Data Science Learning Path

  • BeginnerLevel

  • 25408+Students Enrolled

  • 30 Hrs Duration

  • 4.7Average Rating

About this Course

  • Learn essential data analysis skills through Excel, SQL, Power BI, and Python—the complete toolkit for beginners.
  • Understand the end-to-end data workflow, from data cleaning to dashboard creation, using real-world datasets.
  • Explore Python automation, Pandas, and visualization techniques to boost productivity in data-driven projects.
  • Gain practical experience through case studies that replicate real-world business analytics problems.
  • Build portfolio-ready projects that showcase your ability to analyze, visualize, and communicate insights effectively.
  • Earn a free data science certificate and become job-ready with guidance on interviews and resumes.

Learning Outcomes

Excel to Insight

Analyze, visualize & derive insights from data using Excel & Power BI

Code for Data

Learn Python and SQL to automate analysis and work with real datasets.

Present Like a Pro

Create interactive dashboards & reports that tell data stories.

Who Should Enroll

  • Beginners seeking a free data science course with a certificate and hands-on practice.
  • Students or career changers eager to learn analytics tools like Excel, SQL, and Python.
  • Working professionals aiming to upskill with data science online training.
  • Anyone wanting to explore the best data analytics certification path

Course Curriculum

This free data science learning path introduces you to the essentials of data analytics. Learn Excel for quick analysis, SQL for data querying, Python for automation, and Power BI tools for storytelling.

toolstoolstools

  1. 1. Basic Introduction

  2. 2. Data Science Metaverse

  3. 3. ABC of Data Scientist

  1. 1. Introduction & Basics

  2. 2. Productivity & Shortcuts

  3. 3. Power Query

  4. 4. Functions & Charts

  5. 5. Excel Formulas

  6. 6. Data Structure Query

  1. 1. Introduction to Databases

  2. 2. A Beginners Guide to SQL

  1. 1. Powering Your Decisions with Chart

  2. 2. Tableau in Action: Case Study-Driven Learning

  1. 1. Introduction to Python

  2. 2. Understanding Operators

  3. 3. Variables and Data Types

  4. 4. Conditional Statements

  5. 5. Looping Constructs

  6. 6. Python Functions

  7. 7. Data Structure

  8. 8. Python Lists

  9. 9. Understanding Standard Libraries in Python

  10. 10. Data Frames and basic operations with Data Frames

  11. 11. Data Manipulation and Visualization

  12. 12. Cheatsheet for Python

  1. 1. Overview of the Course

  2. 2. Introduction to Data Science and Machine Learning

  3. 3. Setting up your system

  4. 4. Introduction to Python

  5. 5. Variables and Data Types

  6. 6. Understanding Operators

  7. 7. Conditional Statements

  8. 8. Looping Constructs

  9. 9. Data Structures

  10. 10. String Manipulation

  11. 11. Understanding Functions

  12. 12. Modules, Packages and Standard Libraries

  13. 13. Handling Text Files in Python

  14. 14. Introduction to Python Libraries for Data Science

  15. 15. Python Libraries for Data Science

  16. 16. Reading Data Files in Python

  17. 17. Preprocessing, Subsetting and Modifying Pandas Dataframes

  18. 18. Sorting and Aggregating Data in Pandas

  19. 19. Visualizing Patterns and Trends in Data

  20. 20. Machine Learning Lifecycle

  21. 21. Problem statement and Hypothesis Generation

  22. 22. Importance of Stats and EDA

  23. 23. Build Your First Predictive Model

  1. 1. Introduction to Time Series

  2. 2. Understanding Problem Statements and Data Sets

  3. 3. Exploration and Preprocessing

  4. 4. Modelling Techniques and Evaluation

  1. 1. Intro to NLP using Pytorch

  2. 2. Setting up the System

  3. 3. Recap of MLP and RNN

  4. 4. Recap: NLP Basics

  5. 5. Neural Language Modeling

  6. 6. Sequence-to-Sequence Modeling

  7. 7. Transformer Models Basics

  8. 8. Understanding the BERT Model

  1. 1. Introduction to Deep Learning

  2. 2. Introduction to Neural Network

  3. 3. Forward and Backward Propagation

  4. 4. Activation Functions

  5. 5. Introduction to Optimizers

  6. 6. Loss Function

  7. 7. NN on structured Data

  8. 8. Functional API in Keras for Deep Learning

  9. 9. Getting started with image data

  10. 10. Solving Image Classification Using Keras

  11. 11. Improving your Deep Learning Model

  12. 12. Introduction to CNN and Implementation

  13. 13. Introduction to Transfer Learning

  14. 14. CNN Visualization

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Kunal Jain

Founder & CEO, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.

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Dhruv Singh

Corporate Trainer, Analytics Vidhya

Dhruv Singh is Microsoft Certified Corporate Trainer with deep expertise in Power BI, Power Platform, SQL Server, and Excel. Currently mentoring learners across 50+ global EdTech platforms as a Data Analytics Instructor.

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Apoorv Vishnoi

Training Head, Analytics Vidhya

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

Get this Course Now

With this course you’ll get

  • 30 Hours

    Duration

  • 4.7

    Average Rating

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
certificate

Frequently Asked Questions

Looking for answers to other questions?

No prior experience is needed. This learning path starts from the very basics. You will gradually learn essential tools like Excel, SQL, Python, and Power BI through guided lessons designed specifically for those new to the field.

Yes, this is a free course. Upon successfully finishing all lessons and hands-on projects, you will receive a certificate from Analytics Vidhya. This recognized credential helps showcase your new skills and your commitment to learning.

The course typically takes learners three to four weeks to complete. It is designed for flexibility, allowing you to learn at your own pace with self-paced video modules and practical assignments that fit your schedule.

This path is effective because it blends essential theory with practical application. You will cover key tools like Excel, SQL, Python, and Power BI, and finish with a real-world project that enhances your resume and professional portfolio.

Absolutely. The course is designed for non-technical learners. Every module begins with foundational concepts and progresses gradually, using clear examples, helpful visuals, and practical exercises to ensure everyone can build their skills with confidence.

Yes, you will work with real business datasets throughout the course. This includes automating tasks with Python and creating interactive dashboards for decision-making scenarios that directly mirror the challenges and use cases you will find in industry roles.

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