Module 01 - Python Environment Setup and Essentials

• Introduction to Python Language
• Features, the advantages of Python over other programming languages
• Python installation – Windows, Mac & Linux distribution for Anaconda Python
• Deploying Python IDE
• Basic Python commands, data types, variables, keywords and more

Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac

Module 02 - Python language Basic Constructs

• Built-in data types in Python
• Learn classes, modules, Str (String), Ellipsis Object, Null Object, Ellipsis, Debug
• Basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise
• Loop and control statements while, for, if, break, else, continue.

Hands-on Exercise –

• Write your first Python program
• Write a Python Function (with and without parameters)
• Use Lambda expression
• Write a class
• Create a member function and a variable
• create an object
• Write a for loop

Module 03 - OOP concepts in Python

• How to write OOP concepts program in Python
• Connecting to a database
• Classes and objects in Python
• OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation
• Python functions, return types and parameters
• Lambda expressions

Hands-on Exercise –

• Creating an application which helps to check balance, deposit money and withdraw the money using the concepts of OOPS.

Module 04 - Database connection

• Understanding the Database, need of database
• Installing MySQL on windows
• Understanding Database connection using Python.

Hands-on Exercise – Demo on Database Connection using python and pulling the data.

Module 05 - NumPy for mathematical computing

• Introduction to arrays and matrices
• Broadcasting of array math, indexing of array
• Standard deviation, conditional probability, correlation and covariance.

Hands-on Exercise –

• How to import NumPy module
• Creating array using ND-array
• Calculating standard deviation on array of numbers
• Calculating correlation between two variables.

Module 06 - SciPy for scientific computing

• Introduction to SciPy
• Functions building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy with Bayes Theorem.

Hands-on Exercise –

• Importing of SciPy
• Applying the Bayes theorem on the given dataset.

Module 07 - Pandas for data analysis and machine learning

• Introduction to Python data frames
• Importing data from JSON, CSV, Excel, SQL database, NumPy array to data frame
• Various data operations like selecting, filtering, sorting, viewing, joining, combining

Hands-on Exercise –

• Working on importing data from JSON files
• Selecting record by a group
• Applying filter on top, viewing records

Module 08 - Exception Handling

• Introduction to Exception Handling
• Scenarios in Exception Handling with its execution
• Arithmetic exception
• RAISE of Exception
• What is Random List, running a Random list on Jupyter Notebook
• Value Error in Exception Handling.

Hands-on Exercise –

• Demo on Exception Handling with an Industry-based Use Case.

Module 09 - Web scraping with Python

• Introduction to web scraping in Python
• Installing of beautiful soup
• Installing Python parser lxml
• Various web scraping libraries, beautiful soup, Scrapy Python packages
• Creating soup object with input HTML
• Searching of tree, full or partial parsing, output print

Hands-on Exercise –

• Installation of Beautiful soup and lxml Python parser
• Making a soup object with input HTML file
• Navigating using Python objects in soup tree.