction to Artificial Intelligence (AI)

Module 2: Natural Language Processing (NLP)

Module 3: Introduction to Computer Vision

Hands-on AI Project

DAY 6 – 10: ADVANCED TOPICS AND PROJECT WORK

Module 1: Advanced Topics in Data Science and AI

Module 2: Project Work and Mentoring

Module 3: Project Work and Collaboration

Module 4: Project Presentations and Evaluation

View the Curriculum

Duration: 3 days

6 Lessons

Learn about Apache Spark and the Spark 3.0 architecture
Build and interact with Spark DataFrames using Spark SQL
Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively
Read, transform, and understand data and use it to train machine learning models
Build machine learning models with MLlib and ML
Learn how to submit your applications programmatically using spark-submit
ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
Featurization: feature extraction, transformation, dimensionality reduction, and selection
Pipelines: tools for constructing, evaluating, and tuning ML Pipelines
Persistence: saving and load algorithms, models, and Pipelines
Utilities: linear algebra, statistics, data handling, etc

DAY 1

Module 1: Introduction to Spark

Module 2: Resilient Distributed Datasets

DAY 2

Module 3: DataFrames

Module 4: Prepare Data for Modeling

DAY 3

Module 5: Introducing MLlib

Module 6: Introducing the ML Package

View the Curriculum

Duration: 32 Classroom Hours + 38 Lab Hours

16 Lessons

Objective: Practicing Machine Learning Algorithms

SESSION 1

Machine Learning in Nutshell
Supervised and Unsupervised Learning
ML Applications
Evaluating ML techniques
Uses of Machine Learning

SESSION 2

Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation
Data Validation & Modelling
Feature selection Techniques
Dimensionality reduction

SESSION 3

Principal Component analysis (PCA)
Clustering
Hierarchical Clustering &K means
Distance Measure and Data Preparation – Scaling & Weighting

SESSION 4

Evaluation and Profiling of Clusters
Hierarchical Clustering
Clustering Case Study

SESSION 5

Decision Trees
Classification and Regression Trees

SESSION 6

Bayesian analysis and Naïve Bayes classifier
Assigning probabilities and calculating results

SESSION 7

Discriminant Analysis (Linear and Quadratic)
K-Nearest Neighbors Algorithm

SESSION 8

Concept of Model Ensembling
Random forest, Gradient boosting Machines, Model Stacking

SESSION 9

Association rules mining
Apriori and FP-growth algorithms

SESSION 10

Linear Regression
Logistic Regression
Polynomial Regression
Stepwise Regression
Ridge Regression
Lasso Regression
ElasticNet Regression

SESSION 11

Support vector Machines
Basic classification principle of SVM
Linear and Nonlinear classification (Polynomial and Radial)

SESSION 12

Auto-correlation (ACF & PACF)
Auto-regression
Auto-regressive Models
Moving Average Models
ARMA &ARIMA

SESSION 13

ML in Real Time
Algorithm Performance Metrics
ROC and AOC
Confusion Metrix
F1 Score
MSE and MAE

SESSION 14

Recommendation Systems
Data Collection & Storage, Data Filtering
Collaborative Filtering
Factorization Methods
Evaluation Metrics: Recall, Precision, RMSE, Mean Reciprocal Rank, MAP at K, NDCG

SESSION 15

Anomaly detection
Point, Contextual and Collective Anomaly
Supervised and Unsupervised anomaly detection

SESSION 16

DBSCAN Clustering

CASE STUDIES

Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE)
Credit Card Fraud Analysis
Intrusion Detection system

View the Curriculum

Duration: 15 Hours

5 Lessons

MODULE 1

Function Application
Reindexing Python
Iteration
Sorting
Working with Text Data Options & Customization
Indexing & Selecting
Data Statistical Functions
Window Functions
Date Functionality
Time Delta
Categorical Data
Visualization Python Pandas – IO Tools

MODULE 2

Supervised and Unsupervised Learning
Types of Supervised Algorithms
Semi-supervised
Reinforcement Train
Test
Validation Split

MODULE 3

Semi-supervised
Reinforcement Train
Test
Validation Split
R square
Introduction to Scikit Learn
Training Methodology
Hands on Linear Regression
Ridge Regression
Logistics Regression
Precision Recall ROC Curve
F-Score

MODULE 4

Classification: Decision Tree
Cross Validation Bias vs Variance
Ensemble approach Bagging
Boosting Random Forest Variable Importance

MODULE 5

K-Means Clustering
Hierarchical Clustering
Recommender System and Association

PROJECT CASE STUDIES

Income Qualification,
Book Rental Recommendation

View the Currculum

TRAINING

DATA ANALYSIS

Data analysis is essential across various fields such as business, healthcare, social sciences, finance, and more, as it enables organizations to make data-driven decisions, understand market trends, optimize operations, and improve outcomes.

Power BI and Excel
Analytics using R Programming
Business Analytics Using Power BI for Executives
Clinical Data Science
Data Science Using Python
Excel Training for Management and Finance Professionals
Healthcare Analytics and Data Visualization Using Excel and Power BI
Data Warehousing and Business Intelligence
Python, PySpark, RDD, DataFrame

Duration: 8 hours

6 Lessons

To provide an in-depth understanding of Power BI and Excel for data analysis, visualization, and reporting purposes.

Module 1: Introduction to Excel

Module 2: Advanced Excel Functions

Module 3: Introduction to Power BI

Module 4: Power BI Data Modeling

Module 5: Power BI Visualizations

Practical Exercises and Hands-on Labs

View the Curriculum

Duration: 22 hours

4 Lessons

R is an open source environment used for solving the statistical problems. This package is useful for researchers doing research in their respective fields; R will help you in analyzing your experimental results.

In this course, the candidate will learn how to program in R and how to use R for effective data analysis and other statistical inference. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

These modules covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

R For: R programming tool is useful for all the research scholars and business developers for performing the analysis on the scientific data, scholars from following field can use this environment for their effective data analysis;

Commerce
Management
Computer Science
Information Technology
Biological / Life Sciences
Bioinformatics
Biotechnology
Engineering / Technology
Economics
Ergonomics
Medical Sciences etc

View the Curriculum

Duration: 17 hours

5 Lessons

Module 1:

Installation

Installation
Explore Power BI environment and features
Analyze data with Power BI
Power BI building blocks
Basic features of Power BI
Data Filtering
Data Processing

Module 2:

Exploring data
Model data in Power BI
Power BI for consumer
Formulas
Aggregations

Module 3:

Visualization with Power BI
Publish and
Share Report on Mobile

Module 4: Case Study

Module 5: Assignments and Evaluation

View the Curriculum

Duration: 50 hours

16 Lessons

In this course, you will learn how clinical data are generated and the format of this data. You will learn Statistics, SQL, Python, Machine Learning methods with clinical cases.

Completing a clinical data science course can help students enhance their proficiency in analyzing complex clinical data sets. This includes learning advanced statistical techniques, data visualization, and machine learning algorithms.

With a solid foundation in clinical data science, professionals can make more informed decisions based on evidence and data-driven insights. By completing these projects, students can develop their research skills and contribute new knowledge to the field.

DATA SCIENCE WITH PYTHON

Module 1:

Data Types
Variables And Other Basic Elements Control Statements
Date and Time in Python
Arrays and Strings

Module 2:

Lists and Tuples
Dictionaries
Series
DataFrame
Panel
Basic Functionality

Module 3:

NumPy
Pandas
SciPy
Visualization with Matplotlib

Module 4:

Data Wrangling
Web Scrapping
Exploratory Data Analysis
Feature
Engineering
Feature Selection

Module 5:

Project Case Studies: Patient Demographic Analysis
Healthcare Analysis

MACHINE LEARNING WITH PYTHON

Module 1:

Function Application
Reindexing Python
Iteration
Sorting
Working with Text Data Options & Customization
Indexing & Selecting Data Statistical Functions
Window Functions
Date Functionality
Time delta
Categorical Data
Visualization Python Pandas – IO Tools

Module 2:

Supervised and UnSupervised Learning
Types of Supervised Algorithms
Semi-supervised
Reinforcement Train
Test
Validation Split

Module 3:

Semi-supervised
Reinforcement Train
Test
Validation Split
R square
Introduction to Scikit learn
Training methodology
Hands on linear regression
Ridge Regression
Logistics regression
Precision Recall ROC curve
F-Score

Module 4:

Classification: Decision Tree
Cross Validation Bias vs Variance
Ensemble approach Bagging
Boosting Random Forest Variable Importance

Module 5:

K-Means clustering
Hierarchical Clustering
Recommender System and Association
Project case studies: Classification of drugs, prediction of heart disease, Association of Ingredients in Drug, Liver Disease Prediction

SQL

Module 1:

Introduction to SQL, MYSQL installation and setup

Module 2:

Entity relationship and database normalization

Module 3:

Working with database and tables

Module 4:

Working with operators and constraints

Module 5:

Functions and Views

Module 6:

Stored procedures and Triggers

By analyzing data using Python, organizations can make more informed decisions based on insights derived from their data. The outcome of implementing data science with Python is typically improved business performance, increased efficiency, and a deeper understanding of data-driven insights.

Version 1 – View the Curriculum

Version 2 – View the Curriculum

Duration: 8 hours

5 Lessons

To enhance the Excel skills of management and finance professionals for improved data analysis, reporting, and decision-making.

Module 1: Excel Basics for Management and Finance

Overview of Excel and its applications in management and finance
Introduction to Excel interface and basic functions
Understanding spreadsheet organization and navigation
ChatGPT integration

Module 2: Data Analysis and Visualization

Using Excel functions for data analysis (ie. SUM, AVERAGE, COUNT)
Creating Pivot tables for summarizing and analyzing data
Visualizing data trends with charts and graphs

Module 2: Advanced Functions and Formulas

Using Excel functions for data analysis (e.g., SUM, AVERAGE, COUNT)
Creating Pivot tables for summarizing and analyzing data
Visualizing data trends with charts and graphs

Module 3: Advanced Functions and Formulas

Advanced Excel functions (e.g., VLOOKUP, INDEX-MATCH, IF-ELSE)
Combining multiple functions for complex calculations
Writing custom formulas for financial modeling and analysis
ChatGPT Integration

Module 4: Financial Analysis and Modeling

Analyzing financial data using Excel tools
Building financial models for budgeting, forecasting, and scenario analysis
Performing sensitivity analysis and what-if scenarios

Module 5: Dashboard Reporting and Presentation

Designing interactive dashboards for management reporting
Creating professional reports with Excel templates
Tips for effective data visualization and presentation

View the Curriculum

Duration: 30 hours

7 Lessons

Employ cutting edge tools and technologies to analyze healthcare data.

Predict different outcomes based on the data you provide

Apply quantitative modelling and data analysis techniques to the solution of real-world business problems, communicate results, and effectively present these results using data visualization techniques.

