Mastering Python 3 With Hands On Programming

Learn extensive file handling - csv, json, pickling, gems of Python, data structures and Data Visualization techniques

What you'll learn

Learn Python programming from basics

Confirm your learning by writing programs

Become Proficient by carrying out the assignment

Learn core Python 3 and Introduction to Data Structures

Become thorough with file handling - CSV files, JSON, Pickling, text and binary files

Learn the gems of Python - lambda functions, modules, Decorators, functional programming, namespaces and packages

You will be able to face job interview successfully

Requirements

No Prior programming experience is need; you will learn everything from the scratch

Should have flair for Logical reasoning and a bit of Mathematics

Should have access to computers since you have to carry out abundant programming during and after the course

Be ready to type the programs in your computer and execute them

Description

The course on Python 3 (core) is embedded with hundreds of tested program, 5 assignments and number of Case Studies. The learners will gain immensely by using the Integrated DeveLopment Environment (IDLE) and Jupyter Notebook in Anaconda for executing the programs themselves.It gives comprehensive and in-depth coverage of functions and recursions and tail recursion, in particular. The powerful features of Python containers such as lists, tuples, sets, and dictionaries are explained in an interesting manner.Unicode-UTF-8, strings, selection and iteration concepts are explained with simplicity by executing programs. Python calculator and use of Mathematics and other libraries are illustrated.   Python is also an object-oriented programming (OOP) language. The OOP concepts of inheritance and polymorphism, which facilitate seamless reuse of existing code as implemented in Python are discussed in a lucid manner.Exceptions such as divide by zero might occur when we execute the programs. Python's techniques of such exception handling are described with interesting case studies.Python has many gems in terms of programming concepts such as decorator functions, lambda functions, function objects, and first-class functions, which are rather unique and thrilling and are discussed so that the student learns without even taking a break.An introduction to data structures using Python is given. The section addresses stack and queue and also four popular sorting methods.Data visualization must precede data analysis – predictive and descriptive. Python has excellent libraries for data visualization. An introduction to data visualization using pandas, matplotlib, and seaborn is given describing how to create a number of visuals.The learning by programming and learning one new concept at a time in each program. is the strategy adopted so as to ease the burden of learning a new and modern programming language.

Overview

Section 1: M1 (1) -Introduction

Lecture 1 M1 Lesson 1 Introduction

Lecture 2 M1 Lesson 2 Python Calculator

Lecture 3 M1 Lesson 3 Built-in Library Functions

Lecture 4 M1 Lesson 4 Numerical Computations

Lecture 5 M1 Lesson 5 Console Input

Lecture 6 M1 Lesson 6 Unicode

Lecture 7 M1 Lesson 7 Strings

Lecture 8 M1 Lesson 8 String Methods

Lecture 9 Lesson 9 Console Output

Section 2: M1(2) - Functions & Recursion

Lecture 10 M1 Lesson 10 Selection Constructs

Lecture 11 M1 Lesson 11 Iterations

Lecture 12 M1 Lesson 12 Functions

Lecture 13 M1 Lesson 13 Fruitful Functions

Lecture 14 M1 Lesson 14 Recursion

Section 3: M2(1) Containers

Lecture 15 M2 Lesson 1 List Fundamentals

Lecture 16 M2 Lesson 2 List advanced

Lecture 17 M2 Lesson 3 List Case Studies

Lecture 18 M2 Lesson 4 Tuples

Lecture 19 M2 Lesson 5 Sets

Lecture 20 M2 Lesson 6 Dictionaries

Section 4: M2(2) Object Oriented Programming

Lecture 21 M2 Lesson 7 Object Oriented Programming

Lecture 22 M2 Lesson 8 Classes & Objects

Lecture 23 M2 Lesson 9 Operator overloading

Lecture 24 M2 Lesson 10 Containership

Lecture 25 M2 Lesson 11 Multi-level &Multiple Inheritance

Lecture 26 M2 Lesson 12 Polymorphism

Lecture 27 M2 Lesson 13 Iterators

Section 5: M3(1) File, and Exception handling

Lecture 28 M3 Lesson 1 Introduction to File Handling

Lecture 29 M3 Lesson 2 Text and Binary Files

Lecture 30 M3 Lesson 3 Traversing in a file

Lecture 31 M3 Lesson 4 JSON and Pickling

Lecture 32 M3 Lesson 5 Comma Separated Value (CSV) files

Lecture 33 M3 Lesson 6 Exception Handling Basics

Lecture 34 M3 Lesson 7 Advanced Exception Handling

Section 6: M3(2) Some Gems of Python

Lecture 35 M3 Lesson 8 Lambdas

Lecture 36 M3 Lesson 9 Modules

Lecture 37 M3 Lesson 10 Datetime Module

Lecture 38 M3 Lesson 11 Functional Programming

Lecture 39 M3 Lesson 12 Decorators, Namespaces and Packages

Section 7: M3(3) Introduction to Data Structures with Python

Lecture 40 M3 Lesson 13 Intro’ to Data Structures – Stack and Queue

Lecture 41 M3 Lesson 14 Sorting Data

Section 8: M3(4) Descriptive Analytics - Data Visualization

Lecture 42 M3 Lesson 15 Data Visualization

Lecture 43 M3 Lesson 16 Data Visualization with Pandas

Lecture 44 M3 Lesson 17 Data Visualization using Matplotlib and Seaborn

Students willing to Learn Python,Software developers who wish to brush up their knowledge in Python,Beginner Python developer curious about machine learning and data science,Students of all streams such as engineering, Science and computer applications who have taken Python as a subject,Persons interested in new jobs in Machine Learning and data science,Persons with Programming as a hobby