What background is required?

No prior experience with spreadsheets or data analytics required. All you need is high school level math and curiosity about how things work.

What tools or platforms are included in the curriculum?

You'll learn to use analysis tools and platforms such as spreadsheets (Google Sheets or Microsoft Excel), SQL, presentation tools (Powerpoint or Google Slides), Tableau, Python, and Kaggle.

Which "spreadsheet" platform is being taught?

Learners can self-select which platform they want to use throughout the program, Google Sheets or Microsoft Excel. It’s entirely up to the learner’s preference, and all activities throughout the syllabus can be performed on either platform.

Do you need to take each course in course order?

We highly recommend completing the courses in the order presented because the content in each course builds on information from earlier lessons.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Introduction to Data Analysis Using Python

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

Introduction to Data Analysis Using Python

This course is part of Google Data Analytics Professional Certificate

Instructor: Google Career Certificates

21,814 already enrolled

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

55 reviews

Beginner level

No prior experience required

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

55 reviews

Beginner level

No prior experience required

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

What you'll learn

Explain how Python is used by data professionals
Explore basic Python building blocks, including syntax and semantics
Understand loops, control statements, and string manipulation
Use data structures to store and organize data

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your Data Analysis expertise

This course is part of the Google Data Analytics Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Google

There are 4 modules in this course

This course is the seventh course in the Google Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures.

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, you will: -Define what a programming language is and why Python is used by data scientists -Create Python scripts to display data and perform operations -Control the flow of programs using conditions and functions -Utilize different types of loops when performing repeated operations -Identify data types such as integers, floats, strings, and booleans -Manipulate data structures such as , lists, tuples, dictionaries, and sets -Import and use Python libraries such as NumPy and pandas

You’ll discover the main features and benefits of the Python programming language, and how Python can help power your data analysis. Python is an object-oriented programming language based on objects that contain data and useful code. You’ll become familiar with the core concepts of object-oriented programming: object, class, method, and attribute. You’ll learn about Jupyter Notebooks, an interactive environment for coding and data work. You’ll investigate how to use variables and data types to store and organize your data; and, you'll begin practicing important coding skills.

What's included

12 videos10 readings4 assignments3 ungraded labs

12 videos Total 46 minutes

Introduction to Course 7 4 minutes
Adrian: My path to a data career 2 minutes
Welcome to module 1 2 minutes
Introduction to Python 5 minutes
Discover more about Python 7 minutes
Jupyter Notebooks 3 minutes
Object-oriented programming 5 minutes
Hamza: How Python helped my data science career 3 minutes
Variables and data types 6 minutes
Create precise variable names 5 minutes
Data types and conversions 4 minutes
Wrap-up 1 minute

10 readings Total 84 minutes

Course 7 overview 8 minutes
Helpful resources and tips 8 minutes
From spreadsheets to SQL to Python 10 minutes
Python versus other programming languages 8 minutes
Introduction to R 10 minutes
Ways to learn about programming 12 minutes
How to use Jupyter Notebooks 8 minutes
More about object-oriented programming 8 minutes
Explore Python syntax 8 minutes
Glossary terms from module 1 4 minutes

4 assignments Total 70 minutes

Test your knowledge: Get started with the course 6 minutes
Test your knowledge: The power of Python 6 minutes
Test your knowledge: Using Python syntax 8 minutes
Module 1 challenge 50 minutes

3 ungraded labs Total 100 minutes

Annotated follow-along guide: Hello, Python! 20 minutes
Activity: Use Python syntax 60 minutes
Exemplar: Use Python Syntax 20 minutes

Next, you’ll discover how to call functions to perform useful actions on your data. You’ll also learn how to write conditional statements to tell the computer how to make decisions based on your instructions. And you’ll practice writing clean code that can be easily understood and reused by other data professionals.

What's included

8 videos4 readings3 assignments5 ungraded labs

8 videos Total 40 minutes

Welcome to module 2 3 minutes
Lateefat: Tips to address challenges when learning to code 3 minutes
Define functions and return values 6 minutes
Write clean code 4 minutes
Use comments to scaffold your code 7 minutes
Make comparisons using operators 4 minutes
Use if, elif, else statements to make decisions 11 minutes
Wrap-up 1 minute

4 readings Total 32 minutes

Reference guide: Functions 8 minutes
Reference guide: Python operators 8 minutes
Reference guide: Conditional statements 8 minutes
Glossary terms from module 2 8 minutes

3 assignments Total 64 minutes

Test your knowledge: Functions 6 minutes
Test your knowledge: Conditional statements 8 minutes
Module 2 challenge 50 minutes

5 ungraded labs Total 180 minutes

Annotated follow-along guide: Functions and conditional statements 20 minutes
Activity: Functions 60 minutes
Exemplar: Functions 20 minutes
Activity: Conditional statements 60 minutes
Exemplar: Conditional statements 20 minutes

You’ll start off by exploring loops, which repeat a portion of code until a process is complete. You’ll learn how to work with different kinds of iterative or repeating code, such as for loops and while loops. Then, you'll explore strings, which are sequences of characters like letters or punctuation marks. You’ll learn how to manipulate strings by indexing, slicing, and formatting them.

