Tools for Data Science

Tools for Data Science

This course is part of multiple programs.

Instructors: Aije Egwaikhide +3 more

595,424 already enrolled

Included with

6 modules

Gain insight into a topic and learn the fundamentals.

30,343 reviews

Beginner level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

89%

Most learners liked this course

6 modules

Gain insight into a topic and learn the fundamentals.

30,343 reviews

Beginner level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

89%

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

Category: Other Programming Languages
Category: Data Visualization Software
Category: Open Source Technology
Category: Data Science
Category: Statistical Programming
Category: Software Development Tools
Category: Machine Learning
Category: Development Environment
Category: Cloud Hosting
Category: Cloud Computing

Tools you'll learn

Category: GitHub
Category: Python Programming
Category: Jupyter
Category: R Programming
Category: R (Software)
Category: Query Languages
Category: Application Programming Interface (API)
Category: Git (Version Control System)

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments

Taught in English

91%

of learners achieved a positive career outcome

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 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.

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

6 videosTotal 39 minutes

Course Introduction3 minutes
Categories of Data Science Tools8 minutes
Open Source Tools for Data Science - Part 18 minutes
Open Source Tools for Data Science - Part 25 minutes
Commercial Tools for Data Science7 minutes
Cloud Based Tools for Data Science8 minutes

4 readingsTotal 45 minutes

Learning goals for the course10 minutes
Model Development10 minutes
Summary: Open Source Tools for Data Science20 minutes
Module 1 Summary5 minutes

3 assignmentsTotal 54 minutes

Practice Quiz: Introduction to Data Science Tools12 minutes
Practice Quiz: Commercial and Cloud-Based Data Science Tools12 minutes
Graded Quiz - Data Science Tools 30 minutes

1 pluginTotal 15 minutes

Open Source Tool Board15 minutes

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

5 videosTotal 27 minutes

Libraries for Data Science5 minutes
Application Programming Interfaces (APIs)5 minutes
Data Sets - Powering Data Science6 minutes
Machine Learning Models – Learning from Models to Make Predictions7 minutes
The Model Asset eXchange4 minutes

3 readingsTotal 23 minutes

Additional Sources of Datasets5 minutes
Hands on Lab: Getting Started with Open Source Datasets and Deep Learning Models15 minutes
Module 3 Summary3 minutes

2 assignmentsTotal 42 minutes

Practice Quiz - Libraries, APIs, Data Sets, Models 12 minutes
Graded Quiz - Libraries, APIs, Data Sets, Models 30 minutes

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

6 videosTotal 21 minutes

Introduction to Jupyter Notebooks3 minutes
Getting Started with Jupyter4 minutes
Jupyter Kernels2 minutes
Jupyter Architecture2 minutes
Additional Anaconda Jupyter Environments6 minutes
Additional Cloud Based Jupyter Environments4 minutes

3 readingsTotal 22 minutes

(Optional): Hands-on Lab: Download & Install Anaconda on Windows15 minutes
Jupyter Notebooks on the Internet5 minutes
Module 4 Summary2 minutes

2 assignmentsTotal 40 minutes

Practice Quiz - Jupyter Notebooks and Jupyter Lab10 minutes
Graded Quiz - Jupyter Notebooks and JupyterLab 30 minutes

3 app itemsTotal 40 minutes

Hands-on Lab: Getting Started with Jupyter Notebooks10 minutes
Hands-on Lab: Using Markdown in Jupyter Notebooks15 minutes
Hands-on Lab: Working with Files in Jupyter Notebooks15 minutes

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

7 videosTotal 29 minutes

Introduction to R and RStudio3 minutes
Plotting in RStudio4 minutes
Overview of Git/GitHub4 minutes
Introduction to GitHub5 minutes
GitHub Repositories4 minutes
GitHub - Getting Started3 minutes
GitHub - Working with Branches 6 minutes

5 readingsTotal 68 minutes

[Optional] Download & Install R and RStudio15 minutes
Hands-on Lab: Getting Started with GitHub20 minutes
Hands-On Lab: Branching and Merging (Web UI)20 minutes
Module 5 Summary3 minutes
Glossary10 minutes

3 assignmentsTotal 50 minutes

Practice Quiz - RStudio 10 minutes
Practice Quiz - GitHub10 minutes
Graded Quiz - RStudio & GitHub 30 minutes

5 app itemsTotal 220 minutes

R Basics with RStudio15 minutes
Getting started with RStudio and Installing packages60 minutes
Creating Data Visualizations using ggplot60 minutes
Plotting with RStudio60 minutes
[Optional] Getting Started with Branches using Git Commands25 minutes

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

Instructor ratings

(5,131 ratings)

Aije Egwaikhide

IBM

6 Courses797,807 learners

Svetlana Levitan

IBM

1 Course595,424 learners

Romeo Kienzler

IBM

10 Courses835,354 learners

Offered by

IBM

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Learner reviews

5 stars
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4 stars
21.62%
3 stars
6.37%
2 stars
2.06%
1 star
1.95%

Showing 3 of 30343

Reviewed on Sep 15, 2020

Absolutely Loved this course!! Challenging at times to keep up with all the terms and processes. The course provided great insight into Data Science. Would highly recommend it as your first course.

Reviewed on Nov 5, 2019

It would be nice if you could update the material since some tools have changed either name or the way they look compared to the videos/images. Very good material though, I enjoyed the course much.

Reviewed on May 19, 2023

The course is overwhelming for a beginner with no experiecne of programming. The examples given in the class seem difficult and should have been of a lower difficulty level to keep the hopes high.

Frequently asked questions

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