Module 1:

Excel Fundamentals: Introduction to Excel environment, basic excel features.
Data handling in Excel Sorting the data: Sort by color, Reverse list, Randomize list
Filter: Number and Text Filters, Date Filters, Advanced filter using Criteria, Remove Duplicates, Outlining Data, Subtotal
Conditional Formatting: Manage Rules, Color Scales, Find Duplicates, Conflicting Rules, Checklist, Checklist, Heat Map
Advance functions: Logical functions, Look up and Reference functions (VLookup, HLookUp, Match, Index and Offset), Statistical functions (SUMIFS, CONUTIFS, PERCENTILE, QUARTILE, STDEV, MEDIAN, RANK)

Module 2:

Pivot Tables: Group Pivot table items, Multi-level Pivot table, Frequency Distribution, Pivot Chart, Slicers, Update Pivot Table, Calculated Field/Item, GetPivotData
What-If Analysis: Data Tables, Goal Seek, Quadratic Equation

Module 3:

Analysis Toolpak: Histogram, Descriptive Statistics, Anova, F-test, T-test, Moving Average, Exponential Smoothing, Correlation, Regression
(All above functions will be covered with respect to Health
Care data)
Patient demographic
Drug content analysis
Diagnosis analysis

Module 4:

Installation
Explore Power BI environment and features
Analyze data with power BI
Power BI building blocks
Basic Features of Power BI
Data Filtering
Data Processing
Exploring data
Model data in Power BI
Power BI for consumer
Formulas
Aggregations
Data Discovery with Power BI Desktop
Transforming Data (Basic Transforms)
Transforming Data (Add Column From Example)
Transforming Data (Appending Queries)
Transforming Data (Merging Queries)
Transforming Data (Combine Files)
Transforming Data (M Query Basics)
Transforming Data (Parameters and Templates)
Transforming Data (Other Query Features)
Introduction to Modeling Data
Creating the Data Model (Modeling Basics)
Creating the Data Model (Model Enhancements)
Creating the Data Model (What If Parameters)
Creating Calculated Columns and Tables (DAX Basics)
Creating Calculated Columns and Tables (Navigation Function)
Creating Calculated Columns and Tables (Calculated Tables)
Creating Calculated Measures (Measure Basics)
Creating Calculated Measures (Time Intelligence Functions)

Module 5:

Introduction to Visualizing Data
Creating Basic Reports with the Power BI Desktop
Creating Interactive Reports (Adding Slicers for Filters)
Creating Interactive Reports (Visualizing Tabular Data)
Creating Interactive Reports (Visualizing Categorical Data)
Creating Interactive Reports (Visualizing Data Trends)
Creating Interactive Reports (Visualizing Categorical and Trend Data Together)
Creating Interactive Reports (Visualizing Geographical Data with Maps)
Creating Interactive Reports (Visualizing Goal Tracking)
Creating Interactive Reports (Using Custom Visuals)
Creating Interactive Reports (Digital Storytelling)
Creating Interactive Reports (Other Features)
Using the Power BI Service (Deploying to the Power BI Service)
Using the Power BI Service (Creating and Sharing Dashboards)
Using the Power BI Service (Using Power BI Q&A)
Using the Power BI Service (Setting up App Workspaces)
Using the Power BI Service (Subscriptions and Alerts)
Using the Power BI Service (Excel Integration)
Using the Power BI Service (Export and Embed Options)
Refreshing the Data (Refreshing Data Overview)
Refreshing the Data (Installing the Data Gateway)
Refreshing the Data (Scheduling a Data Refresh)
Mobile BI (Power BI Mobile Overview)
Mobile BI (Designing Reports and Dashboards for Mobile)
Mobile BI (Interacting with the Power BI Mobile App)
Dashboards
Workbooks
Reports
Datasets

Module 6:

Introduction to DAX
Class Introduction (Class Files)

Module 7:

Data Visualization Case Study using Electronic Medical Record

View the Curriculum

Duration: 8 hours

6 Lessons

To provide comprehensive training on data warehousing concepts and business intelligence tools for improved decision-making and data analysis.

Module 1: Introduction to Data Warehousing

Understanding the basics of data warehousing
Overview of data warehousing architecture
Importance of data warehousing in decision support systems

Module 2: Data Modeling and Design

Dimensional modeling vs. relational modeling
Star schema and snowflake schema design
Best practices for designing data warehouses

Module 3: Extract, Transform, Load (ETL) Process

Data extraction techniques
Data transformation and cleaning processes
Loading data into the data warehouse

Module 4: Introduction to Business Intelligence

Overview of Business Intelligence (BI) tools
Benefits of BI for data analysis and reporting
Popular BI tools in the market

Module 5: Data Visualization in BI

Creating interactive dashboards and reports
Visual representation of data using charts and graphs
Drill-down and filtering options in BI tools

Module 6: Business Intelligence Implementation

Implementing BI solutions in organizations
Integrating BI tools with existing systems
Utilizing BI for strategic decision-making

View the Curriculum

Duration: 5 days

8 Lessons

Learn about Apache Spark and the Spark 3.0 architecture

Learn Big data Concept

Hadoop Ecosystem

Python DataScience

Build and interact with Spark DataFrames using Spark SQL

DAY 1

Module 1: Introduction to Hadoop, Spark and Python

Introduction to Big Data
Hadoop Architecture
Mapper and Reducer
What is Apache Spark?
Spark Jobs and APIs
Spark 3.0 architecture
Using Anaconda, Notebook
Installation and Configuration
Python Introduction
Python Objects
Complex
Boolean
Python DataStructure
list
list methods
tuple
string
string methods
dictionary
Dictionary methods with examples

Module 2: Dictionary Case Study

Control Structure
Functions
glob variale
Variable Argument *arg, **kwarg
Built in Functions
range
lambda
filter
map
reduce
set
zip
Conclusion and Summary

DAY 2

Module 3: Advance Python

File Handling
Exception Handling
List Comprehension
Dictionary Comprehension
Modules
Uer Define Modules
Built in Modules
os
sys
system
glob

Module 4: Introduction to Object Oriented Programming

Class
Methods
Inheritance
Case Study
Iterator
Generator
Regular Expression ( re )
File Handling and Exception Handling
Conclusion and Summary

DAY 3

Module 5: Python Library for DataScience Numpy

Hands on Session
Array Manipulation
Matrix Manipulation
pandas
Hands on Session
Data Series
DataFrame
Case Study
Data Visualisation
Matplotlib
Case Study

DAY 4

Module 6: PySpark/Spark RDD Resilient Distributed Datasets

Internal workings of an RDD
Creating RDDs
Global versus local scope
Transformations Functions
Actions Functions
Hands on Session on RDD and Spark
Assignments 1
Best Practices 1
Project Discussion using Pyspark
Conclusion and Summary

DAY 5

Module 7: DataFrames

Python to RDD communications
Catalyst Optimizer refresh
Speeding up PySpark with DataFrames
Creating DataFrames
Simple DataFrame queries
Interoperating with RDDs
Querying with the DataFrame API
Hands On Session on Pandas DataFrame and PySpark
Assignments 2

Module 8: Prepare Data for Modeling

Checking for duplicates, missing observations, and outliers
Assignments 3
Conclusion and Summary
Group Project Presentation

View the Curriculum

tRAINING

BIG DATA ANALYTICS

Big Data Analytics transforms vast amounts of raw data into meaningful insights, empowering organizations to make informed decisions, optimize operations, and innovate in their respective fields.

Databricks PySpark
Advanced Data Engineering with Databricks

Duration: 40 Hours

10 Lessons

Day 1: Introduction to Databricks and Apache Spark

Overview of Databricks and its features
Introduction to Apache Spark and its ecosystem
Understanding the advantages of using Databricks for big data processing
Session on creating & using Databrick Spark Cluster
Spark architecture – driver program, cluster manager, executors
Spark operations – number of executors, executor memory
Conclusion and Summary

Day 2: Databricks Architecture and Components

Databricks workspace and notebooks
Clusters and cluster management
Introduction to Databricks Runtime and Spark versions
Understanding the Databricks File System (DBFS)
Conclusion and Summary

Day 3: Spark RDD Transformations and Actions

Spark Fundamentals:

RDDs (Resilient Distributed Datasets) and transformations
Actions and lazy evaluation

Transformations (Hands on Session)

More focused session on
filter, groupBy, sortBy, joins – inner, outer, cross, partitionBy,
union, distinct, coalesce, repartition
Brief overview on: map, flatmap, mapPartitions,
mapPartitionsWithIndex, flatmapValues, groupByKey,
reduceByKey
Conclusion and Summary

Day 4: Actions (Hands On Session)

PySpark RDD count, min, max, sum, mean, variance, stdev
PySpark RDD saveAsTextFile, saveAsParequetFile
Reduce, Collect, Keys, Values, Aggregate, First, take, foreach, top

Hands On Session: Basic Word Count Application

correlating with spark map reduce functioning
Sparkf RDD application to problems
basic word count, log file manipulation and statistics, entity resolution
Conclusion and Summary, Interim Test 1

Day 5: Spark SQL and Databricks SQL

Data Manipulation and Processing:

Data ingestion from various sources (CSV, JSON, Parquet, etc.)
Store and load the data using various formats – csv, avro, json, orc, parquet
Data cleaning, filtering, and transformation
Joins, aggregations, and window functions (Self Join, Recursive Join)
Aggregate window functions – avg, count, max, min, sum
Ranking window functions – cume_dist, dense_rank, ntile, percent_rank, rank,
row_number
Value window functions – lead, lag, first_value, last_value
Handling missing data and outliers
Spark SQL data frames and table creation
Spark SQL querying data using Spark Session available/ created as spark
Some example operations and queries
Creating udfs to transformation
Joining the tables

Day 6: NSE Case Study

Day 7-8: Spark Streaming and Structured Streaming

Introduction to Streaming
Architecture
Benefits
How does it work?
Handling streaming data with Databricks,
Introduction to Structured Streaming
Architecture
How does it work?
using DStreams and structured streaming
Input Sources
Sinks
Structured Streaming Operations
Windowing on the Streams
Spark Streaming Versus Structured Streaming
Conclusion and Summary, Interim Test2

Day 9: Delta Lake Case Study

Retrieve Delta table history
History schema
Operation metrics keys
Retrieve Delta table details
Detail schema
Generate a manifest file
Convert a Parquet table to a Delta table
Convert a Delta table to a Parquet table
Shallow clone a Delta table
Remove files no longer referenced by a Delta table
Conclusion and Summary

Day 10: Memory Optimization or Performance Optimization

Performance tuning and optimization techniques
Caching and persistence
Broadcast variables and accumulators
Working with large datasets and out-of-memory data processing
PySpark coding best practices guidelines
Data Engineer Associate Certificate Guideline
Participant Project Review
Conclusion and Summary
Final Test

View the Curriculum

Duration: 5 Days

19 Lessons

This bootcamp is designed for data engineers seeking advanced skills in building and managing data pipelines using Databricks. Participants will learn how to leverage Databricks’ Unified Analytics Platform to perform data engineering tasks efficiently, including data ingestion, transformation, orchestration, and optimization.