What's included

9 videos5 readings4 assignments7 ungraded labs

9 videos Total 40 minutes

Welcome to module 3 3 minutes
Michelle: Approach problems with an analytical mindset 3 minutes
Introduction to while loops 9 minutes
Introduction to for loops 4 minutes
Loops with multiple range() parameters 4 minutes
Work with strings 4 minutes
String slicing 7 minutes
Format strings 5 minutes
Wrap-up 2 minutes

5 readings Total 40 minutes

Loops, break, and continue statements 8 minutes
For loops 8 minutes
String indexing and slicing 8 minutes
String formatting and regular expressions 8 minutes
Glossary terms from module 3 8 minutes

4 assignments Total 68 minutes

Test your knowledge: While loops 6 minutes
Test your knowledge: For loops 6 minutes
Test your knowledge: Strings 6 minutes
Module 3 challenge 50 minutes

7 ungraded labs Total 260 minutes

Annotated follow-along guide: Loops and strings 20 minutes
Activity: While loops 60 minutes
Exemplar: While loops 20 minutes
Activity: For loops 60 minutes
Exemplar: For loops 20 minutes
Activity: Strings 60 minutes
Exemplar: Strings 20 minutes

Now, you’ll explore data structures in Python, which are methods of storing and organizing data in a computer. You’ll focus on data structures that are among the most useful for data professionals: lists, tuples, dictionaries, sets, and arrays. You’ll also discover how to categorize data using data loading, cleaning, and binning. Lastly, you’ll learn about two of the most widely used and important Python tools for advanced data analysis: NumPy and pandas.

What's included

18 videos15 readings5 assignments9 ungraded labs

18 videos Total 89 minutes

Welcome to module 4 2 minutes
Introduction to lists 5 minutes
Modify the contents of a list 4 minutes
Introduction to tuples 4 minutes
More with loops, lists, and tuples 6 minutes
Introduction to dictionaries 5 minutes
Dictionary methods 5 minutes
Introduction to sets 6 minutes
The power of packages 4 minutes
Introduction to NumPy 4 minutes
Basic array operations 6 minutes
Introduction to pandas 5 minutes
pandas basics 10 minutes
Boolean masking 6 minutes
Grouping and aggregation 6 minutes
Merging and joining data 9 minutes
Wrap-up 2 minutes
Course wrap-up 2 minutes

15 readings Total 88 minutes

Reference guide: Lists 8 minutes
Compare lists, strings, and tuples 8 minutes
zip(), enumerate(), and list comprehension 4 minutes
Reference guide: Dictionaries 4 minutes
Reference guide: Sets 8 minutes
Understand Python libraries, packages, and modules 8 minutes
Python’s new versions and features 4 minutes
Reference guide: Arrays 8 minutes
The fundamentals of pandas 4 minutes
Boolean masking in pandas 4 minutes
More on grouping and aggregation 8 minutes
Glossary terms from module 4 8 minutes
Reflect and connect with peers 4 minutes
Course 7 glossary 4 minutes
Coming up next... 4 minutes

5 assignments Total 83 minutes

Test your knowledge: Lists and tuples 8 minutes
Test your knowledge: Dictionaries and sets 6 minutes
Test your knowledge: Arrays and vectors with NumPy 6 minutes
Test your knowledge: Dataframes with pandas 8 minutes
Module 4 challenge 55 minutes

9 ungraded labs Total 340 minutes

Annotated follow-along guide: Data structures in Python 20 minutes
Activity: Lists & tuples 60 minutes
Exemplar: Lists & tuples 20 minutes
Activity: Dictionaries & sets 60 minutes
Exemplar: Dictionaries & sets 20 minutes
Activity: Arrays and vectors with NumPy 60 minutes
Exemplar: Arrays and vectors with NumPy 20 minutes
Activity: Dataframes with pandas 60 minutes
Exemplar: Dataframes with pandas 20 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Instructor ratings

(12 ratings)

Google Career Certificates

Google

386 Courses 15,954,107 learners

Offered by

Google

Explore more from Data Analysis

Google
Hello, Python!
Course
University of Michigan
Introduction to Data Science in Python
Course
Meta
Python Data Analytics
Course
Packt
Python for Data Analysis: Step-By-Step with Projects
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
83.63%
4 stars
12.72%
3 stars
0%
2 stars
0%
1 star
3.63%

Showing 3 of 55

Reviewed on Mar 7, 2026

This course help me to grow my career and improve my profile as a data analyst

View more reviews

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

Data is a group of facts that can take many different forms, such as numbers, pictures, words, videos, observations, and more. Data analytics is the collection, transformation, and organization of these facts in order to draw conclusions, make predictions, and drive informed decision making. Companies need data analysts to sort through this data to help make decisions about their products, services or business strategies.

The amount of data created each day is tremendous. Any time you use your phone, look up something online, stream music, shop with a credit card, post on social media, or use GPS to map a route, you’re creating data. Companies must continually adjust their products, services, tools, and business strategies to meet consumer demand and react to emerging trends. Because of this, data analyst roles are in demand and competitively paid.

Data analysts make sense of data and numbers to help organizations make better business decisions. They prepare, process, analyze, and visualize data, discovering patterns and trends and answering key questions along the way. Their work empowers their wider team to make better business decisions.

You will learn the skill set required for becoming a junior or associate data analyst in the Google Data Analytics Certificate. Data analysts know how to ask the right question; prepare, process, and analyze data for key insights; effectively share their findings with stakeholders; and provide data-driven recommendations for thoughtful action.

You’ll learn these job-ready skills in our certificate program through interactive content (discussion prompts, quizzes, and activities) in under six months, with under 10 hours of flexible study a week. Along the way, you'll work through a curriculum designed with input from top employers and industry leaders, like Tableau, Accenture, and Deloitte. You’ll even have the opportunity to complete a case study that you can share with potential employers to showcase your new skill set.

After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data analytics.

You don't need to be a math all-star to succeed in the certificate. You need to be curious, and open to learning with numbers (the language of data analysts). Being a strong data analyst is more than just math, it's like about asking the right questions, finding the best sources to answer your question effectively and illustrating your findings clearly in visualizations.

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.