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Data analysis with python
New
9 students
Created byAkansha yadav
Last updated 5/2026
English

What you'll learn

  • Learn Python programming fundamentals required for data analysis and data science from scratch.
  • Analyze and clean real-world datasets using NumPy and Pandas efficiently.
  • Create professional data visualizations using Matplotlib and Seaborn libraries.
  • Perform Exploratory Data Analysis (EDA) and build real-world data analysis projects confidently.

Course content

1 section • 51 lectures • 27h 20m total length

  • Introduction47:19

    We will cover:

    1. What is Python?

    2. Why Python for Data Analysis?

    3. Installing Python & IDE Setup

    4. Your first python code

  • Data Types & Variables55:02

    Understand Python variables, data types, and how to store and manipulate data efficiently.

  • Operators & String Manipulation54:35

    Learn arithmetic, comparison, and logical operators along with string operations in Python.

  • Conditional Statements57:49

    Master decision-making in Python using if, else, elif, and nested conditions.

  • Loops in Python52:01

    Learn how to automate repetitive tasks using for loops and while loops.

  • loop methods and Functions in Python53:09

    Understand how to utilise the loops with built-in functions and learn to create reusable code using functions, arguments, and return values.

  • Modules & Packages and Mini Project45:07

    Learn how to import and use Python libraries, organise code using modules, and create a mini project with the concepts learned.

  • Built-in Data Structures51:45

    Explore lists, tuples, dictionaries, and sets for efficient data storage and manipulation.

  • Python Mini Project52:48

    Apply your Python fundamentals by building a practical mini project.

  • Exception Handling57:48

    Learn how to handle errors and exceptions gracefully using try and except blocks.

  • Object-Oriented Programming (OOP53:09

    Understand classes, objects, attributes, and methods using Object-Oriented Programming concepts.

  • Inheritance, Polymorphism & Encapsulation32:35

    Understand Inheritance, Polymorphism & Encapsulation in OOPs.

  • OOP Practical Implementation42:41

    Let's practically implement the OOP concept.

  • Searching Algorithms42:41

    We will cover the linear search and binary search algorithms.

  • Sorting Algorithms36:40

    Here we will cover the Bubble Sort and Selection Sort algorithms.

  • Stack Data Structure45:49

    Understand the LIFO/FIFO concept and stack/queue implementation in detail.

  • Major Python Project43:12

    Practical Implementation Project

  • Major Python Project50:21

    Practical Implementation Project-2

  • File Handling Basics59:18

    Learn how to create, open, read, and write files using Python.

  • File Operations in Python41:36

    Perform advanced file operations including updating and modifying file contents.

  • File Handling Mini Project33:06

    Build a practical project that uses file handling concepts for data storage and retrieval.

  • Math Library in Python28:44

    Explore Python's built-in mathematical functions and numerical operations.

  • DateTime & Time Library28:49

    Learn how to work with dates, times, timestamps, and scheduling tasks.

  • Collections Library30:48

    Understand powerful collection data structures such as Counter, defaultdict, and namedtuple.

  • Random Library Mini Project21:13

    Use the Random module to build practical applications and mini projects.

  • Random Library Mini Project32:21

    Use the Random module to build practical applications and mini projects.

  • Introduction to NumPy41:39

    Learn why NumPy is essential for Data Analysis and how NumPy arrays improve performance.

  • Array Indexing & Slicing32:26

    Master accessing, selecting, and slicing elements and handling missing data from NumPy arrays.

  • Array Manipulation19:38

    Learn to load CSV and work on it with reshaping, stacking, splitting, and combining arrays efficiently.

  • Linear Algebra with NumPy7:39

    Perform matrix operations and explore linear algebra concepts using NumPy.

  • Data Cleaning with numpy-17:39

    Perform the operations on the dataset and understand the core.

  • Data Cleaning with numpy-223:46

    Perform the operations on the dataset and understand the core.

  • Introduction to Pandas20:14

    Understand the fundamentals of Pandas and work with Series and DataFrames.

  • Data Loading & Exploration21:33

    Import datasets from CSV, Excel, and JSON files and perform exploratory analysis. Learn techniques for handling missing values, duplicates, and inconsistent data.

  • Data Cleaning & Preprocessing17:41

    Learn techniques for handling missing values, duplicates, and inconsistent data.


  • Seaborn library of Python13:17

    Get started with Python's most popular data visualization libraries.

  • Working with Time Series Data20:48

    Analyse date-based datasets using time series techniques and resampling methods.

  • Real-World Data Analysis Project22:24

    Apply Pandas concepts to analyze real-world datasets and generate insights.

