Clinical Data Science - Corporate Training

16

Lessons

50h

Duration

English

Language

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OBJECTIVEs:

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.

Course features:

PRE-REQUISITES:

Learning Path

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

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

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

We can customize your training as well.

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