Will this course prepare me for real-world data science roles?

Yes. By learning practical, applied Python skills, including data cleaning, analysis, and visualization, you gain the exact skills needed for junior data analyst or data science roles.

Are AI tools part of this Python course for data science?

Yes. You’ll learn how to use GenAI effectively to accelerate your learning, generate code snippets, and support data analysis workflows—without becoming dependent on AI.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Python for Data Science with AI

Ends tomorrow! Grow your skills with Coursera Plus for $239/year (usually $399). Save now.

Python for Data Science with AI

Instructors: Mirza Rahim Baig

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Master Python basics for data science - variables, data types, control structures, loops, and functions with demos
Write efficient Python logic using pseudocode, loops, and functions to solve real-world problems step by step
Analyze and transform data using Pandas - filter, sort, merge, group, and build insights from datasets
Create impactful visualizations with Matplotlib & Seaborn and work seamlessly on Google Colab using GenAI support

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There are 5 modules in this course

Step into the world of data science with this course designed to transform beginners into confident, job-ready data analysts.

Whether you’re a student, working professional or transitioning careers, this hands-on Python for Data Science with AI course is designed to have you coding right away in Google Colab and solving real-world data challenges with Python from day one. You’ll build a solid foundation in Python programming by mastering variables, data types, control structures, loops, and functions. Then, learn powerful tools like Pandas for cleansing and transforming datasets and Matplotlib & Seaborn for professional visualizations that convey insights. What makes this course stand out? Its case-study-driven approach, pseudocode-first workflow, AI-assisted learning, and heavy hands-on coding ensure you develop the exact practical skills employers are looking for in data roles. By the end, you’ll be able to: • Confidently analyze & clean real-world datasets. • Build data analysis workflows in Python. • Create impactful visualizations & dashboards. • Turn raw data into actionable insights. Ready to launch your data science journey? Enroll now and start analyzing data like a true professional!

This module introduces you to Python for data science, helping you understand its real-world relevance and setting up your coding environment using Google Colab.You’ll also learn a structured problem-solving approach using pseudocode while working on an initial case study.

What's included

30 videos2 readings8 assignments

30 videosTotal 88 minutes

Course Introduction4 minutes
Meet your instructor1 minute
Learning Objectives1 minute
Demo: Hands-on Reality Check: What Data Work Actually Looks Like5 minutes
Why Python Still Dominates Data Science and AI4 minutes
The Learning Approach Used in This Course4 minutes
Demo: Getting Started with Google Colab11 minutes
Demo: Writing Your First Python Program on Google Colab5 minutes
Learning Objectives1 minute
Introduction to the Case Study1 minute
How the Case Study Connects Every Concept You’ll Learn1 minute
The End-to-End Data Science Workflow2 minutes
Planning Your Solution Approach2 minutes
Summary1 minute
Learning Objectives1 minute
A Tale of Two Pythons1 minute
Python for Software Development vs Data Science5 minutes
What Matters for Programming for Data Science2 minutes
Code vs. Pseudocode4 minutes
Example of Code vs Pseudocode2 minutes
Importance of Pseudo Code2 minutes
Writing Pseudo Code2 minutes
Demo : Writing Pseudocode for Data Problems5 minutes
Summary1 minute
Learning Objectives1 minute
Common Mistakes Learners Make with GenAI3 minutes
The Illusion of “Knowing Code”2 minutes
A Smarter Way to Use Genai for Learning3 minutes
Demo: Using GenAI as a Coding Assistant8 minutes
Reflection & Next Steps3 minutes

2 readingsTotal 10 minutes

Summary5 minutes
Summary5 minutes

8 assignmentsTotal 166 minutes

Checkpoint Quiz20 minutes
Checkpoint Quiz12 minutes
Checkpoint Quiz24 minutes
Checkpoint Quiz20 minutes
Proficiency Quiz20 minutes
Proficiency Quiz15 minutes
Proficiency Quiz30 minutes
Proficiency Quiz25 minutes

This module builds your core Python foundation by covering variables, data types, strings, lists, and control structures like if-else.It also focuses on effective learning strategies and helps you apply these concepts to approach real-world problems confidently.

