Published 9/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.63 GB | Duration: 15h 37m
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
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