1. An Introduction to Python
   • Why Python, its Unique Feature and where to use it?
   • Python Environment Setup
   • Discuss about IDE’s like IDLE, Pycharm and Enthought Canopy
   • Start programming on an interactive shell.
   • Python Identifiers, Keywords
   • Discussion about installed module s and packages
   • Access Command line arguments within programs
2. Conditional Statement, Loops, and File Handling
   • Python Data Types and Variable
   • Condition and Loops in Python
   • Decorators
   • Python Modules & Packages
   • Python Files and Directories manipulations
   • Use various files and directory functions for OS operations
3. Python Core Objects and Functions
   • Built in modules (Library Functions)
   • Numeric and Math’s Module
   • String/List/Dictionaries/Tuple
   • Complex Data structures in Python
   • Arbitrary data types and their Data Structure
   • Python built-in function
   • Python user-defined functions
   • Python packages and functions
   • The anonymous Functions – Lambda Functions
4. Object Oriented Python
   • OOPs Concepts
   • Object, Classes and Destroying Objects
   • Accessing attributes, Built-In Class Attributes
   • Inheritance and Polymorphism
   • Overriding Methods,
   • Abstraction and Encapsulation
5. Regular Expression
   • Regular Expressions
   • What are regular expressions?
   • The match and search Function
   • Compile and matching
   • Matching vs searching
   • Search and Replace feature using RE
   • Extended Regular Expressions
   • Wildcard characters and work with them
6. Multithreading
   • Multithreading with Python
   • What is multithreading?
   • Starting a New Thread
   • The Threading Module
   • Synchronizing Threads
7. File Handling
   • Writing data to a file
   • Reading data from a file
   • Read and Write data from CSV file
   • OS module
   • Rename and Removing files, directories
8. Exception Handling in Python
   • Exceptions Handling
   • Handling various exceptions using try….except…else
   • Try-finally clause
   • The argument of an Exception and create a self exception class
   • Python Standard Exceptions
   • Raising an exceptions, User-Defined Exceptions
9. Debugging Python Programs
   • Debug Python programs using pdb debugger
   • Assert for debugging
   • Standard project setup in Python
10. Modules & Packages
    • Modules
    • How to import a module?
    • Packages
    • How to create packages
11. Database Handling
    • Create Database Connection
    • Creating and accessing SQLite database
    • Python with MySQL Database
    • Creating Database table
    • CRUD operation on a database
    • Performing Transactions
    • Handling Database Errors
    • Disconnecting Database

1. Basics of Web Page Creation
   • Understanding of basic HTML /CSS
   • HTML Header, paragraph
   • Various tag for button, label and combo-box
   • Creation of forms in HTML
2. Django Framework
   • Introduction to Django
   • MVT Architecture
   • How to create Django App
   • Url Mapping
   • Templates
   • Introduction to static file
   • Django Model Overview
   • Creating model
   • Model template view creation
   • Django forms and validation
   • Relative Url with Template
3. Interacting with a Database: Models
   • Overview of Models
   • Creating Models
   • Configuring the Database
   • Your First App
   • Using Django with MySQL.
   • Models-Templates-Views Paradigm
   • Inserting and Updating Data
4. The Django Administration Site
   • Activating the Admin Interface
   • Using the Admin Interface
   • Users, Groups, and Permissions
   • Selecting an Objects
   • Deleting an Objects
5. Views and Templates
   • URL Template Inheritance
   • Template Inheritance Coding Example
   • Quick Note on Custom Template Filters
   • Template Filters and Custom Filters
   • Template Filters Coding Examples
   • Django Passwords
   • Deploying Django Framework
6. Form Processing
   • Django Forms
   • Form Validation
   • Model Forms
   • Relative URLs with Templates 
   • Relative URLs Coding Examples 
7. Project Work
   • Discussion on Overview and requirements of Project
   • Creation a Web-based Application
8.  

1. Fundamentals of Data Science and Machine Learning
   • Introduction to Data Science
   • The need for Data Science
   • BigData and Data Science’
   • Data Science and machine learning 
   • Data Science Life Cycle
   • Data Science Platform
   • Data Science Use Cases 
   • Skill Required for Data Science
2. Mathematics For Data Science
   • Linear Algebra
     • Vectors
     • Matrices
   • Optimization
     • Theory Of optimization
     • Gradients Descent

3. Introduction to Statistics
   • Descriptive vs. Inferential Statistics
   • Types of data
   • Measures of central tendency and dispersion
   • Hypothesis & inferences
   • Hypothesis Testing
   • Confidence Interval
   • Central Limit Theorem
4. Probability and Probability Distributions
   • Probability Theory
   • Conditional Probability
   • Data Distribution
   • Binomial Distribution
   • Normal Distribution

Machine Learning
Python for ML along with Module1

1. Introduction to NumPy
   • Array Operations
   • Arrays Functions
   • Array Mathematics
   • Array Manipulation
   • Array I/O
   • Importing Files with Numpy
2. Data Manipulation with Pandas
   • Data Frames
   • I/O
   • Selection in DFs
   • Retrieving in DFs
   • Applying Functions
   • Reshaping the DFs – Pivot
   • Combining DFs
     • Merge
     • Join
   • Data Alignment
3. SciPy
   • Matrices Operations
   • Create matrices
     • Inverse, Transpose, Trace, Norms , Rank etc
   • Matrices Decomposition
     • Eigen Values & vectors
     • SVDs

4. MatPlotLib
   • Basics of Plotting
   • Plots Generation
   • Customization
   • Store Plots
5. SciKit LearnBasics
   • Data Loading
   • Train/Test Data generation
   • Preprocessing
   • Generate Model
   • Evaluate Models

Machine Learning

1. Exploratory Data Analysis
   • Data Exploration
   • Missing Value handling
   • Outliers Handling
   • Feature Engineering
2. Feature Selection
   • Importance of Feature Selection in Machine Learning
   • Filter Methods
   • Wrapper Methods
   • Embedded Methods
3. Machine Learning: Supervised Algorithms Classification
   • Introduction to Machine Learning
   • Logistic Regression
   • Naïve Bays Algorithm
   • K-Nearest Neighbor Algorithm
   • Decision Tress (SingleTree)
   • Support Vector Machines
   • Model Ensemble
     • Bagging
     • Random Forest
     • Boosting
     • Gradient Boosted Trees
   • Model Evaluation and performance
     • K-Fold Cross Validation
     • ROC, AUC etc…

4. Machine Learning: Regression
   • Simple Linear Regression
   • Multiple Linear Regression
   • Decision Tree and Random Forest Regression
5. Machine Learning: Unsupervised Learning Algorithms
   • Similarity Measures
   • Cluster Analysis and Similarity Measures
   • K-chical Clustering
   • Principal means Clustering
   • HierarComponents Analysis
   • Association Rules Mining & Market Basket Analysis
6. Text Mining
   • Basics
   • Term Document Matrix
   • TF-IDF
   • Twitter Sentiment Analysis

7. Project Work

• Machine Learning end to end Project blueprint
• Regression predictive modeling – House Price Prediction
• Classification predictive modeling – Binary Classification
• Widespread coverage for each Topic
• Various Approaches to Solve Data Science Problem
• Pros and Cons of Various Algorithms and approaches