DAY 1: INTRODUCTION TO DATABRICKS AND SPARK

Module 1: Introduction to Databricks

Overview of Databricks Unified Analytics Platform
Setting up Databricks workspace and clusters
Creating and managing notebooks

Module 2: Introduction to Apache Spark

Overview of Apache Spark architecture
Working with Resilient Distributed Datasets (RDDs)
Introduction to Spark SQL and DataFrames

Module 3: Data Ingestion in Databricks

Ingesting data from various sources (e.g., S3, Azure Blob Storage, relational databases)
Exploring data formats (CSV, JSON, Parquet, Avro)
Hands-on exercises: ingesting sample datasets into Databricks

Module 4: Data Exploration and Transformation

Exploratory data analysis (EDA) using Databricks notebooks
Data cleaning and preprocessing techniques
Hands-on exercises: performing data transformation tasks using Spark DataFrame API

DAY 2: ADVANCED DATA ENGINEERING TECHNIQUES

Module 1: Advanced Spark SQL Operations

Working with complex data types (arrays, structs, maps)
User-defined functions (UDFs) in Spark SQL
Window functions and analytical queries

Module 2: Data Partitioning and Optimization

Understanding data partitioning and its impact on performance
Techniques for optimizing Spark jobs (e.g., caching, broadcast joins)
Hands-on exercises: optimizing data pipelines in Databricks

Module 3: Introduction to Delta Lake

Overview of Delta Lake and its features
ACID transactions and data versioning
Hands-on exercises: working with Delta tables in Databricks

Module 4: Orchestrating Workflows with Databricks Jobs

Creating and scheduling jobs in Databricks
Monitoring job performance and execution history
Hands-on exercises: scheduling data pipeline jobs in Databricks

DAY 3: DATA ENGINEERING BEST PRACTICES

Module 1: Introduction to Structured Streaming

Overview of Apache Spark Structured Streaming
Working with streaming DataFrames
Hands-on exercises: building streaming pipelines in Databricks

Module 2: Managing Data Pipelines with MLflow

Overview of MLflow and its components
Tracking experiments, packaging, and deploying models
Hands-on exercises: managing machine learning pipelines with MLflow

Module 3: Data Engineering Best Practices

Design patterns for building scalable and reliable data pipelines
Error handling and fault tolerance strategies
Optimization techniques for improving pipeline performance

Modules 4: Real-World Data Engineering Use Cases

Case studies and examples of data engineering projects
Best practices for handling common data engineering challenges
Q&A and open discussion on real-world scenarios

DAY 4: ADVANCED TOPICS IN DATABRICKS

Module 1: Introduction to Machine Learning Pipelines in Databricks

Overview of MLlib and ML packages in Spark
Building end-to-end machine learning pipelines
Hands-on exercises: building and deploying ML pipelines in Databricks

Module 2: Advanced Databricks Features

Introduction to Databricks Runtime
AutoML and hyperparameter tuning with Databricks AutoML
Hands-on exercises: exploring advanced features of Databricks workspace

Module 3: Scaling Data Engineering Workloads

Strategies for scaling data engineering workloads in Databricks
Autoscaling clusters and optimizing resource allocation
Hands-on exercises: scaling data pipelines for performance and efficiency

Module 4: Monitoring and Performance Tuning

Monitoring and logging techniques in Databricks
Performance tuning and optimization strategies
Hands-on exercises: monitoring and optimizing data pipelines in Databricks

DAY 5: CAPSTONE PROJECT AND FINAL ASSESSMENT

Module 1: Capstone Project

Participants work on a comprehensive data engineering project using Databricks
Project scope includes data ingestion, transformation, orchestration, and optimization
Guidance and support provided by instructors for project implementation

Module 2: Project Presentations and Evaluation

Participants present their capstone projects to the class and instructors
Projects are evaluated based on completeness, accuracy, scalability, and adherence to best practices
Feedback provided to participants for further improvement and learning

Module 3: Course Conclusion and Certification

Recap of key concepts and takeaways from the bootcamp
Distribution of course completion certificates to participants

View the Curriculum

TRAINING

IT AUTOMATION

IT Automation is the use of software and systems to create repeatable instructions and processes to replace or reduce human interaction with IT systems.

Ansible, GIT & Terraform
DevOps Training Program – Parts 1 & 2
Docker and Kubernetes
Comprehensive DevOps : Beginner, Intermediate, Advanced

Duration: 100 hours

15 Lessons

Use of Ansible to automate the configuration management process, GIT for version control and Terraform to automate the infrastructure provisioning.

Module 1: GIT Introduction

Introduction
Local and remote repositories
Installation of GIT
Initialize a GIT repository
GIT log

Module 2: GIT Branches

GIT branches concept
Use of GIT branches
Why are branches important in GIT
GIT merging branches concept
Use of different branches in GIT

Module 3: Initialize Remote Repositories in GIT

How to initialize remote repositories
Pushing concept
How to push to remote repositories
What is cloning in GIT?
Cloning remote repositories
Understanding Pull Requests
Fetching and Pulling in GIT
Why merge conflicts occurs and how to resolve those

Module 4: Rebasing in GIT

What is rebasing in GIT
Interactive Rebasing
Cherry Picking in GIT

Module 5: Resetting and Reverting in GIT

Resetting and Reverting in GIT
Stashing concept and how to use it
Reflog in GIT

Module 6: Introduction to Ansible

Ansible introduction
Setup ansible in local environment using few virtual machines
Install ansible on virtual machines
Configuration of Ansible on virtual machines

Module 7: Ansible Concepts

Ansible Inventory and its usage
Ansible Playbooks and Facts
Configuration files in Ansible
Ansible Modules such as Packages, Services, Firewall rules, Users and groups etc
Ansible Variables Precedence, Scope and Register

Module 8: Installation and Configuration of Ansible

Install required packages, create static host inventory file, create config file
Create and distribute SSH keys to managed nodes
Validate a working configuration using ad-hoc Ansible commands
Create simple shell scripts that tun ad hoc Ansible commands
Privilege Escalation in Ansible
Understanding YAML, its syntax and errors in Playbooks

Module 9: Conditionals, Loops & Roles in Ansible

Ansible Conditionals
Ansible Loops
Ansible Roles and its creation

Module 10: Introduction to Terraform

Introduction to Terraform
Introduction to Infrastructure as Code
Challenges with Traditional IT Infrastructure
Why Terraform?
Types of IAC Tools

Module 11: Getting Started with Terraform

Installing Terraform
HashiCorp Configuration Language (HCL) basics
Update and destroy infrastructure

Module 12: Terraform Basics

Understanding and using Terraform providers
Use input variables
Resource attributes and dependencies
Use of output variables
Introduction to Terraform state
Purpose of State and its considerations

Module 13: Working with Terraform

Terraform commands
Mutable vs Immutable Infrastructure
Lifecycle Rules and Data sources in Terraform
Count and for each
Version Constraints

Module 14: Terraform with AWS

Getting started with AWS
Initializing and configuring the AWS Account with Terraform
Programmatic access and use of IAM
AWS IAM with Terraform
AWS EC2 with Terraform
AWS S3 with Terraform

Module 15: Monitoring and Alerting with Prometheus

Introduction to monitoring systems
Prometheus architecture and components
Setting up and configuring Prometheus for monitoring applications and infrastructure
Prometheus Configuration
Authentication/Encryption
Metrics in Prometheus

View the Curriculum

Duration: 80 hours

9 Lessons

By completing both parts of this comprehensive DevOps training program, you will gain a solid foundation in DevOps principles, essential tools, and best practices.

You will develop practical skills in Linux, AWS, Git, Terraform, Terraform Cloud, Ansible, Prometheus, and monitoring, enabling you to effectively collaborate between development and operations teams, automate infrastructure, and enhance software delivery processes.

PART 1: BASIC DEVOPS (40 HOURS)

Module 1: Introduction to DevOps

Understanding the DevOps culture and principles
Benefits and challenges of implementing DevOps
DevOps tools and technologies overview

Module 2: Linux Fundamentals

Introduction to Linux operating system
Linux command line basics and navigation
File system management and permissions

Module 3: AWS Fundamentals

Introduction to Amazon Web Services (AWS)
Overview of essential AWS services and functionalities
Setting up and managing AWS resources

Module 4: Version Control with GIT

Introduction to version control systems
Git fundamentals and workflows
Collaborative development with Git

PART 2: ADVANCED DEVOPS (40 hours)

Module 5: Infrastructure as Code with Terraform

Introduction to Infrastructure as Code (IaC) concept
Terraform fundamentals and syntax
Managing infrastructure with Terraform

Module 6: Terraform Cloud

Leveraging Terraform Cloud for collaboration and remote state management
Workspaces and environment management
Continuous integration and delivery with Terraform

Module 7: Configuration Management with Ansible

Introduction to Ansible and its role in configuration management
Ansible basics and playbook creation
Automating server configuration and deployment with Ansible

Module 8: Monitoring and Alerting with Prometheus

Introduction to monitoring systems
Prometheus architecture and components
Setting up and configuring Prometheus for monitoring applications and infrastructure

Module 9: Real-Life Examples and Case Studies

Hands-on exercises and projects based on real-world scenarios
Implementing DevOps best practices in a practical setting
Analyzing case studies to understand successful DevOps implementations

View the Curriculum

Duration: 100 hours

9 Lessons

Build, test, and deploy Docker applications with Kubernetes while learning production-style development workflows.

Learning Docker, Kubernetes and its benefits and usage in the market.

Module 1: Understanding the Benefits of Containers

Introduction to Containers
Introduction to Docker
Building Container Images
Container Registries
Running Containers
Docker Container Health checks
Linking Docker Containers

Module 2: Dive into Docker

Why use Docker
What is Docker?
Docker for Mac/Windows/Linux
Using the Docker Client
But Really…What a Container?
How’s Docker Running on Your Computer?
Installing Docker on MacOS
Installing Docker with WSL2 on Windows 10 Home and Pro
Installing Docker for Windows Professional with HyperV
Installing Docker on Linux

Module 3: Manipulating Containers with the Docker Client

Docker Run in Detail
Overriding Default Commands
Listing Running Containers
Container Lifecycle
Restarting Stopped Containers
Removing Stopped Containers
Retrieving Log Outputs
Stopping Containers
Multi-Command Containers
Executing Commands in Running Containers
The Purpose of the IT Flag
Getting a Command Prompt in a Container
Starting with a Shell
Container Isolation

Module 4: Docker Compose

Docker-Compose: What & Why?
Creating a Compose File, Diving into the Compose File Configuration
Installing Docker Compose on Linux
Docker Compose Up & Down
Need for Nginx, Running Nginx
Multi-Step Docker Builds, Implementing Multi-Step Builds
Working with Multiple Containers, adding another Container
Building Images & Understanding Container Names
Images & Containers
Data, Volumes & Networking
Introducing Volumes, Combining & Merging Different Volumes
A Look at Read-Only Volumes
Managing Docker Volumes

Module 5: Docker Hub & Docker Swarm

Docker Registry
Docker Hub, what is it really?
Docker hub Registry Alternatives
Portus, an Enterprise Docker Registry
Docker Swarm
Docker Swarm Services
Docker Compose vs Docker Stack Deploy
Docker Secrets & Configs

Module 6: Understanding Kubernetes Architecture

Container Orchestration
Kubernetes Architecture
Introduction to Kubernetes Objects
Basic Kubernetes Objects
Kubectl CLI
Using Kubernetes

Module 7: Kubernetes Cluster Installation & Configuration

Cluster Installation and configuration
Use Kubeadm to install a basic cluster
Manage a highly-available Kubernetes cluster
Provision underlying infrastructure to deploy a Kubernetes cluster

Module 8: Workloads & Scheduling in Kubernetes

Understand deployments and how to perform rolling update and rollbacks
Know how to scale applications
Understand the primitives used to create robust, self healing, application deployments
Understand how resource limits can affect Pod scheduling

Module 9: Services & Networking in Kubernetes

Understand host networking configuration on the cluster nodes
Understand connectivity between Pods
Understand ClusterIP, NodePort, Load Balancer service types and endpoints
Know how to configure and use CoreDNS

View the Curriculum

Duration: 120 Hours

19 Lessons

Upon completion of all three modules, students will have a comprehensive understanding of DevOps principles, practices, tools and will be well-prepared to implement DevOps strategies in a professional environment.

MODULE 1 – BEGINNER

Introduce students to fundamentals of DevOps, including basic tools, concepts and practices.

Introduction to DevOps

What is DevOps?
The History and Evolution of DevOps
DevOps Principles and Practices
Benefits of DevOps

DevOps Tools and Environment Setup

Overview of Common DevOps Tools
Setting Up a Development Environment (Linux, Windows)
Introduction to Virtualization and Containers (Docker)
Version Control with Git and GitHub

Continuous Integration (CI) Basics

Introduction to Continuous Integration
Setting Up a CI Pipeline
Using Jenkins for CI
Basic Build Automation (Maven, Gradle)

Configuration Management

Introduction to Configuration Management
Using Ansible for Configuration Management
Managing Infrastructure as Code (IaC)
Basic Playbook and Roles in Ansible

Continuous Delivery (CD) and Deployment

Introduction to Continuous Delivery
Setting Up a CD Pipeline
Using Jenkins for Continuous Deployment
Introduction to Kubernetes for Deployment

Monitoring and Logging

Importance of Monitoring and Logging
Using Prometheus for Monitoring
Setting Up Grafana Dashboards
Introduction to Log Management (ELK Stack)

Practical Lab Exercises

Setting Up a Simple CI/CD Pipeline
Automating Configuration Management with Ansible
Deploying Applications with Docker and Kubernetes
Basic Monitoring and Logging Setup

MODULE 2 – INTERMEDIATE

Build on foundational knowledge with more advanced DevOps practices, tools, and techniques.