What's included

24 videos3 readings6 assignments

24 videosTotal 92 minutes

Revisiting the Case Study Requirements2 minutes
Identifying the Exact Python Concepts Needed2 minutes
Learning Objectives1 minute
Understanding Variables Used in Data Problems1 minute
Numbers and Strings in Python4 minutes
Demo : Working with Numbers6 minutes
Demo : Working with Strings9 minutes
Indexing in Strings2 minutes
Demo : Indexing and String Operations11 minutes
Lists and List Indexing3 minutes
Demo: Indexing and Access in Lists2 minutes
List Functions1 minute
Demo: Useful List Functions for Data Work3 minutes
Summary1 minute
Learning Objectives1 minute
Why Control Structures Matter in Data Logic2 minutes
'if' Statements in Python5 minutes
Demo : Simple 'if' Conditions6 minutes
'if-else' Logic4 minutes
Demo : Using "if-else"4 minutes
Multiple Conditions (if-elif-else)4 minutes
Demo: Using "if-elif-else"5 minutes
Nested Decision Logic5 minutes
Demo: Nested 'if' Statements7 minutes

3 readingsTotal 15 minutes

Learning Objective5 minutes
Summary5 minutes
Summary5 minutes

6 assignmentsTotal 155 minutes

Checkpoint Quiz10 minutes
Checkpoint Quiz28 minutes
Checkpoint Quiz32 minutes
Proficiency Quiz10 minutes
Proficiency Quiz40 minutes
Proficiency Quiz35 minutes

This module teaches you how to use loops and functions to write efficient, reusable, and scalable code.You’ll learn to translate pseudocode into Python programs and apply logic to solve real-world problems.

What's included

29 videos1 reading8 assignments

29 videosTotal 118 minutes

Learning Objectives1 minute
Reviewing the Problem Logic2 minutes
Identifying Repeated Patterns3 minutes
Demo: Writing Pseudocode Before Coding11 minutes
Summary1 minute
Learning Objectives1 minute
Why Loops Are Essential in Data Processing5 minutes
Pseudocode for "for" Loops7 minutes
Demo: Writing "for" Loops - Part-18 minutes
Demo: Writing "for" Loops - Part-26 minutes
“while” Loops and Use Cases6 minutes
Demo: "while" Loop2 minutes
Demo: Conditional Execution Inside “while” Loop - Part-18 minutes
Demo: Conditional Execution Inside “while” Loop - Part-24 minutes
Demo: Conditional Execution Inside “for” Loop6 minutes
Summary1 minute
Learning Objectives1 minute
Why Abstraction Improves Clarity3 minutes
How Functions Work in Python5 minutes
Python Inbuilt Functions1 minute
Demo: Inbuilt Functions3 minutes
Write Pseudocode for Custom Functions6 minutes
Writing Custom Functions6 minutes
Demo: Custom Functions8 minutes
Summary1 minute
Learning Objectives1 minute
Reviewing Solution Approach1 minute
Demo : Reviewing Pseudo Code6 minutes
Demo : Using Gemini to Verify Pseudo Code4 minutes

1 readingTotal 5 minutes

Summary5 minutes

8 assignmentsTotal 167 minutes

Checkpoint Quiz12 minutes
Checkpoint Quiz28 minutes
Checkpoint Quiz20 minutes
Checkpoint Quiz12 minutes
Proficiency Quiz15 minutes
Proficiency Quiz35 minutes
Proficiency Quiz30 minutes
Proficiency Quiz15 minutes

This module introduces data analysis using Pandas, where you’ll work with Series and DataFrames to handle real-world datasets.You’ll learn to clean, filter, merge, and analyze data, applying your skills to a practical case study.

