4.5
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26,039 ratings

Aije Egwaikhide +2 more instructors

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Part of the Bachelor of Applied Arts and Sciences degree
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IBM Skills Network

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Beginner Level

Anyone can complete this course in a self-paced manner without any prior programming or Data Science experience.

Approx. 16 hours to complete

English

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What you will 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 will gain

Data Science
Github
Python Programming
Jupyter notebooks
Rstudio

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Anyone can complete this course in a self-paced manner without any prior programming or Data Science experience.

Approx. 16 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.43/5 (3,899 Ratings)

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

Start working towards your Bachelor's degree

This course is part of the 100% online Bachelor of Applied Arts and Sciences from IBM Skills Network. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Content Rating85%(53,617 ratings)

Week1

Week 1

1 hour to complete

Overview of Data Science Tools

1 hour to complete

6 videos (Total 39 min), 1 reading, 2 quizzes

Week2

Week 2

1 hour to complete

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.

1 hour to complete

5 videos (Total 21 min), 1 reading, 2 quizzes

Week3

Week 3

2 hours to complete

Packages, APIs, Datasets and Models

2 hours to complete

6 videos (Total 33 min), 1 reading, 2 quizzes

6 videos

Libraries for Data Science5mApplication Programming Interfaces (APIs)4mData Sets - Powering Data Science5mSharing Enterprise Data - Data Asset eXchange4mMachine Learning Models – Learning from Models to Make Predictions7mThe Model Asset eXchange6m

1 reading

Module 3 Summary2m

2 practice exercises

Practice Quiz - Libraries, APIs, Data Sets, Models12mGraded Quiz - Libraries, APIs, Data Sets, Models30m

Week4

Week 4

2 hours to complete

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.

2 hours to complete

6 videos (Total 21 min), 1 reading, 5 quizzes

Week5

Week 5

5 hours to complete

RStudio & GitHub

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.

5 hours to complete

8 videos (Total 34 min), 2 readings, 7 quizzes

Week6

Week 6

3 hours to complete

Create and Share your Jupyter Notebook

3 hours to complete

Week7

Week 7

2 hours to complete

[Optional] Bonus Module

Watson Studio is a collaborative platform for the data science community and is used by Data Analysts, Data Scientists, Data Engineers, Developers, and Data Stewards to analyze data and construct models. In this module, you will learn about Watson Studio and IBM Cloud Pak for data as a service. Then you will create an IBM Watson Studio service and a project in Watson Studio. After creating the project, you will create a Jupyter notebook and load a data file. You will also explore the different templates and kernels in a Jupyter notebook. Finally, you will connect your Watson Studio account to GitHub and publish the notebook in GitHub.

2 hours to complete

5 videos (Total 22 min), 1 reading, 2 quizzes

Reviews

4.5

4280 reviews

5 stars
66.78%
4 stars
22.18%
3 stars
6.76%
2 stars
2.23%
1 star
2.02%

TOP REVIEWS FROM TOOLS FOR DATA SCIENCE

by RJOct 7, 2021

Great course with practical approach to tools that come handy beside data science with python such as git and github, Some basic R coding and a great introduction to IBM Watson studio and cloud.

by FCNov 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.

by AJSep 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.

by NKMay 19, 2019

The course video contents and the tools versions are not the same.There are some significant differences .Videos should be updated.In general the course is a good fundamental course about the tools.

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The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
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When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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Tools for Data Science

About this Course

What you will learn

Skills you will gain

Instructors

Aije Egwaikhide

Svetlana Levitan

Romeo Kienzler

Offered by

IBM Skills Network

Start working towards your Bachelor's degree

Syllabus - What you will learn from this course

Overview of Data Science Tools

Languages of Data Science

Packages, APIs, Datasets and Models

Jupyter Notebooks and JupyterLab

RStudio & GitHub

Create and Share your Jupyter Notebook

[Optional] Bonus Module

Reviews

TOP REVIEWS FROM TOOLS FOR DATA SCIENCE

Frequently Asked Questions

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Tools for Data Science

About this Course

What you will learn

Skills you will gain

Instructors

Aije Egwaikhide

Svetlana Levitan

Romeo Kienzler

Offered by

IBM Skills Network

Start working towards your Bachelor's degree

Syllabus - What you will learn from this course

Overview of Data Science Tools

Languages of Data Science

Packages, APIs, Datasets and Models

Jupyter Notebooks and JupyterLab

RStudio & GitHub

Create and Share your Jupyter Notebook

[Optional] Bonus Module

Reviews

TOP REVIEWS FROM TOOLS FOR DATA SCIENCE

Frequently Asked Questions

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Popular Data Science Courses

Popular Computer Science & IT Courses

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Coursera

Community

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