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IBM

Tools for Data Science

IBM via Coursera

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Overview

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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.

Syllabus

  • Overview of Data Science Tools
    • In this module, you will learn about the different types and categories of tools that data scientists use and popular examples of each. You will also become familiar with Open Source, Cloud-based, and Commercial options for data science tools.
  • Languages of Data Science
    • For users who are just starting on their data science journey, the range of programming languages can be overwhelming. So, which language should you learn first? This module will bring awareness about the criteria that would determine which language you should learn. You will learn the benefits of Python, R, SQL, and other common languages such as Java, Scala, C++, JavaScript, and Julia. You will explore how you can use these languages in Data Science. You will also look at some sites to locate more information about the languages.
  • Packages, APIs, Data Sets, and Models
    • In this module, you will learn about the various libraries in data science. In addition, you will understand an API in relation to REST request and response. Further, in the module, you will explore open data sets on the Data Asset eXchange. Finally, you will learn how to use a machine learning model to solve a problem and navigate the Model Asset eXchange.
  • Jupyter Notebooks and JupyterLab
    • With the advancement of digital data, Jupyter Notebook allows a Data Scientist to record their data experiments and results that others can reuse. This module introduces the Jupyter Notebook and Jupyter Lab. You will learn how to work with different kernels in a Notebook session and about the basic Jupyter architecture. In addition, you will identify the tools in an Anaconda Jupyter environment. Finally, the module gives an overview of cloud based Jupyter environments and their data science features.

Taught by

Polong Lin

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Reviews

1.3 rating, based on 3 Class Central reviews

4.5 rating at Coursera based on 28373 ratings

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  • Anonymous
    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
    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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