Advanced Version Control and Collaboration

Advanced Git Techniques (Rebasing, Cherry-Picking)
Branching Strategies (GitFlow, Trunk-Based Development)
Code Review Practices
Managing Git Repositories with GitHub/GitLab

Advanced CI/CD Pipelines

Advanced Jenkins Configuration
Pipeline as Code (Jenkinsfile)
Using CircleCI and Travis CI
Blue/Green Deployments and Canary Releases

Advanced Configuration Management

Advanced Ansible Playbooks
Using Chef and Puppet for Configuration Management
Managing Secrets and Credentials
Automating Infrastructure Provisioning with Terraform

Container Orchestration with Kubernetes

Deep Dive into Kubernetes Architecture
Kubernetes Networking and Storage
Managing Kubernetes Clusters
Helm for Kubernetes Package Management

Cloud Infrastructure and Services

Overview of Cloud Providers (AWS, Azure, GCP)
Deploying Infrastructure on AWS
Using AWS Services (EC2, S3, RDS)
Introduction to Serverless Computing (AWS Lambda)

Security in DevOps

Introduction to DevSecOps
Integrating Security into CI/CD Pipelines
Container Security Best Practices
Security Tools and Practices (Aqua, Twistlock)

Practical Lab Exercises

Implementing Advanced CI/CD Pipelines
Automating Infrastructure with Terraform
Deploying and Managing Kubernetes Clusters
Setting Up Secure DevOps Pipelines

MODULE 3 – ADVANCED

Prepare students to tackle complex DevOps challenges and implement robust, scalable DevOps practices in real-world scenarios.

Advanced Monitoring and Performance Tuning

Advanced Prometheus and Grafana Usage
Performance Tuning and Optimization
Using APM Tools (New Relic, Datadog)
Setting Up Alerting and Incident Management

Scalable and Highly Available Infrastructure

Designing for Scalability and High Availability
Load Balancing and Auto-Scaling
Disaster Recovery and Backup Strategies
Using CloudFormation and Terraform for Infrastructure Automation

Advanced Containerization Techniques

Advanced Docker Features and Best Practices
Multi-Stage Builds and Optimizing Docker Images
Using Kubernetes Operators
Service Mesh with Istio

DevOps for Microservices

Introduction to Microservices Architecture
Deploying Microservices with Kubernetes
Service Discovery and API Gateway (Consul, Kong)
Monitoring and Managing Microservices

Advanced Cloud Native DevOps

Advanced AWS, Azure, and GCP Services
Multi-Cloud and Hybrid Cloud Strategies
Serverless DevOps Practices
Cloud Cost Management and Optimization

DevOps Culture and Transformation

Building a DevOps Culture
Leading DevOps Transformation
Metrics and KPIs for DevOps
Case Studies of Successful DevOps Implementations

Capstone Project

Designing and Implementing a Comprehensive CI/CD Pipeline
Deploying and Managing a Scalable, Highly Available Application
Integrating Security, Monitoring, and Logging
Presenting the Project and Peer Review

View the Curriculum

tRAINING

SOFTWARE DEVELOPMENT

Software development is a dynamic and complex process that involves a combination of technical skills, methodologies, tools, and best practices to create high-quality software solutions tailored to meet specific needs and solve real-world problems.

Java Programming
Python Programming Essentials
GitHub Copilot for Software Developers
Scala Play Framework Programming
Python Bootcamp
Beginner Python
Intermediate Python
Advanced Python
Basic .NET and C# Programming
Intermediate Course for .NET and C# (.NET)
Advanced Course: .NET and C# .NET
Basic Java Programming
Intermediate Java Programming
Advanced Java
JavaScript Essentials

Duration: 8 Hours

7 Lessons

Module 1: Introduction to Java Programming

Overview of Java and its features
Setting up the Java development environment
Basics of Java syntax and structure

Module 2: Data Types and Variables

Primitive data types in Java
Declaring and initializing variables
Understanding variable scope and lifetime

Module 3: Control Flow Statements

Conditional statements (if-else, switch)
Looping statements (for, while, do-while)
Using break and continue statements

Module 4: Object-Oriented Programming in Java

Classes and objects
Inheritance, polymorphism, and encapsulation
Abstract classes and interfaces

Module 5: Exception Handling

The try-catch block
Handling exceptions using throw, throws, and finally
Custom exception classes

Module 6: File Handling in Java

Reading from and writing to files
Handling file input-output operations
Working with streams and buffers

Module 7: Java Collections Framework

Overview of collections (List, Set, Map)
Using ArrayList, HashSet, HashMap
Iterating through collections and using iterators

View the Curriculum

Duration: 4 Days

9 Lessons

Module 1: Introduction to Python

Module 2: Flow Control

Module 3: Python Functions

Module 4: Python Native Datatypes and File Handling

Module 5: Class & Objects in Python (OOPS)

Module 6: Unit Test with Python

Module 7: Introduction to PyUnit

Module 8: NumPy Library

Module 9: SciKit Library

View the Curriculum

Duration: 52 Hours

13 Lessons

GitHub Copilot is an AI coding assistant that helps you write code faster and with less effort, allowing you to focus more energy on problem-solving and collaboration.

GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor. As you type, Copilot offers autocomplete-style suggestions, sometimes completing the current line and other times providing a whole new block of code.

Module 1: Introduction to GitHub Copilot

Understanding the fundamentals of GitHub Copilot and its role in code generation
Overview of the AI and machine learning techniques powering GitHub Copilot
Exploring the potential impact of GitHub Copilot on software development workflows

Module 2: Getting Started with GitHub Copilot

Setting up GitHub Copilot in popular code editors (e.g., VS Code, JetBrains IDEs)
Exploring the user interface and basic functionalities of GitHub Copilot
Configuring settings and preferences for optimal code suggestions

Module 3: Understanding GitHub Copilot Models

Overview of the underlying models and datasets used by GitHub Copilot
Exploring model architectures, training data, and model capabilities
Understanding model limitations and potential biases

Module 4: Leveraging GitHub Copilot for Code Generation

Using GitHub Copilot to generate code snippets, functions, and classes
Exploring common programming tasks and scenarios where GitHub Copilot can assist
Best practices for reviewing and integrating Copilot-generated code into projects

Module 5: Collaborating with GitHub Copilot

Collaborative coding workflows with GitHub Copilot in team environments
Integrating Copilot into version control systems (e.g., Git) and collaborative coding platforms
Strategies for code review, feedback, and collaboration when using Copilot-
generated code

Module 6: Customizing and Extending GitHub Copilot

Configuring Copilot to suit your coding style, preferences, and project requirements
Creating custom code templates and snippets for frequent tasks
Extending Copilot’s capabilities with custom models and datasets

Module 7: Enhancing Software Development Workflows with GitHub Copilot

Integrating Copilot into existing software development workflows
Automating repetitive tasks and boilerplate code generation with Copilot
Using Copilot to explore new programming languages, libraries, and frameworks

Module 8: Best Practices for Using GitHub Copilot

Guidelines for effective use of GitHub Copilot in software development projects
Addressing security, licensing, and intellectual property concerns when using Copilot-generated code
Strategies for evaluating and validating Copilot suggestions

Module 9: Addressing Challenges and Limitations

Identifying common challenges and limitations of GitHub Copilot
Strategies for mitigating potential risks, biases, and errors in Copilot-generated code
Balancing automation with human oversight in software development workflows

Module 10: Ethical and Responsible AI in Copilot Development

Understanding ethical considerations and biases in AI-powered code generation
Promoting fairness, transparency, and accountability in Copilot usage
Compliance with privacy regulations and data protection laws when using Copilot

Module 11: Case Studies and Success Stories

Real-world case studies showcasing successful implementations of GitHub Copilot
Best practices and lessons learned from organizations adopting Copilot in their
development workflows
Opportunities for innovation and collaboration with Copilot in diverse software projects

Module 12: Future Trends and Emerging Technologies

Exploring future trends and advancements in AI-assisted software development
Emerging technologies shaping the future of code generation and automation
Opportunities for research and innovation in AI-driven development tools like GitHub Copilot

Module 13: Conclusion and Next Steps

Recap of key learnings and takeaways from the course
Resources for further learning, community engagement, and professional development in GitHub Copilot usage
Actionable steps for incorporating GitHub Copilot into your software development workflows and driving innovation in code generation.

View the Curriculum

Duration: 40 Hours

12 Lessons

DAY 1

Module 1: Scala Introduction

Scala History
Scala Features
Scala Hello Program
Variable and Data Type
Type Inference
Scala Comments
Conditional
Expressions
Scala While Loop
for Loops
The foreach method
Using for and foreach with Maps
for (enumerators) yield example
Scala Break Statement
Scala Array
Multidimensional Array
Scala Tuples
Scala Tuple Example
Scala List
Scala List Example
Scala String
Scala String Methods
Scala String Interpolation

Assignments: Scala Other Collections References

Lists and Tuples
Dictionaries
Series
DataFrame
Panel
Basic Functionality

DAY 2

Module 1: Scala Functions

User Define Functions
Recursion
Call-by-Name and Call-by-Value
Default and Named Arguments

Module 2: Scala OOPs Concepts

Scala Object and Classes
Singleton and Companion Object
Scala Constructors
Scala Method Overloading
Scala Inheritance
Scala Method Overriding
Scala Field Overriding

Module 3: Advance OOPs

Scala Abstract Class
Why Scala Abstract Classes?
Usage of Abstract classes
Scala Trait Class
Scala Trait Mixins
Scala Access Modifiers
Case Classes & Objects
Case Class example
Anonymous Classes
Anonymous Class Example
GENERIC CLASSES
Generic class example
Mind-Map Summary and Conclusion

DAY 3

Module 1: Pattern Matching

Matching on case classes

Module 2: Scala Exceptions

Scala Exception Handling
Scala Try Catch Block
Scala Finally Block
Scala Throw keyword
Scala Throws keyword
Scala Custom Exception

Module 3: Functional Programming

Higher Order Functions
Pure Functions
Examples of pure functions
Examples of impure functions
Functional Concurrent Programming
Implicits
Introductions to Implicits
Organizing Implicits
Mind-Map Summary and Conclusion

DAY 4

Module 1: Scala Play Framework Introduction

Introduction
Module Outline
What We Will Build
History of Play!
Philosophy
Technologies
Summary
Introduction
Downloading Play!
The Play Command
Compiling and Hot Deploy
Testing
IDE’s
Project Structure
Configuration
Error Handling
Summary

Module 2: Routing

Introduction
The Router
Router Mechanics
Routing Rules
Play! Routes
Play! Routes: HTTP Verbs
Play! Routes: The Path
Play! Routes: The Action Call
Routing in Action
Summary

Module 3: Controllers, Actions and Results

Introduction
Controllers
Actions
Results
Session and Flash Scope
Request Object
Implementing the Contacts Stub Controller
Summary

DAY 5

Module 1: Scala Play Views

Introduction
Play! Views
Static Views
Passing Arguments
Iteration
Conditionals
Partials and Layouts
Accessing the Session Object
The Asset Route
Play Project and Assignment
Mind-Map Summary and Conclusion

View the Curriculum

Duration: 5 Days

20 Lessons

This course outline provides a comprehensive curriculum covering fundamental to advanced Python concepts, practical applications, and real-world project work, enabling participants to develop proficiency in Python programming within a span of five intensive days.

By fulfilling these prerequisites and lab requirements, participants will be well-prepared to engage effectively with the Python Bootcamp curriculum and develop proficiency in Python programming.

