• Getting started with Python.
• Who uses Python?
• Why use Python?
• Python 2.x vs python 3.x
• Python Installation
• Adding python as environmental variable
• Adding pip as an environmental variable
• Basic Python 3 Syntax writing
• Primitive Data Structures
• Multiple operations on strings

• Getting started with Python
• What is Python?
• Installing Anaconda
• Variables, and Data Structure
• List, tuples and dictionary
• Control Structure
• Functions in python
• Lambda functions
• Object Oriented Programming
• Modules
• Using Packages
• Os package
• time and datetime
• File Handling in Python
• Miscellaneous Functions in python 

• Introduction to Statistics
• Population and Sample  
• Descriptive Statistics v/s Inferential Statistics
• Types of variable
• Categorical and Continuous Data
• Ratio and Interval
• Nominal and Ordinal Data

Descriptive Statistics

• Measure of Central Tendency – Mean, Mode and Median
• Percentile and Quartile
• Measure of Spread – IQR, Variance and Standard Deviation
• Coefficient of Variation
• Measure of Shape – Kurtosis and Skewness
• Correlation Analysis

Inferential Statistics

• Empirical Rule & Chebyshev’s Theorem
• Z Test
•  One Sample T test, independent t test
• ANOVA – f test
• Chi Square test

Working with Numpy

• NumPy Overview
• Properties, Purpose, and Types of ndarray
• Class and Attributes of ndarray Object
• Basic Operations: Concept and Examples
• Accessing Array
• Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Shape Manipulation & Broadcasting
• Linear Algebra using numpy
• Stacking and resizing the array
• random numbers using numpy

Working with Pandas

• Data Structures
• Series, DataFrame & Panel
• DataFrame basic properties
• Importing excel sheets, csv files, executing sql queries
• Importing and exporting json files
• Data Selection and Filtering
• Selection of columns and rows
• Filtering Dataframes
• Filtering – AND operaton and OR operation

Working with Pandas

• Data Cleaning
• Handling Duplicates
• Handling unusual values
• handling missing values
• Finding unique values
• Descriptive Analysis with pandas
• Creating new features
• Creating new categorical features from continuous variable
• combining multiple dataframes
• groupby operations
• groupby statistical Analysis
• Apply method
• String Manipulation 

Basic Visualization with matplotlib

• Matplotlib Features
• Line Properties
• Plot with (x, y)
• Controlling Line Patterns and Colors
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple PlotsSubplots
• Advance visualization using seaborn
• Types of Plots and Seaborn
• Boxplots
• Distribution Plots
• Countplots
• Heatmaps
• Voilin plots
• Swarmplots and pointplots

Project 1

• Objective
• Writing Codes

Project 2

• Objective
• Writing codes