What's included

32 videos1 reading8 assignments

32 videosTotal 155 minutes

Learning Objectives1 minute
Essential Python Libraries for Data Analysis5 minutes
Why Pandas Is the Core Data Tool5 minutes
Understanding Panda Series3 minutes
Demo: Pandas Series & Useful Functions10 minutes
Pandas DataFrames: Collection of Series3 minutes
Demo: Creating Dataframes9 minutes
Importing and Exporting Data4 minutes
Demo: Importing Data6 minutes
Demo: Exporting Data6 minutes
Summary1 minute
Learning Objectives1 minute
Data Access in Pandas (Loc, Iloc)1 minute
Demo: Data Access in Pandas14 minutes
Demo: Filtering and Querying Data11 minutes
Demo: Modifying Values8 minutes
Demo: Sorting Datasets9 minutes
Summary1 minute
Learning Objectives1 minute
Demo: Combining Datasets with Concat6 minutes
Demo: Data Merging6 minutes
Grouping and Aggregation4 minutes
Demo: Grouping and Summarizing Data7 minutes
Pivot Tables for Insights2 minutes
Demo : Pivot Tables7 minutes
Summary1 minute
Learning Objectives1 minute
Demo: Using Pandas for Our Case Study3 minutes
Demo: Writing Pseudocode for Our Case Study4 minutes
Demo - Verifying Pesudocode with Gemini3 minutes
Demo - Converting Pseudo Code to Python Code8 minutes
Demo - Evaluating the Code with Gemini3 minutes

1 readingTotal 5 minutes

Summary5 minutes

8 assignmentsTotal 213 minutes

Checkpoint Quiz28 minutes
Checkpoint Quiz20 minutes
Checkpoint Quiz20 minutes
Checkpoint Quiz20 minutes
Proficiency Quiz45 minutes
Proficiency Quiz25 minutes
Proficiency Quiz30 minutes
Proficiency Quiz25 minutes

This module focuses on data visualization using Matplotlib and Seaborn to turn data into meaningful insights.You’ll create and customize a variety of plots and apply the complete workflow to present results effectively.

What's included

25 videos2 readings6 assignments

25 videosTotal 119 minutes

Overview of Python Visualization Libraries4 minutes
Why Matplotlib Is Foundational2 minutes
Common Plot Types for Analysis3 minutes
Demo: Creating Line, Bar, Pie Charts9 minutes
Demo: Creating Histograms, Box Plots, Scatter Plots6 minutes
Demo: Many Ways to Plot6 minutes
Demo: Saving and Exporting Visualizations4 minutes
Summary1 minute
Learning Objectives1 minute
Why Seaborn Is Powerful for Analysis3 minutes
Statistical Visualizations in Seaborn5 minutes
Demo: Boxplots, Violin Plots, Histograms10 minutes
Demo: Scatter Plots, Regression Plots, and Facets6 minutes
Demo: Heatmaps for Correlations4 minutes
Customizing Visuals in Seaborn2 minutes
Demo: Customizing Visualizations7 minutes
Summary1 minute
Learning Objectives1 minute
Demo: Reviewing the Full Data Workflow4 minutes
Demo: Final Pseudocode Walkthrough4 minutes
Demo: Validating Pseudocode with Gemini4 minutes
Demo: Writing the Complete Python Solution - Part-112 minutes
Demo: Writing the Complete Python Solution - Part-29 minutes
Demo: Validating and Running the Full Process8 minutes
Course Completion3 minutes

2 readingsTotal 10 minutes

Learning Objectives5 minutes
Summary5 minutes

6 assignmentsTotal 185 minutes

Checkpoint Quiz28 minutes
Checkpoint Quiz28 minutes
Checkpoint Quiz24 minutes
Proficiency Quiz35 minutes
Proficiency Quiz35 minutes
Proficiency Quiz35 minutes

Instructors

Mirza Rahim Baig

6 Courses14,582 learners

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160 Courses198,895 learners

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Frequently asked questions

This course is perfect for beginners, students, and professionals who want practical Python skills for data science. No prior coding experience is required—learn to analyze data, visualize insights, and solve real-world problems from day one.

Unlike traditional courses, this program focuses on real-world case studies, demos, and logical thinking. You learn Python by solving real-world problems, using pseudocode, and applying tools like Pandas, Matplotlib, and Seaborn. It also teaches effective GenAI usage for faster, smarter learning.

No. This course starts from zero, covering Python fundamentals, control structures, loops, functions, and data analysis in a practical, context-based manner.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.