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⇱ Hello, Python! | Coursera


Hello, Python!

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Hello, Python!

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Gain insight into a topic and learn the fundamentals.
4.8

88 reviews

Beginner level

Recommended experience

Flexible schedule
3 hours to complete
Learn at your own pace
99%
Most learners liked this course

Gain insight into a topic and learn the fundamentals.
4.8

88 reviews

Beginner level

Recommended experience

Flexible schedule
3 hours to complete
Learn at your own pace
99%
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

  • 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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Google Data Analysis with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • 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

There are 4 modules in this course

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.

By the end of this course, you will be able to: - Explain Python fundamentals, including core Python syntax, data types (integer, float, string), and variable assignment - Define fundamental concepts like object, class, method, and attribute in object-oriented programming - Recognize the uses and benefits of Jupyter Notebook for data work and as a Python environment - Identify Python's relevance to data science and why it is an essential tool for data analysis - Perform basic mathematical calculations in Python - Use Python's inherent capabilities to explore data effectively with built-in functions and keywords - Gain knowledge of how to manage and utilize Python packages and interpreter options

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

2 videosβ€’Total 7 minutes
  • Hello, Python!β€’2 minutes
  • Introduction to Pythonβ€’5 minutes
1 readingβ€’Total 8 minutes
  • Helpful resources and tipsβ€’8 minutes
1 assignmentβ€’Total 6 minutes
  • Test your knowledge: Get started with Pythonβ€’6 minutes

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

3 videosβ€’Total 14 minutes
  • Discover more about Pythonβ€’7 minutes
  • Jupyter Notebookβ€’3 minutes
  • Object-oriented programmingβ€’5 minutes
3 readingsβ€’Total 24 minutes
  • Python versus other programming languagesβ€’8 minutes
  • How to use Jupyter Notebooksβ€’8 minutes
  • More about object-oriented programmingβ€’8 minutes
1 assignmentβ€’Total 6 minutes
  • Test your knowledge: The power of Pythonβ€’6 minutes
1 ungraded labβ€’Total 20 minutes
  • Annotated follow-along guide: Hello, Python!β€’20 minutes

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

3 videosβ€’Total 15 minutes
  • Variables and data typesβ€’6 minutes
  • Create precise variable namesβ€’5 minutes
  • Data types and conversionsβ€’4 minutes
1 readingβ€’Total 8 minutes
  • Explore Python syntaxβ€’8 minutes
1 assignmentβ€’Total 8 minutes
  • Test your knowledge: Using Python syntaxβ€’8 minutes
2 ungraded labsβ€’Total 30 minutes
  • Activity: Use Python syntaxβ€’20 minutes
  • Exemplar: Use Python Syntaxβ€’10 minutes

Review everything you’ve learned and take the final assessment.

What's included

1 reading1 assignment

1 readingβ€’Total 10 minutes
  • Wrap-upβ€’10 minutes
1 assignmentβ€’Total 50 minutes
  • Course 1 challenge: Hello, Python! β€’50 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
4.6 (21 ratings)
Google
386 Coursesβ€’16,905,595 learners

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AV
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Reviewed on Dec 8, 2025

Amazing experience with hands on practice sessions

KS
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Reviewed on Oct 13, 2025

I learned a lot in this course and the interactive Jupyter notebooks are a fun way to learn.

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.

Large volumes of data β€” and the technology needed to manage and analyze it β€” are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.

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.

When you enroll in the course, you get access to all of the courses in the Specialization, 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.

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.

Financial aid available,