DAY 1: INTRODUCTION TO PYTHON BASICS

Module 1: Introduction to Python

Overview of Python programming language
Setting up Python environment (interpreter, IDE)
Writing and executing Python scripts

Module 2: Python Basics Part 1

Data types: integers, floats, strings, lists, tuples, dictionaries
Variables and assignments
Basic arithmetic and string operations

Module 3: Python Basics Part 2

Control flow: if statements, loops (for and while), conditional expressions
Functions: defining functions, parameters, return values

Module 4: Python Basics Part 3

Input/output operations: reading from/writing to files
Exception handling: try-except blocks
Hands-on exercises and practice problems

DAY 2: INTERMEDIATE PYTHON CONCEPTS

Module 1: Intermediate Data Structures

Lists: list comprehensions, slicing, common list methods
Tuples: immutability, packing/unpacking
Dictionaries: key-value pairs, dictionary comprehensions

Module 2: Advanced Control Flow

Nested loops and conditionals
Break, continue, and pass statements
Enumerate and zip functions

Module 3: Advanced Functions

Lambda functions and anonymous functions
Higher-order functions: map, filter, reduce
Function decorators

Module 4: Object-Oriented Programming (OOP) in Python

Classes and objects
Inheritance, encapsulation, and polymorphism
Hands-on OOP exercises and projects

DAY 3: PYTHON LIBRARIES AND APPLICATIONS

Module 1: Introduction to Python Standard Library

Overview of built-in modules and functions
Commonly used modules: os, sys, math, random

Module 2: Introduction to External Libraries

Installing and managing external libraries using pip
Exploring popular Python libraries: NumPy, pandas, matplotlib

Module 3: Introduction to Web Development with Flask

Setting up Flask environment
Creating routes and handling requests
Templating with Jinja2

Module 4: Building a Simple Web Application

Creating a CRUD (Create, Read, Update, Delete) application
Integrating Flask with databases (SQLite)

DAY 4: ADVANCED PYTHON TOPICS

Module 1: Concurrency and Parallelism

Multithreading vs. multiprocessing
Thread synchronization and locks
Asynchronous programming with async/await

Module 2: Error Handling and Debugging

Handling exceptions effectively
Logging and debugging techniques
Unit testing with unittest module

Module 3: Python Best Practices and Code Optimization

Writing clean and maintainable code
Performance optimization techniques
Code profiling and optimization tools

Module 4: Introduction to Data Analysis and Visualization

Data manipulation with pandas
Data visualization with matplotlib and seaborn
Hands-on data analysis projects

DAY 5: REAL-WORLD APPLICATIONS AND PROJECT WORK

Module 1: Introduction to Data Science and Machine Learning

Overview of data science lifecycle
Introduction to machine learning concepts
Exploring popular machine learning libraries: scikit-learn, TensorFlow, Keras

Module 2: Building a Machine Learning Model

Data preprocessing and feature engineering
Model training and evaluation
Model deployment and inference

Module 3: Introduction to Automation with Ansible

Overview of Ansible and its architecture
Writing Ansible playbooks for automation tasks
Hands-on Ansible exercises

Module 4: Capstone Project

Participants work on a final project that integrates various Python concepts learned throughout the bootcamp
Project presentations and peer feedback

View the Curriculum

Duration: 40 Hours

9 Lessons

Module 1: Course Introduction

Introduction of Data science and its application in Day to Day life
Course overview and description

Module 2: Basic Python and Mathematics for Data Science

Introduction of python programming language
Installation of Anaconda Distribution and other python IDE Python objects, number & Booleans, strings
Container objects, mutability of objects
Operators – Arithmetic, Bitwise, comparison and assignment operators, operators precedence and associativity
Conditions (If else,if-elif-else) , Loops (While, for)
Break and continue statement and range function

Module 3: String Objects and Collections

String object basics
String methods
Splitting and joining strings
String format functions
List object basics list methods
List as stack and queues
List comprehensions

Module 4: Tuples, Set, Dictionaries & Functions

Tuples, sets, dictionary object basics, dictionary object methods, dictionary view objects
Functions basics, parameter passing, Iterators. Generator functions
Lambda functions
Map, reduce, filter functions

Module 5: OOPS Concepts & Working with Files

OOPS basic concepts
Creating classes and objects inheritance
Multiple inheritance
Working with files
Reading and writing files
Buffered read and write
Other file methods

Module 6: Exception Handling & Database Programming

Using standard module
Creating new modules
Exceptions handling with try-except
Creating, inserting and retrieving table
Updating and deleting the data

Module 7: Python Pandas Modules

Series
Data frame
Panel
Basic functionality

Module 8: Function Application

Reindexing Python
Iteration
Sorting
Working with text data options & customization
Indexing & selecting
Data statistical functions
Window functions
Date functionality
Time delta
Categorical data
Visualization Python Pandas – IO Tools

Module 9: Python NumPy

ND array object
Data types
Array attributes
Array creation routines
Array from existing data array from numerical ranges
Indexing & slicing
Advanced indexing
Broadcasting
Iterating over array
Array manipulation
Binary operators
String functions
Mathematical functions
Arithmetic operations
Statistical functions
Sort, search & counting functions
Byte swapping
Copies & views
Matrix library
Linear algebra

View the Curriculum

Duration: 35 Hours

10 Lessons

This intermediate Python course aims to build upon basic Python skills and delve deeper into advanced concepts and techniques.

Participants will enhance their proficiency in Python programming, understand more complex data structures and algorithms, and be prepared to tackle more sophisticated projects and challenges.

Deepen understanding of advanced Python topics crucial for software development and data science.

Master data manipulation techniques and algorithms for efficient programming.

Develop problem-solving skills through challenging exercises and projects.

Advanced Functions

Lambda functions and functional programming concepts.
Decorators, closures, and higher-order functions.

Object-Oriented Programming (OOP) in Depth

Advanced OOP concepts (inheritance, polymorphism, encapsulation)
Abstract base classes (ABCs) and method resolution order (MRO).

Data Structures: Advanced Lists and Collections

List comprehensions revisited and advanced techniques.
Using collections module (deque, defaultdict, namedtuple).

File Handling and CSV Processing

Reading and writing CSV files.
Handling exceptions and edge cases in file operations.

Advanced Topics in Python Standard Library

Working with datetime and time modules.
Using itertools for advanced iterators and generators.

Regular Expressions in Python

Syntax and usage of regular expressions.
Practical examples and applications in data processing.

Database Access with Python

Connecting to databases (SQLite, MySQL, or PostgreSQL).
CRUD operations using Python DB-API.

Concurrency and Parallelism

Threading vs. multiprocessing in Python.
Using concurrent.futures for parallel execution.

Web Scraping with Python

Basics of web scraping and HTML parsing.
Handling dynamic content and using Selenium (if applicable).

Introduction to Data Visualization

Overview of data visualization libraries (Matplotlib, Seaborn).
Creating basic plots and customizing visualizations.

View the Curriculum

Duration: 40 Hours

10 Lessons

The Advanced Python course aims to equip participants with in-depth knowledge and skills in Python programming, focusing on advanced concepts and techniques.

By the end of the course, participants will be proficient in leveraging Python’s powerful features for complex software development, data analysis, and more.

Master advanced Python topics essential for professional software development and data science.

Enhance problem-solving skills and ability to design scalable applications.

Gain practical experience through projects and case studies.

Object-Oriented Programming (OOP) in Python

Classes, objects, and instances.
Inheritance, polymorphism, and encapsulation.

Advanced Data Structures

Advanced usage of lists, dictionaries, tuples, and sets.
Custom data structures and their applications.

Functional Programming

Lambda functions, map, filter, and reduce.
Decorators and closures.

Concurrency and Multithreading

Threading vs. multiprocessing.
Synchronization and thread safety.

Python Generators and Iterators

Creating iterators and generators.
Using yield for lazy evaluation.

Regular Expressions

Pattern matching using regex in Python.
Practical examples and use cases.

Advanced File Handling

Reading and writing CSV, JSON, and XML files.
Working with large files and memory management.

Database Access with Python

Connecting to databases (SQLite, MySQL, or PostgreSQL) using libraries like
SQLAlchemy.
Executing SQL queries, managing transactions, and working with ORM
frameworks.

Web Scraping with Python

Using BeautifulSoup and Scrapy frameworks.
Handling HTTP requests and parsing HTML.

Introduction to Data Science Libraries

Using NumPy for numerical computations.
Introduction to Pandas for data manipulation and analysis.

View the Curriculum

Duration: 31 Hours

10 Lessons

The Basic .NET and C# Programming course aims to provide participants with a foundational understanding of .NET framework concepts and practical skills in C# programming.

By the end of the course, participants will be able to develop basic .NET applications using C#.

Gain proficiency in C# programming language.

Understand the fundamentals of .NET framework.

Develop skills to create basic desktop and web applications.

Prepare for advanced .NET and C# courses or certifications.

Introduction to .NET Framework

Overview of .NET framework architecture
Common Language Runtime (CLR) and Common Type System (CTS)
Introduction to .NET languages (C#, VB.NET)

Getting Started with C#

Basics of C# syntax and structure
Data types, variables, and constants
Control flow statements (if-else, switch)

Object-Oriented Programming Concepts

Classes and objects
Encapsulation, inheritance, and polymorphism
Constructors and destructors

Collections and Generics

Arrays, Lists, and Dictionaries
Introduction to generics in C#

Exception Handling

Handling exceptions using try-catch-finally
Custom exceptions

File I/O and Streams

Reading and writing files
Working with streams in C#

Introduction to Windows Forms

Creating a basic Windows Forms application
Event-driven programming in Windows Forms

Introduction to ASP .NET Core

Overview of ASP.NET Core framework
Building a simple web application using ASP.NET Core

Database Connectivity with ADO .NET

Connecting to databases using ADO.NET
Executing SQL commands and retrieving data

Introduction to LINQ

Basics of LINQ (Language Integrated Query)
Querying collections using LINQ

View the Curriculum

Duration: 35 Hours

10 Lessons

This course aims to deepen participants’ understanding and proficiency in .NET framework and C# programming language, equipping them with intermediate-level skills to develop robust applications.

Participants will gain hands-on experience in advanced topics such as asynchronous programming, LINQ, and MVC architecture.

Enhanced proficiency in .NET framework and C# programming.

Ability to design and implement scalable applications.

Preparation for advanced .NET certifications.

Career advancement opportunities in software development roles.

Advanced C# Programming

Delegates and Events
Generics
Lambda Expressions
Nullable Types

Asychronous Programming in C#

Asynchronous Methods
Async and Await Keywords
Task Parallel Library (TPL)

LINQ (Language Integrated Query)

Query Expressions
Standard Query Operators
LINQ to Objects

Entity Framework

Introduction to ORM (Object-Relational Mapping)
Code-First and Database-First Approaches
CRUD Operations with EF

ASP .NET MVC

MVC Architecture Overview
Controllers and Actions
Views and Razor Syntax
Model Binding and Validation

Web API Development

RESTful Services
Building and Consuming APIs
Authentication and Authorization

Unit Testing in C#

Introduction to Unit Testing
Using NUnit or MSTest Framework
Test-Driven Development (TDD)

Dependency Injection

IoC (Inversion of Control) Containers
Dependency Injection Patterns

Security Best Practices in .NET

Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF)
Authentication and Authorization Techniques

Performance Optimization

Code Profiling and Performance Tools
Optimizing Database Queries and Code

View the Curriculum

Duration: 36 Hours

10 Lessons

This course aims to deepen participants’ knowledge and skills in advanced .NET development using C#.

Participants will gain expertise in advanced concepts, frameworks, and best practices to build robust, scalable applications.

Master advanced .NET and C# programming techniques.

Gain proficiency in using advanced frameworks and tools.

Learn industry best practices for designing and developing scalable applications.

