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
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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
January 2026
16 assignments
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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
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Reviewed on Jun 6, 2026
I liked how it went form basics to advanced,you might need to exercise some patience, but it will give you a very strong foundation in python.
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
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.
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