When you enroll through our links, we may earn a small commission—at no extra cost to you. This helps keep our platform free and inspires us to add more value.

Computer Science
IBM logo

IBM: Python Basics for Data Science

This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you'll be ready to create your first Python scripts on your own!

     
  • 4.5
    •  |
  • Reviews ( 289 )
₹8217
IBM: Python Basics for Data Science

Popular Courses

This Course Includes

  • iconedx
  • icon4.5 (289 reviews )
  • icon3 weeks at 4-10 hours per week
  • iconenglish
  • iconOnline - Self Paced
  • iconcourse
  • iconIBM

About IBM: Python Basics for Data Science

Please Note: Learners who successfully complete this IBM course can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

Kickstart your learning of Python for data science, as well as programming in general with this introduction to Python course. This beginner-friendly Python course will quickly take you from zero to programming in Python in a matter of hours and give you a taste of how to start working with data in Python. ~~~~

Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.

You can start creating your own data science projects and collaborating with other data scientists using IBM Watson Studio. When you sign up, you will receive free access to Watson Studio. Start now and take advantage of this platform and learn the basics of programming, machine learning, and data visualization with this Beginner course.

What You Will Learn?

  • Learn Python - the most popular programming language and easy to learn for beginners.
  • Develop fundamental Python skills for Data Science and Software Development.
  • Work with commonly used Python libraries such as Pandas and Numpy.
  • Utilize Jupyter Notebooks to develop, experiment with and execute Python code.
  • Access and webscrape data using APIs and libraries like Beautiful Soup.