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

kurs ist nicht verfügbar in Deutsch (Deutschland)

Wir übersetzen es in weitere Sprachen.

Python for Data Science with AI

Dozenten: Mirza Rahim Baig

Bei enthalten

Mehr erfahren

5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

3 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

3 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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

Kompetenzen, die Sie erwerben

Kategorie: Pseudocode
Kategorie: Computer Programming
Kategorie: Data Visualization
Kategorie: Data Visualization Software
Kategorie: Data Analysis
Kategorie: Data Science
Kategorie: Programming Principles
Kategorie: Matplotlib
Kategorie: Plot (Graphics)
Kategorie: AI literacy
Kategorie: Problem Solving
Kategorie: Data Manipulation
Kategorie: Data Processing

Werkzeuge, die Sie lernen werden

Kategorie: Seaborn
Kategorie: Generative AI
Kategorie: Dashboard
Kategorie: Pandas (Python Package)
Kategorie: Python Programming

Wichtige Details

Zertifikat zur Vorlage

Zu Ihrem LinkedIn-Profil hinzufügen

Kürzlich aktualisiert!

April 2026

Bewertungen

36 Aufgaben

Unterrichtet in Englisch

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

Weitere Informationen zu Coursera für Unternehmen

Logos von Petrobras, TATA, Danone, Capgemini, P&G und L'Oreal

In diesem Kurs gibt es 5 Module

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.

Das ist alles enthalten

30 Videos2 Lektüren8 Aufgaben

30 VideosInsgesamt 88 Minuten

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

2 LektürenInsgesamt 10 Minuten

Summary5 Minuten
Summary5 Minuten

8 AufgabenInsgesamt 166 Minuten

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

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.

Das ist alles enthalten

24 Videos3 Lektüren6 Aufgaben

24 VideosInsgesamt 92 Minuten

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

3 LektürenInsgesamt 15 Minuten

Learning Objective5 Minuten
Summary5 Minuten
Summary5 Minuten

6 AufgabenInsgesamt 155 Minuten

Proficiency Quiz10 Minuten
Proficiency Quiz40 Minuten
Proficiency Quiz35 Minuten
Checkpoint Quiz10 Minuten
Checkpoint Quiz28 Minuten
Checkpoint Quiz32 Minuten

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.

Das ist alles enthalten

29 Videos1 Lektüre8 Aufgaben

29 VideosInsgesamt 118 Minuten

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

1 LektüreInsgesamt 5 Minuten

Summary5 Minuten

8 AufgabenInsgesamt 167 Minuten

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

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.

Das ist alles enthalten

32 Videos1 Lektüre8 Aufgaben

32 VideosInsgesamt 155 Minuten

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

1 LektüreInsgesamt 5 Minuten

Summary5 Minuten

8 AufgabenInsgesamt 213 Minuten

Proficiency Quiz45 Minuten
Proficiency Quiz25 Minuten
Proficiency Quiz30 Minuten
Proficiency Quiz25 Minuten
Checkpoint Quiz28 Minuten
Checkpoint Quiz20 Minuten
Checkpoint Quiz20 Minuten
Checkpoint Quiz20 Minuten

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.

Das ist alles enthalten

25 Videos2 Lektüren6 Aufgaben

25 VideosInsgesamt 119 Minuten

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

2 LektürenInsgesamt 10 Minuten

Learning Objectives5 Minuten
Summary5 Minuten

6 AufgabenInsgesamt 185 Minuten

Proficiency Quiz35 Minuten
Proficiency Quiz35 Minuten
Proficiency Quiz35 Minuten
Checkpoint Quiz28 Minuten
Checkpoint Quiz28 Minuten
Checkpoint Quiz24 Minuten

Dozenten

Mirza Rahim Baig

6 Kurse14.650 Lernende

LearnKartS

160 Kurse199.678 Lernende

von

LearnKartS

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Häufig gestellte Fragen

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.