Base Python For Data Analytics

Step-by-step Python training for beginners—no prior coding experience required!

What you'll learn

Understand and apply core Python concepts – Learn variables, data types, operators, and how Python works behind the scenes.

Work with essential data structures – Master lists, dictionaries, tuples, and sets to efficiently store and manipulate data.

Write and control program flow – Use if-else statements, for loops, and while loops to build logical and dynamic programs.

Create reusable code with functions and classes – Write functions and object-oriented programs to structure and optimize your code.

Requirements

Time to practice coding

Free Anaconda software

Free Chrome browser

No prior Python coding knowledge

Description

Who Should NOT Take This Course?This course is designed for those who want to truly learn Python and build a strong foundation for data analytics—not just memorize syntax.If you enjoy learning by solving problems rather than just copying and pasting code, you’ll benefit the most.This course focuses on base Python fundamentals without relying on libraries like NumPy, Pandas, or Matplotlib.If you're looking for a shortcut to a certificate or a quick-fix approach, this may not be the right course for you. But if you want to develop real skills and confidence in Python, you're in the right place!"If you want to truly learn Python and build a strong foundation for data analytics, this course is exactly what you need!"Want to build a solid foundation in Python? This course is for you!Get 570+ practice questions, including standalone exercises and projects to apply your Python skills!!!Python is one of the most widely used programming languages, especially in data analytics, machine learning, and automation. Before diving into specialized libraries like NumPy and pandas, it’s crucial to master base Python concepts—and that’s exactly what this course will help you do. Note that numpy, pandas, matplotlib is not covered in this course.This course focuses on pure base Python for analytics. It does not cover NumPy, pandas, or matplotlib.What You Will Learn:- Python syntax, variables, and data types-Working with lists, tuples, dictionaries, and sets- Using if-else statements and loops to control program flow- Writing functions and understanding function arguments- Introduction to object-oriented programming (OOP) with classes and objects- Best practices for writing clean and efficient Python codeCourse Structure:(1) Introduction to Python – Why Python? Installation and setup (Anaconda & Jupyter Notebook).(2) Python Basics – Variables, data types, type conversions, operators, and expressions.(3) Data Structures – Lists, tuples, dictionaries, sets, and their use cases.(4) Control Flow – If-else conditions, for loops, while loops, and iteration techniques.(5) Functions & Modular Programming – Writing reusable functions, function arguments, and lambda functions.(6) Object-Oriented Programming (OOP) – Understanding classes, objects, methods, and basic OOP concepts.(7) Hands-on Coding Exercises – Apply your skills with real-world examples and challenges.This course is designed for absolute beginners and requires no prior programming experience. By the end, you’ll have a strong grasp of Python fundamentals, preparing you for advanced topics like data analytics and automation.Join now and start your Python journey today!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Install Anaconda Navigator on Mac

Lecture 3 Install Anaconda Navigator on Windows

Lecture 4 Overview of Jupyter notebook

Section 2: Boolean

Lecture 5 Truth value testing

Lecture 6 Mini Project: The Mysterious Cave

Lecture 7 Boolean operations

Lecture 8 Boolean Puzzle – The Digital Lock

Lecture 9 Comparisons

Lecture 10 Mini Project: The Treasure Chest Lock ?

Lecture 11 Project: The Secret Society Entrance Test ?

Section 3: Numeric types

Lecture 12 Numeric types

Lecture 13 Mini Project: Numeric Types Adventure

Lecture 14 Math operations

Lecture 15 Mini Project: The Treasure Vault Puzzle

Lecture 16 Project: The Lost Treasure of Data Island

Section 4: Sequence operations

Lecture 17 Sequence types: List

Lecture 18 Mini Project: Organizing a Grocery List

Lecture 19 Sequence operations - 1

Lecture 20 Mini Project: Movie Night Planner

Lecture 21 Sequence operations - 2

Lecture 22 Mini Project: Student Grade Manager (Using Lists & Tuples)

Lecture 23 Range

Lecture 24 Mini Project: Sequence-Based Countdown Timer

Lecture 25 String

Lecture 26 Mini Project: Text Formatter

Lecture 27 Print

Lecture 28 Mini Project: Personalized Greeting Generator

Lecture 29 Project: The Grand Data Analyzer

Section 5: Binary data

Lecture 30 Binary data - 1

Lecture 31 Mini Project: Decoding and Manipulating Binary Messages

Lecture 32 Binary data - 2

Lecture 33 Mini-Project: Custom Binary Message Printer

Lecture 34 Project: Byte-Based Data Report Generator

Section 6: Sets, Mapping types: Dictionary

Lecture 35 Set types

Lecture 36 Mini Project: Data Categorization using Sets

Lecture 37 Mapping types: Dictionary

Lecture 38 Mini Project: Student Grades Analysis

Lecture 39 Project: Customer Purchase Data Analysis

Section 7: Control flow

Lecture 40 Control flow: If statement

Lecture 41 Mini Project: Customer Discount Eligibility

Lecture 42 Control flow: For loop

Lecture 43 Mini Project: Analyzing Store Revenue Data

Lecture 44 Control flow: While loop

Lecture 45 Mini Project: Analyzing Daily Temperature Data

Lecture 46 Control flow: Break and Continue

Lecture 47 Mini Project: Analyzing Product Sales Data

Lecture 48 Control flow: Else

Lecture 49 Mini Project: Data Analysis on Customer Purchases

Lecture 50 Pass

Lecture 51 Mini Project: Analyzing and Cleaning Customer Purchase Data

Lecture 52 Match

Lecture 53 Mini Project: Movie Ratings Classifier

Lecture 54 Project: NASCAR Race Analytics: Lap Data & Performance Analysis

Section 8: Functions

Lecture 55 Function

Lecture 56 Mini-Project: Analyzing NASCAR Race Data using Functions

Lecture 57 Mini-Project: Structural Load Analysis for a Bridge

Section 9: Classes

Lecture 58 Class

Lecture 59 Mini-Project: Building a Music Library Using Classes

Lecture 60 Mini-Project: Project: Building a Library Management System

Lecture 61 Class inheritance

Lecture 62 Mini-Project: Animal Sound Describer

Lecture 63 Project: Live Weather Data Processor

Section 10: Study Project: Build a Supermarket Module

Lecture 64 Project: Super Market

Section 11: Time

Lecture 65 Time

Lecture 66 Mini-Project: Analyzing Employee Work Hours

Section 12: Statistics

Lecture 67 Statistics

Lecture 68 Mini-Project: Analyzing Employee Salaries

Section 13: Handling files

Lecture 69 OS .

Lecture 70 glob

Lecture 71 Data compression

Lecture 72 File formats

Section 14: Grand Challenges: 200 Base Python Practice Questions & Solutions!

Lecture 73 Overview

Lecture 74 Grand Challenge 1

Lecture 75 Grand Challenge 2

Lecture 76 Grand Challenge 3

Lecture 77 Grand Challenge 4

Lecture 78 Grand Challenge 5

Lecture 79 Grand Challenge 6

Lecture 80 Course End - What's next!

Beginner in Python,Want to learn coding,Student,Working professional,Looking for a career change into data analytics or data science,Want to build a strong Python foundation before diving into data analytics, machine learning, or AI? Start here!