Python

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

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

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

Hands-on Exercise –
1. Write your first Python program
2. Write a Python Function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. Create an object
7. Write a for loop

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

Hands-on Exercise –
1. Creating an application that helps to check balances, deposit money, and withdraw money using the concepts of OOPs

4.1 Understanding the database, need for database
4.2 Installing MySQL on Windows
4.3 Understanding database connections using Python

Hands-on Exercise – Demo on database connection using Python and pulling the data.

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

Hands-on Exercise –
1. How to import the NumPy module?
2. Creating an array using ND-array
3. Calculating standard deviation on an array of numbers
4. Calculating the correlation between two variables

6.1 Introduction to SciPy
6.2 Functions building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, and SciPy with Bayes’ Theorem

Hands-on Exercise –
1. Importing of SciPy
2. Applying Bayes’ theorem to the given dataset

7.1 How to plot graphs and charts with Python?
7.2 Various aspects of line, scatter, bar, histogram, 3D, the API of Matplotlib, and subplots

Hands-on Exercise –
1. Deploying Matplotlib for creating Pie, Scatter, Line, and Histogram

8.1 Introduction to Python dataframes
8.2 Importing data from JSON, CSV, Excel, SQL database, NumPy array to dataframe
8.3 Various data operations like selecting, filtering, sorting, viewing, joining, combining

Hands-on Exercise –
1. Working on importing data from JSON files
2. Selecting record by a group
3. Applying filter on top, viewing records

9.1 Introduction to Exception Handling
9.2 Scenarios in Exception Handling with its execution
9.3 Arithmetic exception
9.4 RAISE of Exception
9.5 What is Random List, running a Random list on Jupyter Notebook
9.6 Value Error in Exception Handling.

Hands-on Exercise –
1. Demo on Exception Handling with an Industry-Based Use Case

10.1 Introduction to Thread, the need of threads
10.2 What are thread functions?
10.3 Performing various operations on thread like joining a thread, starting a thread, and enumeration in a thread
10.4 Creating a Multithread, finishing the multithreads.
10.5 Understanding race condition, lock, and synchronization

Hands-on Exercise –
1. Demo on Starting a Thread and a Multithread and then performing multiple operations on them

11.1 Introduction to modules in Python, the need for modules
11.2 How to import modules in Python
11.3 Locating a module, namespace, and scoping
11.4 Arithmetic operations on Modules using a function
11.5 Introduction to the search path, global and local functions, filter functions
11.6 Python packages, import in packages, various ways of accessing the packages
11.7 Decorators, pointer assignments, and Xldr

Hands-on Exercise –
1. Demo on Importing the modules and performing various operations on them using arithmetic functions
2. Importing various packages, accessing them, and then performing different operations on them

12.1 Introduction to web scraping in Python
12.2 Installing beautiful soup
12.3 Installing Python parser lxml
12.4 Various web scraping libraries, beautiful soup, scrapy Python packages
12.5 Creating soup object with input HTML
12.6 Searching of tree, full or partial parsing, output print

Hands-on Exercise –
1. Installation of beautiful soup and lxml Python parser
2. Making a soup object with input HTML file
3. Navigating using Py objects in the soup tree