Enhance problem-solving skills through practical exercises and projects.

Prepare for advanced certifications in .NET development.

Advanced C# Programming

Delegates and Events
LINQ (Language Integrated Query)
Asynchronous Programming with async/await

.NET Framework Internals

Garbage Collection and Memory Management
Reflection and Attributes

Advanced Object-Oriented Programming

Inheritance and Polymorphism
Design Patterns (e.g., Factory, Singleton)

ASP .NET Core

MVC Architecture
Dependency Injection in ASP.NET Core

Entity Framework Core

ORM Concepts and Best Practices
Code-First Approach

Web APIs with ASP .NET Core

RESTful Services
Authentication and Authorization

Unit Testing and Test-Driven Development (TDD)

Writing Unit Tests with MSTest or NUnit
Implementing TDD in .NET Projects

Microservices Architecture

Introduction to Microservices
Building Microservices with .NET Core

Containerization with Docker

Docker Fundamentals
Dockerizing .NET Applications

Performance Tuning and Optimization

Profiling and Debugging .NET Applications
Performance Best Practices

View the Curriculum

Duration: 30 Hours

10 Lessons

The Basic Java Programming course aims to equip participants with fundamental knowledge and skills in Java programming.

By the end of the course, participants will be able to write basic Java programs, understand core programming concepts, and be prepared for further Java development or related studies.

Gain a solid foundation in Java programming.

Learn essential programming concepts applicable to other languages.

Hands-on experience with coding exercises and real-world projects.

Enhance problem-solving and logical thinking skills.

Prepare for further study or career opportunities in software development.

This course provides a comprehensive introduction to Java programming, focusing on foundational skills essential for further learning and application in software development.

Introduction to Java

Overview of Java programming language.
Setting up Java development environment.
Writing and executing a basic Java program.

Variables and Data Types

Declaring variables and assigning values.
Primitive data types: int, double, boolean, etc.
Using Strings and arrays.

Operators and Expressions

Arithmetic, relational, and logical operators.
Operator precedence and associativity.
Using expressions in Java programs.

Control Flow Statements

Conditional statements: if, else-if, switch.
Looping statements: for, while, do-while.
Using break and continue statements.

Methods and Functions

Declaring and invoking methods.
Passing parameters to methods.
Returning values from methods.

Arrays and Collections

Declaring and initializing arrays.
Accessing array elements.
Introduction to Java Collections framework.

Object-Oriented Programming Basics

Understanding classes and objects.
Creating classes and objects in Java.
Encapsulation and access modifiers.

Inheritance and Polymorphism

Inheriting classes in Java.
Overriding methods.
Polymorphism and dynamic method dispatch.

Exception Handling

Handling exceptions using try-catch blocks.
Throwing and catching exceptions.
Using finally block for cleanup.

File I/O and Basics of GUI

Reading from and writing to files in Java.
Working with file streams.
Introduction to graphical user interface (GUI) concepts.

View the Curriculum

Duration: 30 Hours

10 Lessons

The objective of this Intermediate Java Programming Course is to deepen participants’ understanding of Java programming concepts and prepare them for developing more complex applications.

By the end of the course, participants will gain proficiency in advanced topics and best practices in Java development.

Gain proficiency in advanced Java programming concepts.

Enhance problem-solving skills with complex programming challenges.

Learn best practices for Java application development.

Prepare for advanced Java certifications and career advancement opportunities.

This Intermediate Java Programming Course is designed to equip participants with advanced Java skills necessary for building robust and scalable applications.

Through comprehensive coverage of key topics and practical exercises, participants will gain the confidence to tackle complex programming challenges effectively.

Advanced Object-Oriented Programming

Inheritance and polymorphism
Abstract classes and interfaces

Exception Handling

Try-catch blocks
Custom exceptions

Collections Framework

Lists, Sets, and Maps
Iterators and comparators

File Handling and I/O Operations

Reading from and writing to files
Buffered streams and file handling techniques

Multithreading and Concurrency

Thread lifecycle and synchronization
Executors and thread pools

Database Connectivity with JDBC

Connecting to databases
Executing queries and handling results

Lambda Expressions and Functional Interfaces

Syntax and usage of lambda expressions
Functional interfaces in Java standard library

Generics

Generic classes and methods
Wildcards and bounded types

Reflection API

Obtaining class information at runtime
Dynamic invocation of methods

Java 8+ Features

Stream API and functional programming concepts
Optional class and method references

View the Curriculum

Duration: 30 Hours

10 Lessons

This advanced Java course aims to equip participants with comprehensive knowledge and skills in advanced Java programming concepts and techniques.

Participants will deepen their understanding of Java programming paradigms, enhance their ability to develop robust and efficient applications, and prepare themselves for complex software development challenges.

Gain expertise in advanced Java topics essential for building scalable and high- performance applications.

Enhance problem-solving skills and ability to design complex software solutions.

Increase employability with in-demand skills sought by top tech companies.

This course is designed for Java developers looking to advance their skills beyond basic programming and explore the nuances of Java’s advanced features.

By the end of this course, participants will have a solid understanding of advanced Java concepts and be ready to tackle complex software development challenges with confidence.

Introduction to Advanced Java

Overview of advanced Java features and enhancements.
Java memory model and garbage collection.

Concurrency and Multithreading

Understanding threads and synchronization.
Java concurrency utilities (java.util.concurrent package).

Java Generics

Generic classes, methods, and bounded type parameters.
Wildcards and generic collections.

Java Collections Framework

Lists, Sets, Maps, and their implementations.
Comparator and Comparable interfaces.

Java IO and NIO

File handling with java.io package.
Non-blocking IO with java.nio package.

Lambda Expressions and Functional Interfaces

Introduction to lambda expressions.
Functional interfaces and java.util.function package.

Exception Handling in Java

Custom exceptions and exception chaining.
Best practices for exception handling.

Java Annotations

Built-in annotations (e.g., @Override, @Deprecated).
Creating custom annotations.

Java Reflection

Accessing class information at runtime.
Using reflection for dynamic code execution.

Java Streams API

Stream operations: map, filter, reduce.
Parallel streams and performance considerations.

View the Curriculum

Duration: 32 Hours

10 Lessons

This course aims to equip participants with essential JavaScript skills, from basic syntax to advanced concepts.

Participants will learn to develop interactive web applications, manipulate the DOM, handle events, and work with modern JavaScript features.

Participants will apply learned JavaScript skills to develop a web application, incorporating DOM manipulation, event handling, and asynchronous requests.

Master fundamental and advanced JavaScript concepts.

Build interactive web applications and user interfaces.

Learn modern JavaScript features and best practices.

Prepare for front-end development roles and advanced JavaScript frameworks.

Develop skills applicable across web development projects.

Introduction to JavaScript

History and evolution of JavaScript
Setting up a development environment

Basic JavaScript Syntax

Variables, data types, and operators
Control flow: if-else statements, loops

Functions and Scope

Function declaration vs. expression
Scope and closures

Arrays and Objects

Arrays: methods and manipulation
Objects: properties, methods, and prototypes

DOM Manipulation and Events

Accessing DOM elements
Event handling and listeners

Asynchronous JavaScript

Callback functions
Promises and async/await

ES6+ Features

Arrow functions and template literals
Destructuring, spread operator, and rest parameters

Error Handling and Debugging

Common JavaScript errors
Using browser developer tools for debugging

Introduction to AJAX and Fetch API

Making asynchronous requests
Handling API responses

Introduction to JavaScript Frameworks

Overview of popular frameworks (e.g., React, Vue.js)
Integration of JavaScript frameworks in web development

View the Curriculum

TRAINING

SOFTWARE QA TESTING

Software QA testing plays a crucial role in ensuring the reliability, functionality, and usability of software products, ultimately contributing to the success of projects and the satisfaction of stakeholders.

QA Orientation + Performance & Security
Python for Automation Testing
Python Programming with Ansible Bootcamp
Python Bootcamp

Duration: 2 Days

11 Lessons

Module 1: Introduction to Quality Assurance and Testing

Understanding the role of QA in software
development

Module 2: Software Development Life Cycle (SDLC)

Understanding the different phases of SDLC
Role of QA in each phase
Importance of requirements and design phase in testing

Module 3: Introduction to Testing

What is testing and why is it necessary?
Testing objectives and goals
Testing process and its importance

Module 4: Types of Testing

Functional testing
Performance testing
Security testing
Usability testing
Compatibility testing
Regression testing
Exploratory testing
Acceptance testing
Introduction to automation testing

Module 5: Test Planning and Test Strategy

Creating a test plan
Defining test objectives and scope
Identifying test requirements and test
scenarios
Test strategy and approach

Module 6: Test Case Design and Execution

Writing effective test cases
Test case management tools
Test case execution and tracking defects

Module 7: Bug Tracking and Defect Management

Understanding the lifecycle of a defect
Bug tracking systems and tools
Importance of effective defect management

Module 8: Test Reporting and Metrics

Generating test reports
Test metrics and their significance
Importance of test documentation

Module 9: Introduction to Automation Testing

Benefits of automation testing
Popular automation testing tools
Introduction to scripting and frameworks

Module 10: Test Environment and Test Data Management

Setting up a test environment
Test data management best practices
Data-driven testing techniques

Module 11: Testing Best Practices and Guidelines

Writing efficient test cases
Test data creation and management
Continuous improvement in testing process

View the Curriculum

Duration: 5 Days

15 Lessons

Basic Language Syntax
Object Oriented Features in Python
Exception Handling
Regular Expression
Working with inbuilt database support (SQLite)
Itertools and Collections framework
Testing
Py.test

DAY 1

Module 1

Introduction to Python
Dynamic Typing
Object Types
Complex Object Type
Operators
Unbounded Integers
Useful functions
type()
id()
dir()
help()
chr()
unichr()

Module 2

Simple Program Using Basic Python
Anaconda Installation
Sublime Text Editor
Python Project using Eclipse

Module 3

Basic Language Construct

Data types and Variables
String type
Format method
Operators and Expressions
Indentation

Module 4

Data Structures Mutable and Immutable Data Structures

List, Subscripting, Nested List
Tuple, Use cases
String Manipulation
Dictionary with Case Study
Use Cases and Assignment

Module 5

Control Structure

Indentation
if elif else
while
for ( nested )
Use Cases and Assignments

DAY 2

Module 6

Functions

User Define Functions

global variable
default arguments
variable arguments *arg
Multiple Variable Default Argument
**kwarg
Use Case Design Multiplier

Sequence Operation using:

lambda
filter
map
reduce
sum/max/min
set
enumerate
sorted
reversed
range

Operation using:

List /Tuple Comprehension
Dictionary Comprehension
Dictionary Use Case

Module 7

Modules
User Define Modules

Import Categories

1) using import
2) using from

Built In Modules

1) math
2) os
3) sys
4) random
5) pickle / Unpickle (Object Serialization)
6) json etc

DAY 3

Module 8

Object Oriented Programming

Classes and Objects
The “self” keyword
Methods and Attributes
Constructor and Destructor
Instance and static member
Class Inheritance
Built In Attributes
__private
public
_protected
Multiple Inheritance
Locking Attributes
Super keyword

Module 9

Files Objects and Methods
open()
read(), readlines()
write(), writelines()
tell()
using with statements
Use Case using File Handling

Module 10

Exception Handling
Built in Exceptions
exceptions module
User Define Exceptions

DAY 4

Module 11

Regular expressions
Pattern Writing
Compiling
Match/Search
Group/Groups
findall
re.sub
re.split
Use Case using Regular Expression and Pattern

Module 12

File and Directory handling

1) Fileinput
2) glob
3) Regular Expression
4) Case Study for Extraction of Data from Multiple Files and Generating Reports

Module 13

Itertools and Collections framework

imap/ ifilter /izip

Iterator

file iteration using map

Overriding iterator functions

Generator

yield
Use Case of yield

DAY 5

Module 14

Testing
Testing Fundamental
Types of Testing
Unittest Framework
Run Test
Write Unittest.TestCase for Python
Code

Module 15

Python Debugging and Testing
Use of pdb
Calling pytest through python
py.test
py.test –pdb
PyTest Hands On
Conclusion and Summary

View the Curriculum

Duration: 5 Days

19 Lessons

This bootcamp is designed for participants looking to enhance their automation skills by combining Python programming with Ansible orchestration.

