13
Lessons
40h
Duration
English
Language
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Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
Learning Path
- Introduction of Data science and its application in day-today life.
- Course overview and description.
- 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.
- String object basics
- String methods
- Splitting and joining strings
- String format functions
- List object basics list methods
- List as stack and queues
- List comprehensions
- Tuples, sets, dictionary object. Basics, dictionary, object
methods, dictionary view objects - Functions basics, parameter passing, Iterators. Generator functions.
- Lambda functions
- Map, reduce, filter functions
- OOPS basic concepts
- Creating classes and objects inheritance
- Multiple inheritance
- Working with files
- Reading and writing files
- Buffered read and write
- Other file methods
- Using standard module
- Creating new modules
- Exceptions handling with try-except
- Creating, inserting and retrieving table
- Updating and deleting the data
- Series
- DataFrame
- Panel
- Basic functionality
- 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
- 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
- Feature engineering and selection
- Building tuning and deploying models
- Analyzing bike sharing trends
- Analyzing movie reviews sentiment
- Customer segmentation and effective cross selling
- Analyzing wine types and quality
- Analyzing music trends and recommendations
- Forecasting stock and commodity prices
- Descriptive statistics
- Sample vs population statistics. Random variables.
- Probability distribution function. Expected value.
- Binomial distribution
- Normal distribution z-score
- Central limit theorem
- Hypothesis testing
- Z-Stats vs T-stats
- Type 1 type 2 error
- Confidence interval
- Chi Square test
- ANOVA test
- F-stats
- Introduction
- Supervised, unsupervised, semi-supervised, reinforcement, train, test, validation split
- Performance overfitting, underfitting OLS
- Linear regression assumption
- R square adjusted
- R square
- Introduction to scikit learn
- Training methodology
- Hands on linear regression
- Ridge regression
- Logistics regression
- Precision recall ROC curve
- F-Score
- Decision tree
- Cross validation. Bias vs Variance.
- Ensemble approach. Bagging. Boosting random forest variable importance.