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Tools for Data Science

IBM via Coursera


In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them.

You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools.

Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations.

This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala.

Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.


  • Data Scientist's Toolkit
    • In this module, you will get an overview of the programming languages commonly used, including Python, R, Scala, and SQL. You’ll be introduced to the open source and commercial data science tools available. You’ll also learn about the packages, APIs, data sets and models frequently used by Data Scientists.
  • Open Source Tools
    • In this module, you will learn about three popular tools used in data science: GitHub, Jupyter Notebooks, and RStudio IDE. You will become familiar with the features of each tool, and what makes these tools so popular among data scientists today.
  • IBM Tools for Data Science 
    • In this module, you will learn about an enterprise-ready data science platform by IBM, called Watson Studio. You'll learn about some of the features and capabilities of what data scientists use in the industry. You’ll also learn about other IBM tools used to support data science projects, such as IBM Watson Knowledge Catalog, Data Refinery, and the SPSS Modeler.
  • Final Assignment: Create and Share Your Jupyter Notebook
    • In this module, you will demonstrate your skills by creating and configuring a Jupyter Notebook. As part of your grade for this course, you will share your Jupyter Notebook with your peers for review.

Taught by

Polong Lin


1.3 rating, based on 3 Class Central reviews

4.5 rating at Coursera based on 25413 ratings

Start your review of Tools for Data Science

  • Anonymous

    Anonymous is taking this course right now.

    There are big problems with course, because videos are almost old for IBM Watson Studio, the platform were updated long time ago and no-one correct the instructions and videos. Moreover payment are not refundable, so it was just waste of money and time. Because of the old instructions you can't pass the assignment and earn certificate
  • Anonymous

    Anonymous completed this course.

    The class was sloppy, poorly organized, failed to provide any learning materials, failed to teach foundational concepts, and was overly-focused on IBM products. The worst online class I have ever taken.
  • Anonymous
    This class is like a laundry list of tools. While I could see the utility in awareness of tools in the field, it's difficult to retain anything useful. Also, the class is very focused on IBM products.

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