Participants will learn how to leverage Python scripting to extend Ansible’s capabilities and automate complex IT infrastructure tasks.

DAY 1: INTRODUCTION TO PYTHON FOR AUTOMATION

Module 1: Introduction to Python Programming

Overview of Python language features and syntax
Data types, variables, and operators
Control structures: if statements, loops

Module 2: Functions and Modules in Python

Defining and calling functions
Working with modules and packages
Introduction to Python standard library modules relevant to automation

Module 3: File Operations and Error Handling in Python

Reading from and writing to files
Exception handling with try-except blocks
Best practices for error handling in automation scripts

Hands-On Python Scripting Exercises

Writing Python scripts to perform common automation tasks
Practice exercises covering file manipulation, text processing, and basic system administration tasks

DAY 2: INTRODUCTION TO ANSIBLE

Module 1: Introduction to Ansible

Overview of Ansible and its architecture
Ansible components: control node, managed nodes, inventories
Installing Ansible and configuring the control node

Module 2: Ansible Playbooks and Tasks

Understanding Ansible playbooks
Writing YAML syntax for defining tasks
Executing playbooks to perform configuration management tasks

Module 3: Inventory Management and Variables

Managing inventories in Ansible
Working with dynamic inventories
Using variables for configuration management

Module 4: Working with Ansible Modules

Overview of Ansible modules
Commonly used modules for system administration tasks
Hands-on exercises using Ansible modules for package management, file operations, and user management

Module 5:

K-Means clustering
Hierarchical Clustering
Recommender System and Association
Project case studies: Classification of drugs, prediction of heart disease, Association of Ingredients in Drug, Liver Disease Prediction

DAY 3: INTEGRATING PYTHON WITH ANSIBLE

Module 1: Introduction to Ansible Roles

Organizing playbooks with roles
Structure and conventions of Ansible roles
Writing reusable and modular playbooks using roles

Module 2: Using Ansible with Python Scripts

Integrating Python scripts with Ansible playbooks
Calling Python functions from Ansible tasks
Passing data between Ansible and Python

Module 3: Dynamic Inventory Management with Python

Generating dynamic inventories using Python scripts
Integration with cloud providers and other infrastructure platforms
Automating inventory updates and maintenance

Module 4: Advanced Ansible-Python Integration

Custom Ansible modules in Python
Ansible callback plugins for custom reporting and logging
Hands-on exercises integrating custom Python code with Ansible automation tasks

DAY 4: ANSIBLE BEST PRACTICES AND OPTIMIZATION

Module 1: Ansible Best Practices

Best practices for writing efficient and maintainable playbooks
Organizing code with roles, tasks, and templates
Using Ansible Galaxy for sharing and reusing roles

Module 2: Ansible Optimization Techniques

Performance optimization tips for Ansible playbooks
Reducing playbook execution time with strategies like async and poll
Profiling and troubleshooting playbook performance issues

Module 3: Ansible Testing and Continuous Integration

Testing Ansible playbooks with Ansible-lint and other testing tools
Integration with continuous integration (CI) systems like Jenkins
Automated testing and deployment pipelines for Ansible projects

Case Studies and Real-World Examples

DAY 5 CAPSTONE PROJECT

Module 1: Capstone Project

Participants work on a comprehensive automation project combining Python programming with Ansible orchestration
Project scope includes infrastructure provisioning, configuration management, and deployment automation

Module 2: Project Presentations and Evaluation

Participants present their capstone projects to the class and instructors
Projects are evaluated based on completeness, effectiveness, and adherence to best practices
Feedback provided to participants for further improvement and learning

Course Conclusion and Certification

Recap of key concepts and takeaways from the bootcamp
Distribution of course completion certificates to participants

View the Curriculum

Duration: 5 Days

20 Lessons

By fulfilling these prerequisites and lab requirements, participants will be well-prepared to engage effectively with the Python Bootcamp curriculum and develop proficiency in Python programming.

DAY 1: INTRODUCTION TO PYTHON BASICS

Module 1: Introduction to Python

Overview of Python programming language
Setting up Python environment (interpreter, IDE)
Writing and executing Python scripts

Module 2: Python Basics I

Data types: integers, floats, strings, lists, tuples, dictionaries
Variables and assignments
Basic arithmetic and string operations

Module 3: Python Basics Part II

Control flow: if statements, loops (for and while), conditional expressions
Functions: defining functions, parameters, return values

Module 4: Python Basics Part III

Input/output operations: reading from/writing to files
Exception handling: try-except blocks
Hands-on exercises and practice problems

DAY 2: INTERMEDIATE PYTHON CONCEPTS

Module 1: Intermediate Data Structures

Lists: list comprehensions, slicing, common list methods
Tuples: immutability, packing/unpacking
Dictionaries: key-value pairs, dictionary comprehensions

Module 2: Advanced Control Flow

Nested loops and conditionals
Break, continue, and pass statements
Enumerate and zip functions

Module 3: Advanced Functions

Lambda functions and anonymous functions
Higher-order functions: map, filter, reduce
Function decorators

Module 4: Object-Oriented Programming (OOP) in Python

Classes and objects
Inheritance, encapsulation, and polymorphism
Hands-on OOP exercises and projects

DAY 3: PYTHON LIBRARIES AND APPLICATIONS

Module 1: Introduction to Python Standard Library

Overview of built-in modules and functions
Commonly used modules: os, sys, math, random

Module 2: Introduction to External Libraries

Installing and managing external libraries using pip
Exploring popular Python libraries: NumPy, pandas, matplotlib

Module 3: Introduction to Web Development with Flask

Setting up Flask environment
Creating routes and handling requests
Templating with Jinja2

Module 4: Building a Simple Web Application

Creating a CRUD (Create, Read, Update, Delete) application
Integrating Flask with databases (SQLite)

DAY 4: ADVANCED PYTHON TOPICS

Module 1: Concurrency and Parallelism

Multithreading vs. multiprocessing
Thread synchronization and locks
Asynchronous programming with async/await

Module 2: Error Handling and Debugging

Handling exceptions effectively
Logging and debugging techniques
Unit testing with unittest module

Module 3: Python Best Practices and Code Optimization

Writing clean and maintainable code
Performance optimization techniques
Code profiling and optimization tools

Module 4: Introduction to Data Analysis and Visualization

Data manipulation with pandas
Data visualization with matplotlib and seaborn
Hands-on data analysis projects

DAY 5: REAL-WORLD APPLICATIONS AND PROJECT WORK

Module 1: Introduction to Data Science and Machine Learning

Overview of data science lifecycle
Introduction to machine learning concepts
Exploring popular machine learning libraries: scikit-learn, TensorFlow, Keras

Module 2: Building a Machine Learning Model

Data preprocessing and feature engineering
Model training and evaluation
Model deployment and inference

Module 3: Introduction to Automation with Ansible

Overview of Ansible and its architecture
Writing Ansible playbooks for automation tasks
Hands-on Ansible exercises

Module 4: Capstone Project

Participants work on a final project that integrates various Python concepts learned throughout the bootcamp
Project presentations and peer feedback

View the Curriculum

tRAINING

DATABASE ADMINISTRATION

Effective database administration is essential for organizations to leverage their data assets effectively, drive business insights, and maintain a competitive edge in today's data-driven world.

Oracle - PL / SQL
Introduction to Database and SQL
NoSQL Database Implementation

Duration: 2 Days

3 Lessons

Module 1

Database Fundamentals,
Database objects in Oracle

Module 2

Module 3

Stored Procedures
Functions and Triggers

View the Curriculum

Duration: 48 Hours

12 Lessons

The study of database design and management of a database is an essential component of the business IT world today.

Through this course the participant will gain a background in database design.

The participant will work with entity-relationship diagrams (ERD) to learn and implement the basic database design. Using Oracle SQL, the participants will apply the design principles to actually create and develop a working database.

This course is designed to help participants integrate theoretical material with practical knowledge to implement a database.

Participants will also use SQL commands to query single and multiple tables.

Single and group functions will also be used to enhance queries. Subqueries will be used to enhance data retrieval.

Data manipulation of data will also be covered to change the data in the database.

We will discuss the connection of an application program to the database to store and retrieve data.

WEEK 1

Install SQL Developer 21
Configure the database connection
Activity
 Display connected user name
 Display system date

WEEK 2

Apply normalization principles to DB design
Design tables
Reading Chapter# 1
Activity
 Design tables based on design criteria

WEEK 3

Use the basic SELECT statement
Queries with a single table
Reading Chapter# 2
Activity
 Create queries based on a given request

WEEK 4

Create database tables with and without constraints
Reading Chapter# 3 and 4
Activity
 Create tables with or without constraints

WEEK 5

Perform DML operations on data with TCL commands
Reading Chapter# 5
Activity
 Perform DML operations against data in tables

WEEK 6

Build queries with criteria to restrict rows
Reading Chapter# 8
Activity
 To create queries with conditions or restrictions

WEEK 7

Build queries using more than one table
Reading Chapter# 9
Activity
 Access data from multiple tables
 Display results of given lab queries

WEEK 8

Build queries using single-row functions
Reading Chapter# 10
Activity
 To create queries using single-row functions
 Create tables with single-row functions

WEEK 9

Build queries using aggregate functions
Reading Chapter# 11
Activity
 To create queries using single-row functions and aggregate functions

WEEK 10

Build queries using subqueries
Reading Chapter# 12
Activity
 Create queries with the use of subqueries

WEEK 11

Build views
Reading Chapter# 13
Activity
 Create views based on queries

WEEK 12

Introduction to the Explain Plan
Activity
 To show the explain plan for several queries

View the Curriculum

Duration: 40 Hours

10 Lessons

“Not only SQL” databases are heavily used in Big Data applications – particularly those that are web-related.

The benefits in scalability and performance make a NoSQL database a compelling choice.

In this course, participants will design and implement NoSQL databases using systems like MongoDB, Cassandra etc. Participants will also demonstrate the implementation of a NoSQL database in a large-scale storage and data processing model.

WEEK 1

Introduction to NoSQL Database Implementation

Relational and NoSQL Databases
Define Relational Databases
Define NoSQL Databases
Compare Relational with NoSQL Databases

WEEK 2

ACID (Atomicity, Consistency, Isolation, Durability)

Define each term in ACID
Describe examples where each property of ACID fails.

WEEK 3

Joins and NoSQL

Define a Join as it relates to tables in a database.
Describe ways in which a Join can be accomplished in a NoSQL database.

WEEK 4

Types of NoSQL Databases

Define a Column Store
Define a Document Store
Define a Key-Value Store
Define a Graph-type database

WEEK 5

Document Store (MongoDB)

Describe some of the features of MongoDB.
Discuss where the Document Store approach is most appropriate.

WEEK 6

Document Store (MongoDB)

Describe some of the features of MongoDB.
Discuss where the Document Store approach is most appropriate.

WEEK 7

NoSQL – MongoDB

Create/Delete Documents
Data Types

WEEK 8

MongoDB

Query

WEEK 9

MongoDB

Update Documents
Aggregation

WEEK 10

Column Store (Cassandra)

Describe some of the features of Cassandra
Discuss where the Column Store approach is most appropriate
Demonstrate the use of Cassandra

View the Curriculum

TRAINING

MOBILE DEVELOPMENT

Mobile application development is the process of creating software applications that run on mobile devices such as smartphones and tablets. This involves designing, coding, testing, and deploying applications to platforms like Android and iOS. Developers use various programming languages and frameworks, such as Java, Kotlin, Swift, and React Native, to build user-friendly, efficient, and secure apps.

