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