Python with Data Analysis

Session 1: Introduction

● Why do we need Python?

● Program structure

Execution steps

● Interactive Shell

● Executable or script files

● User Interface or IDE

Session 2: Data Types and Operations

● Numbers

● Strings

● List

● Tuple

● Dictionary

● Other Core Types

Session 3: Statements and Syntax

● Assignments, Expressions and prints

● If tests and Syntax Rules

● While and For Loops

● Iterations and Comprehensions

Session 4: File Operations

● Opening a file

● Using Files

● Other File tools

Session 5: Functions

● Function definition and call

● Function Scope

● Arguments

● Function Objects

● Anonymous Functions

Session 6: Modules and Packages

● Module Creations and Usage

● Module Search Path

● Module Vs. Script

● Package Creation and Importing

Session 7: Classes

● Classes and instances

● Classes method calls

● Inheritance and Compositions

● Static and Class Methods

● Bound and Unbound Methods

● Operator Overloading

● Polymorphism

Session 8: Exception Handling

● Default Exception Handler

● Catching Exceptions

● Raise an exception

● User defined exception

Session 9: Advanced Concepts

● Defining Panda

● Pandas – Creating and Manipulating Data

● How to Create Data Frames?

● Importance of Grouping and Sorting

● Plotting Data

Data analysis

How to see Data

● Basic Stats

● Pandas -

○ Reading CSV, RDBMS, Excel

○ Pivot, Melt

○ Order by, Group By

● Numpy

● Matplotlib / Seaborn Visualizations

Scikit Learn

● Exploratory Data Analysis

● Data Munging

● Predictive Modelling

● Logistic Regression

● Decision Tree

● Random Forest

Prerequisites :

1. Basic programming language

2. Requires a Laptop with minimum of 4GB Ram

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