Python for Data Science
Duration: 7 Weeks
44 hrs of classroom learning & 28 hrs of Self Paced Online.
After Completion of this Course, You’ll be Skilled to Apply For:
Job Description – Requirement for Market Mix Modelling/ Web Analytics/ Prediction Models using Structured Data
Primary Domains – E-Commerce/ Banking, financial services and insurance (BFSI)/ Telecom/ Pharma/ ITeS
Course Content
Module 1: Introduction To Data Science
- What is Data Science?
- Why do we need it?
- Evolution of Data Science?
- Supervised Vs Unsupervised
Module 2: Python Installation & Basics: Syntax & Data Types
- Getting Started, Introduction to Anaconda, Jupyter-Notebook
- Keywords & Identifiers
- Data Types in Python
- Python Operations
- Type Conversion
Module 3: Statistics: Descriptive Statistics & Probability Basics
- Mean, Median, Mode
- Standard Deviation and Variance
- Conditional Probability and Marginal Probability
- Baye’s Theorem & Law of Total Probability
Module 4: Python Basics: Loops, If-else statements, Functions, Exception Handling
- If..else.. Statements
- Loop in Python
- Function in Python
- Exception Handling
- break and continue
- Pass statement
Module 5: Statistics: Data Distributions & Hypothesis Testing
- Data Distribution Types
- Law Of large Numbers
- Null and Alternative Hypothesis
- Type 1 and Type 2 Error
- Confidence Interval, Effect Size and Statistical Power
Module 6: Python Advanced: Numpy, Pandas
- Pandas structure definition
- Basic operation using pandas
- Numpy introduction
- Basic operations using Numpy
Module 9: Types Of ML Problems and Basic methodology used in ML
- Regression and Classification
- Steps Involved in ML problems
- Linear Regression
- Multiple Regression
- Multicolinearity and Overfitting
Module 10: Regression Techniques and implementation using Python
- Making LR and MLR model
- Managing Multicolinearity and Overfitting Trade off
- Fitting MLR model
Module 11: Classification Techniques
- Classification definition
- Logistic Regression
- Odds Ratio
- Log Odds Ratio
- Cutoff Value
Module 12: Python implementation of classification techniques
- Fitting Model
- Making Probabilistic Prediction
- Making Binary Prediction
- Decision Function Interpretation
Module 13: Case Study 1
Module 14: Case Study 2