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.
Tools for Data Science
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Tools for Data Science
This course is part of multiple programs.
Instructors: Aije Egwaikhide
499,975 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.5
(29,219 reviews)
Beginner level
Recommended experience
Flexible schedule
Approx. 6 hours
Learn at your own pace
88%
Most learners liked this course
What you'll learn
Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
Utilize languages commonly used by data scientists like Python, R, and SQL
Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features
Create and manage source code for data science using Git repositories and GitHub.
Skills you'll gain
Details to know
Shareable certificate
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Assessments
8 assignments
Taught in English
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There are 4 modules in this course
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.
What's included
6 videos3 readings2 assignments2 plugins
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.
What's included
5 videos1 reading2 assignments
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.
What's included
6 videos1 reading2 assignments2 plugins
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.
What's included
6 videos1 reading2 assignments3 app items2 plugins
Offered by
Recommended if you're interested in Data Analysis
University of Colorado Boulder
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University of California, Davis
Johns Hopkins University
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