In this course, 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.



Hello, Python!
This course is part of Google Data Analysis with Python Specialization

Instructor: Google Career Certificates
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Recommended experience
What you'll learn
Explain how Python is used by data professionals
Explore basic Python building blocks, including syntax and semantics
Use Python's inherent capabilities to explore data effectively with built-in functions and keywords
Recognize the uses and benefits of Jupyter Notebook for data work and as a Python environment
Skills you'll gain
Details to know

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September 2025
4 assignments
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There are 4 modules in this course
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.
What's included
2 videos1 reading1 assignment
Become familiar with the core concepts of object-oriented programming: object, class, method, and attribute. Learn about Jupyter Notebooks, an interactive environment for coding and data work.
What's included
3 videos3 readings1 assignment1 ungraded lab
Investigate how to use variables and data types to store and organize your data; and begin practicing important coding skills.
What's included
3 videos1 reading1 assignment2 ungraded labs
Review everything you’ve learned and take the final assessment.
What's included
1 reading1 assignment
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science is part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
We highly recommend taking the courses in the order presented, as the content builds on information from earlier courses. This is the first course in a series of six courses that make up the Google Data Analysis with Python Specialization.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
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