IBM

Tools for Data Science

IBM

Tools for Data Science

This course is part of multiple programs.

Aije Egwaikhide
Svetlana Levitan
Romeo Kienzler

Instructors: Aije Egwaikhide +3 more

602,097 already enrolled

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

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

  • Category: Data Science
  • Category: Other Programming Languages
  • Category: Version Control
  • Category: Software Development Tools
  • Category: Cloud API
  • Category: Computer Programming Tools
  • Category: Cloud Computing
  • Category: Cloud Platforms
  • Category: Data Visualization Software
  • Category: Cloud Services
  • Category: Cloud Hosting
  • Category: Statistical Programming

Tools you'll learn

  • Category: Git (Version Control System)
  • Category: Python Programming
  • Category: Scikit Learn (Machine Learning Library)
  • Category: R (Software)
  • Category: R Programming
  • Category: Integrated Development Environments
  • Category: Jupyter
  • Category: GitHub

Details to know

Shareable certificate

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Assessments

13 assignments

Taught in English

Build your subject-matter expertise

This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
  • 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 6 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 videos4 readings3 assignments1 plugin

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

5 videos3 readings2 assignments

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 videos3 readings2 assignments3 app items

R is a statistical programming language and is a powerful tool for data processing and manipulation. This module will start with an introduction to R and RStudio. You will learn about the different R visualization packages and how to create visual charts using the plot function. In addition, Distributed Version Control Systems (DVCS) have become critical tools in software development and key enablers for social and collaborative coding. While there are many distributed versioning systems, Git is amongst the most popular ones. Further in the module, you will develop the essential conceptual and hands-on skills to work with Git and GitHub. You will start with an overview of Git and GitHub, followed by creation of a GitHub account and a project repository, adding files to it, and committing your changes using the web interface. Next, you will become familiar with Git workflows involving branches and pull requests (PRs) and merges. You will also complete a project at the end to apply and demonstrate your newly acquired skills.

What's included

7 videos5 readings3 assignments5 app items

In this module, you will work on a final project to demonstrate some of the skills learned in the course. You will also be tested on your knowledge of various components and tools in a Data Scientist's toolkit learned in the previous modules.

What's included

2 readings1 assignment2 app items

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Aije Egwaikhide
Aije Egwaikhide
IBM
6 Courses806,659 learners
Svetlana Levitan
Svetlana Levitan
IBM
1 Course602,207 learners
Romeo Kienzler
Romeo Kienzler
IBM
10 Courses844,442 learners

Offered by

IBM

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