  • Data Visualization with Python29:09

    Create charts, graphs, and visual reports to communicate data effectively.

  • Advanced DV Library21:36

    Get started with Python's most popular data visualization libraries to create dynamic and interactive graphs.

  • Real-World Data Analysis Project18:23

    Build an end-to-end visualization project using real-world datasets.

  • Dataset Practice Session- IPL Analysis29:41

    Practice data cleaning, analysis, and visualization on industry-style datasets.

  • Business level Analysis10:15

    Understand how businesses perform analysis and how it helps them.

  • Major Visualization Project12:22

    Build a complete end-to-end data analysis project using Python, NumPy, Pandas, and visualization tools.

  • Dataset Practice Session13:40

    Perform data cleaning and analysis.

  • Major Capstone Project-28:17

    Build a complete end-to-end data analysis project using Python, NumPy, Pandas, and visualization tools.

  • Practical implementaton11:48

    Practical implemention with dataset and libraries.

  • Portfolio Building with Streamlit18:53

    Learn how to create a professional portfolio using Streamlit.


  • Final Data Analysis Project26:13

    Add interactive components and improve the usability of your portfolio project.

  • How to Search for Jobs22:53

    Learn effective job search strategies, resume building, and LinkedIn optimization techniques.

  • Career Guidance & Next Steps17:53

    Understand career paths in Data Analysis, Data Science, AI, and Machine Learning, along with future learning recommendations.

Requirements

  • No programming experience needed. This course is beginner-friendly and everything will be taught step by step.
  • A computer or laptop with internet connection is required for coding practice and projects.
  • Basic knowledge of using a computer and installing software will be helpful but not mandatory.
  • Passion to learn Data Analysis, Python, AI, or Data Science is all you need to get started.

Description

Want to become a Data Analyst or start your journey in Machine Learning and AI using Python?

This course is designed for absolute beginners who want to learn Data Analysis with Python step by step, simply and practically.

Whether you are:

  • A college student

  • A beginner in programming

  • Switching careers

  • Preparing for Data Analyst roles

  • Learning AI/ML foundations

This course will help you build strong real-world data analysis skills .

What You Will Learn

  • Python basics for data analysis

  • NumPy from scratch

  • Pandas for data manipulation

  • Data cleaning techniques

  • Handling missing values

  • Data visualisation using Matplotlib & Seaborn

  • Exploratory Data Analysis (EDA)

  • Working with CSV datasets

  • Real-world projects

  • Data Analyst workflow

  • Industry-level practices

  • Beginner-friendly coding approach

Course Features

  • 48+ structured video lectures

  • Beginner-friendly explanations

  • Hands-on coding practice

  • Real-world datasets

  • Practical projects

  • Step-by-step learning path

  • Notes & practice files included

  • Learn by building projects

  • Real Projects Included

In this course, you will work on real datasets like:

  • Netflix Dataset Analysis

  • Sales Data Analysis

  • Student Performance Analysis

  • IPL / Cricket Dataset Analysis

  • Exploratory Data Analysis Projects

These projects will help you understand how data analysts work in real companies.

Tools & Libraries Covered

  • Python

  • NumPy

  • Pandas

  • Matplotlib

  • Seaborn

  • Plotly

  • CSV Files & Datasets

  • Jupyter Notebook / Google Colab

Who This Course Is For

  • Beginners with no coding experience

  • Students preparing for Data Science careers

  • Aspiring Data Analysts

  • Machine Learning beginners

  • Excel users moving to Python

  • Anyone interested in AI & Data

Why Learn Data Analysis?

Data Analysis is one of the most in-demand skills in:

  • Data Science

  • Artificial Intelligence

  • Machine Learning

  • Business Analytics

  • Product Analytics

  • Finance & Marketing

Companies use data to make decisions, and skilled analysts are needed everywhere.

By the End of This Course

You will be able to:

  • Analyze datasets confidently

  • Clean and process real-world data

  • Create visualizations and insights

  • Perform exploratory data analysis

  • Build a strong foundation for Machine Learning

  • Start building your Data Analyst portfolio

Requirements

No prior experience required.

You only need:

  • A computer/laptop

  • Internet connection

  • Passion to learn

We will start from the basics and move step by step.

Join Now

Start your journey into the world of Data Analysis, Python, AI, and Machine Learning today.

Learn practical skills.

Build projects.

Analyse real data.

Become job-ready.

Who this course is for:

  • This course is designed for beginners, students, aspiring Data Analysts, Machine Learning beginners, and anyone who wants to learn Data Analysis with Python from scratch using practical real-world projects.

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