Mobile Application Development

Android Development
Basic Android Development
Intermediate Android Development
Advanced Android Development

Innovate Seamlessly

Elevate your business through our cutting-edge IT solutions, where innovation meets seamless integration for optimal performance and efficiency.

Cybersecurity Excellence

Trust our robust cybersecurity solutions to safeguard your digital assets, ensuring resilience against evolving threats and maintaining the highest standards of protection.

Unlocking Efficiency

Experience a new level of operational efficiency with our cloud services, providing scalable, accessible, and innovative solutions tailored to transform your business dynamics.

File Servers Redefined

Elevate your file management experience with our file servers, where secure data storage meets seamless collaboration, ensuring the integrity and accessibility of your critical information.

Cybersecurity Excellence

Trust our robust cybersecurity solutions to safeguard your digital assets, ensuring resilience against evolving threats and maintaining the highest standards of protection.

Get Started

Lorem ipsum dolor sit amet, consectetur adipisicing elit. Optio, neque qui velit. Magni dolorum quidem ipsam eligendi, totam, facilis laudantium cum accusamus ullam voluptatibus commodi numquam, error, est. Ea, consequatur.

iOS Development

Basic IOS Development
Intermediate IOS Development
Advanced IOS Development

Innovate Seamlessly

Elevate your business through our cutting-edge IT solutions, where innovation meets seamless integration for optimal performance and efficiency.

Cybersecurity Excellence

Trust our robust cybersecurity solutions to safeguard your digital assets, ensuring resilience against evolving threats and maintaining the highest standards of protection.

Unlocking Efficiency

Experience a new level of operational efficiency with our cloud services, providing scalable, accessible, and innovative solutions tailored to transform your business dynamics.

File Servers Redefined

Elevate your file management experience with our file servers, where secure data storage meets seamless collaboration, ensuring the integrity and accessibility of your critical information.

Cybersecurity Excellence

Trust our robust cybersecurity solutions to safeguard your digital assets, ensuring resilience against evolving threats and maintaining the highest standards of protection.

Get Started

tRAINING

WEB DEVELOPMENT

Web development is a dynamic and evolving field that requires a blend of creativity, technical skills, and problem-solving abilities. It plays a crucial role in the digital landscape, providing the foundation for the modern web and enabling businesses, individuals, and organizations to establish their online presence.

jQuery Essentials - Beginner to Advanced
Web Development with HTML and CSS
AngularJS - Beginner to Advanced
Vue.js - Beginner to Advanced
React JS - Beginner to Advanced

Duration: 32 Hours

10 Lessons

This comprehensive course aims to equip participants with essential skills in jQuery, enabling them to build interactive and dynamic web applications efficiently.

Participants will progress from basic concepts to advanced techniques, mastering jQuery for modern web development.

Master jQuery fundamentals and advanced techniques.

Build responsive and interactive web applications.

Enhance productivity through jQuery’s powerful DOM manipulation and event handling capabilities.

Learn to integrate jQuery with other web technologies and frameworks.

Prepare for roles requiring front-end development skills with jQuery.

Introduction to JQuery

What is jQuery?
Benefits of using jQuery in web development

Setting up JQuery

Including jQuery in web pages
Understanding the jQuery document ready function

JQuery Selectors and Filters

Basic selectors (element, class, ID)
Attribute selectors and pseudo-selectors
Filtering and chaining selectors

DOM Manipulation with JQuery

Adding, modifying, and removing elements
Traversing the DOM tree
Creating and appending elements dynamically

JQuery Events

Binding and unbinding events
Event delegation and propagation
Handling user interactions (click, hover, submit)

JQuery Effects and Animations

Showing and hiding elements
Fading, sliding, and custom animations
Delaying and queuing animations

AJAX with JQuery

Making AJAX requests with jQuery
Handling AJAX responses (JSON, XML, HTML)
Implementing callbacks and promises

JQuery Plugins

Introduction to jQuery plugins
Integrating and customizing plugins

Advanced JQuery Techniques

Using jQuery UI for widgets and interactions
Optimizing performance with jQuery
Best practices and coding standards

Responsive Design with JQuery

Adapting layouts and content with jQuery
Implementing responsive navigation and elements

Final Project

Participants will work on a final project applying jQuery techniques
learned throughout the course to create a dynamic and interactive web application.
They will showcase their project and demonstrate their proficiency in using jQuery effectively.

View the Curriculum

Duration: 32 Hours

10 Lessons

This course aims to equip participants with the fundamental and advanced skills in web development using HTML and CSS.

Participants will learn to create responsive and visually appealing websites, applying modern web design principles and best practices.

Gain proficiency in HTML and CSS for building modern websites.

Learn responsive web design techniques to create mobile-friendly websites.

Understand web accessibility standards and best practices.

Develop practical skills applicable to personal projects or professional websites.

Prepare for further specialization in front-end development or web design.

Introduction to Web Development

Overview of web technologies (HTML, CSS, JavaScript)
Introduction to web browsers and developer tools

HTML Fundamentals

Structure of an HTML document
HTML elements, attributes, and semantic markup

CSS Fundamentals

CSS syntax and selectors
Styling text, colors, backgrounds, and borders

Responsive Web Design

Media queries and responsive layout techniques
Flexbox and CSS Grid for responsive design

CSS Layout Techniques

CSS box model and positioning
Floats vs. Flexbox vs. Grid

Advanced CSS Styling

CSS preprocessors (e.g., Sass)
CSS animations and transitions

CSS Frameworks

Introduction to popular CSS frameworks (e.g., Bootstrap)
Using Bootstrap for rapid website development

Web Accessibility

Understanding accessibility standards (WCAG)
Designing accessible web interfaces

Optimizing Web Performance

Minifying CSS and JavaScript
Image optimization and lazy loading techniques

Project Work and Portfolio Development

Building a responsive website project
Creating a portfolio to showcase skills and projects

Final Project

Participants will work on a final project where they will apply all
concepts learned throughout the course to create a fully functional and responsive website.
They will present their project, demonstrating proficiency in HTML and CSS.

View the Curriculum

Duration: 36 Hours

10 Lessons

This course aims to equip participants with a comprehensive understanding of AngularJS, enabling them to build dynamic and scalable web applications.

Participants will progress from fundamental concepts to advanced topics, mastering AngularJS for front-end development.

Master AngularJS fundamentals and advanced techniques.

Develop reusable components and optimize performance.

Gain proficiency in data binding, dependency injection, and routing.

Learn to integrate AngularJS with external APIs and services.

Prepare for roles requiring front-end development skills with AngularJS.

Introduction to AngularJS

What is AngularJS?
Benefits and features of AngularJS

Setting Up An AngularJS Environment

Installing AngularJS and setting up a development environment
Understanding Angular CLI (Command Line Interface)

Components and Templates

Components vs. directives
Creating and using templates
Data binding and interpolation

Services and Dependency Injection

Introduction to services and dependency injection
Using built-in services (e.g., HTTP, Router)
Creating custom services

Directives and Pipes

Built-in directives (ngIf, ngFor, ngSwitch)
Creating custom directives
Using pipes for data transformation

Routing and Navigation

Setting up routes and navigation in AngularJS
Route guards and lazy loading modules
Implementing nested routes

Forms and Form Validation

Template-driven forms vs. reactive forms
Implementing form validation
Handling form submissions

HTTP and Observables

Making HTTP requests in AngularJS
Using Observables for handling asynchronous data
Error handling and interceptors

State Management with NgRX

Introduction to NgRx for state management
Actions, reducers, and effects
Integrating NgRx with Angular applications

Testing AngularJS Applications

Unit testing and end-to-end testing with Angular CLI
Writing and running tests with Jasmine and Protractor
Testing Angular components, services, and routes

Final Project

Participants will work on a final project to apply AngularJS concepts
learned throughout the course.
They will develop a complete AngularJS application showcasing
their proficiency in building dynamic user interfaces and managing application state effectively.

View the Curriculum

Duration: 34 Hours

10 Lessons

This course aims to provide participants with a comprehensive understanding of Vue.js, enabling them to build modern and interactive web applications.

Participants will progress from fundamental concepts to advanced topics, mastering Vue.js for front-end development.

Master Vue.js fundamentals and advanced techniques.

Develop reusable components and optimize performance.

Gain proficiency in Vue.js directives, state management, and routing.

Learn to integrate Vue.js with external APIs and services.

Prepare for roles requiring front-end development skills with Vue.js.

Introduction to Vue.Js

What is Vue.js?
Benefits and features of Vue.js

Setting up a Vue.Js Environment

Installing Vue.js and setting up a development environment
Vue CLI (Command Line Interface) and project structure

Vue Instance and Components

Vue instance lifecycle hooks
Creating and using components
Props, events, and slots

Directives and Filters

Built-in directives (v-bind, v-if, v-for, v-on)
Creating custom directives
Using filters for data transformation

Vue Router

Setting up routes and navigation in Vue.js
Route navigation guards
Nested routes and named views

State Management with Vuex

Introduction to Vuex for state management
State, mutations, actions, and getters
Modularizing Vuex store

Forms and Form Validation

Two-way data binding with forms in Vue.js
Handling form submissions and validation
Form libraries and best practices

HTTP Requests and Axios

Making HTTP requests in Vue.js with Axios
Interceptors and error handling
Using async/await with Axios

Vue.Js and RESTful APIs

Integrating Vue.js with RESTful APIs
Consuming data from external APIs
Handling CRUD operations with Vue.js

Advanced Vue.Js Techniques

Animations and transitions in Vue.js
Server-side rendering (SSR) with Vue.js
Vue.js performance optimization tips

Final Project

Participants will work on a final project to apply Vue.js concepts
learned throughout the course.
They will develop a complete Vue.js application showcasing their
proficiency in building dynamic user interfaces and managing application state effectively.

View the Curriculum

Duration: 34 Hours

10 Lessons

This course aims to provide participants with a comprehensive understanding of React JS, enabling them to build modern, scalable, and interactive web applications.

Participants will progress from fundamental concepts to advanced topics, mastering React JS for front-end development.

Master React JS fundamentals and advanced techniques.

Develop reusable components and optimize performance.

Gain proficiency in state management with React Hooks and Context API.

Learn to integrate React with external APIs and libraries.

Prepare for roles requiring front-end development skills with React JS.

This course structure provides a comprehensive pathway from beginner to advanced proficiency in React JS, covering essential concepts, practical skills, and real-world applications for building modern web applications.

Introduction to ReactJS

What is React JS?
Benefits and features of React

Setting Up a React Environment

Installing React and setting up a development environment
Understanding Create React App

Components and Props

Functional components vs. class components
Props and prop types
State and lifecycle methods

Handling Events in React

Event handling in React
Binding methods and handling form submissions

State Management with Hooks

Introducing React Hooks (useState, useEffect)
Managing complex state with useReducer
Custom hooks and best practices

React Router

Navigation and routing in single-page applications
Route parameters and nested routes
Implementing authentication with React Router

Working with Forms in React

Controlled vs. uncontrolled components
Form validation and error handling
Handling form submissions

Redux for State Management

Introduction to Redux and the Flux architecture
Actions, reducers, and the store
Connecting Redux with React applications

Advanced React Patterns

Higher-order components (HOCs)
Render props pattern
Context API and useContext hook

Testing React Applications

Testing principles and tools (Jest, React Testing Library)
Writing unit tests and integration tests
Testing Redux-connected components

Final Project

Participants will work on a final project to apply React JS concepts
learned throughout the course.
They will develop a complete React application showcasing their
proficiency in building dynamic user interfaces and managing application state effectively.

View the Curriculum