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

Coursera PlusMonthly 3 个月课程4 折优惠 ，让你轻松掌握闪耀技能。立即节省

Python for Data Science with AI

位教师：Mirza Rahim Baig

包含在中

了解更多

5个模块

深入了解一个主题并学习基础知识。

3 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

3 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有5个模块

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.

涵盖的内容

30个视频2篇阅读材料8个作业

30个视频总计88分钟

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

2篇阅读材料总计10分钟

Summary5分钟
Summary5分钟

8个作业总计166分钟

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

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.

涵盖的内容

24个视频3篇阅读材料6个作业

24个视频总计92分钟

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

3篇阅读材料总计15分钟

Learning Objective5分钟
Summary5分钟
Summary5分钟

6个作业总计155分钟

Proficiency Quiz10分钟
Proficiency Quiz40分钟
Proficiency Quiz35分钟
Checkpoint Quiz10分钟
Checkpoint Quiz28分钟
Checkpoint Quiz32分钟

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.

涵盖的内容

29个视频1篇阅读材料8个作业

29个视频总计118分钟

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

1篇阅读材料总计5分钟

Summary5分钟

8个作业总计167分钟

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

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.

涵盖的内容

32个视频1篇阅读材料8个作业

32个视频总计155分钟

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

1篇阅读材料总计5分钟

Summary5分钟

8个作业总计213分钟

Proficiency Quiz45分钟
Proficiency Quiz25分钟
Proficiency Quiz30分钟
Proficiency Quiz25分钟
Checkpoint Quiz28分钟
Checkpoint Quiz20分钟
Checkpoint Quiz20分钟
Checkpoint Quiz20分钟

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.

涵盖的内容

25个视频2篇阅读材料6个作业

25个视频总计119分钟

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

2篇阅读材料总计10分钟

Learning Objectives5分钟
Summary5分钟

6个作业总计185分钟

Proficiency Quiz35分钟
Proficiency Quiz35分钟
Proficiency Quiz35分钟
Checkpoint Quiz28分钟
Checkpoint Quiz28分钟
Checkpoint Quiz24分钟

位教师

Mirza Rahim Baig

6 门课程15,405 名学生

LearnKartS

163 门课程210,621 名学生

提供方

LearnKartS

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自 2018开始学习的学生

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常见问题

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.