Is IBM: Data Science Tools beginner friendly?

Yes, this course is designed for beginners. It covers foundational concepts and requires no prior advanced knowledge in the subject area.

How long does it take to complete IBM: Data Science Tools?

The total duration of the course is approximately 7 weeks. Since it is generally self-paced, completion time depends entirely on your learning schedule and availability.

Does this course include hands-on projects?

Yes, the curriculum is highly practical and includes hands-on projects. Completing these will help you build a portfolio of work to demonstrate your new skills.

Do I get a certificate after completing IBM: Data Science Tools?

Yes, a certificate of completion is included, which you can add to your professional profile or resume to showcase your newly acquired skills.

What careers is this course relevant for?

The skills taught in this course are highly relevant for professionals pursuing roles within Business & Management and adjacent technical fields looking to upskill.

Business & Management

IBM: Data Science Tools

Name: IBM: Data Science Tools
Price: 8217 INR
Availability: InStock
Rating: 4.3 (29 reviews)

Learn about the most popular data science tools, including how to use them and what their features are.

4.3|Reviews (29)

radio_button_checkededX Subscription

₹28,072/yr

Get this course and thousands more with a edX Subscription subscription.

radio_button_uncheckedIndividual Course

₹82170

Keep the course forever with lifetime access and receive a certificate.

EnterpriseUpskill your entire organization with access to the entire course library and enterprise services.

Custom Pricing

EssentialsUpskill your team in one critical focus area with access to a limited number of courses.

₹10,634

Why choose Teams?

check

8 academies

check

2,300+ self-paced courses

check

300+ professional certificates

Compare all plansarrow_forward

verifiedGet this course for free with the Coursera Plus subscription.

✓ Compare courses before making a decision

Check Latest Price →

Price may vary. Check latest price on provider site.

Course Insight

Suitable for beginner learners. This course serves as an entry point into Business & Management, building foundational knowledge before moving on to advanced frameworks or specialized paths.

Beginner FriendlyCertification IncludedSelf-Paced LearningProject-Based

SKILLS TO
MASTER

Business & Management Basics

Fundamental principles and concepts

Practical Application

Real-world project implementation

Best Practices

Industry standard workflows and guidelines

Problem Solving

Core Concepts

Implementation

Workflow Integration

Optimization

Careers:Data Scientist, Data Analyst, Machine Learning Engineer.

Quick Facts

7 weeks

Below sections are verified from last major sync. For real-time updates and today's latest lectures, Check official page here.

What You’ll Learn

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

In this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these tools today.

With the tools hosted in the cloud, you will be able to test each tool and follow instructions to run simple code in Python or R. To complete the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio on Cloud and demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers.

This hands-on course will get you up and running with some of the latest and greatest data science tools.

See how this course curriculum compares with alternatives

Outcomes

List various tools used by data scientists and machine learning engineers.
Describe various programming languages used by data scientists such as Python, R, Julia and SQL.
Explain the various components of a data scientist's toolkit, including Libraries, Packages, Data sets and Machine Learning Models.
Explain the features of Jupyter Notebooks and how to use them.
Work with popular tools employed by data scientists including RStudio IDE and GitHub.
Create and share a Jupyter Notebook.
Navigate IBM Watson Studio and describe its features and capabilities.