1. PYTHON ENVIRONMENT SETUP & ESSENTIALS
1.1 Introduction to Python Language
1.2 Features, the advantages of Python over other programming languages
1.3 Python installation – Windows, Mac & Linux distribution for Anaconda Python
1.4 Deploying Python IDE
1.5 Basic Python commands, data types, variables, keywords and more
Hands–on Exercise:
1. Installing Python Anaconda on Windows, Linux, and Mac
2. PYTHON LANGUAGE BASIC CONSTRUCTS
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. Writing your first Python program
2. Writing a Python function (with and without parameters)
3. Using the Lambda expression
4. Writing a class
5. Creating a member function and a variable
6. Creating an object
7. Writing a for loop
3. OOP CONCEPTS IN PYTHON
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
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 check the balance, deposit money, and withdraw the
money using the concepts of OOPs
4. DATABASE CONNECTION
4.1 Understanding the Database, need of database
4.2 Installing MySQL on windows
4.3 Understanding Database connection using Python.
Hands–on Exercise:
1. Demo on database connection using Python and pulling data
5. NUMPY FOR MATHEMATICAL COMPUTING
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 a NumPy module
2. Creating an array using ND–array
3. Calculating standard deviation on an array of numbers
4. Calculating correlation between two variables
6. SCIPY FOR SCIENTIFIC COMPUTING
6.1 Introduction to SciPy
6.2 Functions building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy
with Bayes Theorem.
Hands–on Exercise
1. Importing of SciPy
2. Applying the Bayes theorem on the given dataset
7. MATPLOTLIB FOR DATA VISUALIZATION
7.1 How to plot graph and chart with Python
7.2 Various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots.
Hands–on Exercise:
1. Deploying Matplotlib for creating pie, scatter, line, and histogram charts
8. PANDAS FOR DATA ANALYSIS & MACHINE LEARNING
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 a record by a group
3. Applying filter on top viewing records
9. EXCEPTION HANDLING
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. MULTI–THREADING & RACE CONDITION
10.1 Introduction to Thread, need of threads
10.2 What are thread functions
10.3 Performing various operations on thread like joining a thread, starting a thread, 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
2. Performing multiple operations on them
11. PACKAGES & FUNCTIONS
11.1 Intro to modules in Python, need of 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 Intro to Search path, Global and local functions, filter functions
11.6 Python Packages, import in packages, various ways of accessing the packages
11.7 11.7 Decorators, Pointer assignments, and Xldr.
Hands–on Exercise:
1. Demo on importing modules and performing various operations on
them using arithmetic functions
2. Importing various packages, accessing them, and performing different
operations on them
