What is data analytics?

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.

Why start a career in data analytics?

<text variant="body1">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. <text variant="body1">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.

Why enroll in the Google Data Analytics Certificate?

<text variant="body1">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. <text variant="body1">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. <text variant="body1">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.

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.

Do you need to be strong at math to succeed in this certificate?

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.

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.

When will I have access to the lectures and assignments?

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.

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

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Introduction to Data Analysis Using Python

This course is part of Google Data Analytics Professional Certificate

Instructor: Google Career Certificates

44,806 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

114 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.

114 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

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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

Module details

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 videosTotal 46 minutes

Introduction to Course 7 4 minutes
Adrian: My path to a data career2 minutes
Welcome to module 12 minutes
Introduction to Python5 minutes
Discover more about Python7 minutes
Jupyter Notebooks3 minutes
Object-oriented programming5 minutes
Hamza: How Python helped my data science career3 minutes
Variables and data types6 minutes
Create precise variable names5 minutes
Data types and conversions4 minutes
Wrap-up1 minute

10 readingsTotal 84 minutes

Course 7 overview8 minutes
Helpful resources and tips8 minutes
From spreadsheets to SQL to Python10 minutes
Python versus other programming languages8 minutes
Introduction to R10 minutes
Ways to learn about programming12 minutes
How to use Jupyter Notebooks8 minutes
More about object-oriented programming8 minutes
Explore Python syntax8 minutes
Glossary terms from module 14 minutes

4 assignmentsTotal 70 minutes

Test your knowledge: Get started with the course6 minutes
Test your knowledge: The power of Python6 minutes
Test your knowledge: Using Python syntax8 minutes
Module 1 challenge50 minutes

3 ungraded labsTotal 100 minutes

Annotated follow-along guide: Hello, Python!20 minutes
Activity: Use Python syntax60 minutes
Exemplar: Use Python Syntax20 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 videosTotal 40 minutes

Welcome to module 23 minutes
Lateefat: Tips to address challenges when learning to code3 minutes
Define functions and return values 6 minutes
Write clean code4 minutes
Use comments to scaffold your code7 minutes
Make comparisons using operators4 minutes
Use if, elif, else statements to make decisions11 minutes
Wrap-up1 minute

4 readingsTotal 32 minutes

Reference guide: Functions8 minutes
Reference guide: Python operators8 minutes
Reference guide: Conditional statements8 minutes
Glossary terms from module 28 minutes

3 assignmentsTotal 64 minutes

Test your knowledge: Functions6 minutes
Test your knowledge: Conditional statements 8 minutes
Module 2 challenge50 minutes

5 ungraded labsTotal 180 minutes

Annotated follow-along guide: Functions and conditional statements20 minutes
Activity: Functions60 minutes
Exemplar: Functions20 minutes
Activity: Conditional statements60 minutes
Exemplar: Conditional statements20 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 videosTotal 40 minutes

Welcome to module 33 minutes
Michelle: Approach problems with an analytical mindset3 minutes
Introduction to while loops9 minutes
Introduction to for loops4 minutes
Loops with multiple range() parameters4 minutes
Work with strings4 minutes
String slicing7 minutes
Format strings5 minutes
Wrap-up2 minutes

5 readingsTotal 40 minutes

Loops, break, and continue statements8 minutes
For loops8 minutes
String indexing and slicing8 minutes
String formatting and regular expressions8 minutes
Glossary terms from module 38 minutes

4 assignmentsTotal 68 minutes

Test your knowledge: While loops 6 minutes
Test your knowledge: For loops 6 minutes
Test your knowledge: Strings6 minutes
Module 3 challenge50 minutes

7 ungraded labsTotal 260 minutes

Annotated follow-along guide: Loops and strings20 minutes
Activity: While loops60 minutes
Exemplar: While loops20 minutes
Activity: For loops60 minutes
Exemplar: For loops20 minutes
Activity: Strings60 minutes
Exemplar: Strings20 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 videosTotal 89 minutes

Welcome to module 42 minutes
Introduction to lists5 minutes
Modify the contents of a list4 minutes
Introduction to tuples4 minutes
More with loops, lists, and tuples6 minutes
Introduction to dictionaries5 minutes
Dictionary methods5 minutes
Introduction to sets6 minutes
The power of packages4 minutes
Introduction to NumPy4 minutes
Basic array operations6 minutes
Introduction to pandas5 minutes
pandas basics10 minutes
Boolean masking6 minutes
Grouping and aggregation6 minutes
Merging and joining data9 minutes
Wrap-up 2 minutes
Course wrap-up 2 minutes

15 readingsTotal 88 minutes

Reference guide: Lists8 minutes
Compare lists, strings, and tuples8 minutes
zip(), enumerate(), and list comprehension4 minutes
Reference guide: Dictionaries4 minutes
Reference guide: Sets8 minutes
Understand Python libraries, packages, and modules8 minutes
Python’s new versions and features4 minutes
Reference guide: Arrays8 minutes
The fundamentals of pandas4 minutes
Boolean masking in pandas 4 minutes
More on grouping and aggregation8 minutes
Glossary terms from module 48 minutes
Reflect and connect with peers4 minutes
Course 7 glossary4 minutes
Coming up next...4 minutes

5 assignmentsTotal 83 minutes

Test your knowledge: Lists and tuples8 minutes
Test your knowledge: Dictionaries and sets6 minutes
Test your knowledge: Arrays and vectors with NumPy6 minutes
Test your knowledge: Dataframes with pandas8 minutes
Module 4 challenge55 minutes

9 ungraded labsTotal 340 minutes

Annotated follow-along guide: Data structures in Python20 minutes
Activity: Lists & tuples 60 minutes
Exemplar: Lists & tuples 20 minutes
Activity: Dictionaries & sets60 minutes
Exemplar: Dictionaries & sets20 minutes
Activity: Arrays and vectors with NumPy60 minutes
Exemplar: Arrays and vectors with NumPy20 minutes
Activity: Dataframes with pandas60 minutes
Exemplar: Dataframes with pandas20 minutes

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Google Career Certificates

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Reviewed on Mar 7, 2026

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

Reviewed on Mar 27, 2026

This course made me from Zero to Hero - Very Good Insights and very useful information

Reviewed on May 17, 2026

really good course, I just wish this entire program focused way more on hands on activities and guided projects than explanatory videos or videos about google employees experiences

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