Course HighlightsCOURSE
Data Science Tools

Data Science Tools

Explore popular data science tools. Discover their features and learn how to use them. Become familiar with Jupyter Notebooks, RStudio IDE, and Watson Studio.

Gain practical experience through project work and build valuable knowledge for a rewarding career in data science.

Data Science Tools Highlights

Course Enrollment

Starts on

15 June 2021

Enrollment closes on
31 December 2022

  Course duration

Duration

  • 5 weeks, online
    4-5 hours/week
  Course Fee

Fee

US$99 - US$199

Course Enrollment

Starts on

15 June 2021

Enrollment closes on
31 December 2022

Course duration

Duration

  • 5 weeks, online
    4-5 hours/week
Course Fee

Fee

US$99 - US$199

Data science is a blend of programming tools, statistical analysis, algorithms, and machine learning principles. It involves the use of multiple approaches including statistics, scientific methodologies, artificial intelligence (AI), and data analysis. A data scientist will utilize all these instruments to then discover meaningful patterns hidden in raw data.

In this course, you will learn about key tools used in data science, including Jupyter Notebooks, RStudio IDE, and Watson Studio. You will discover what each tool is used for, what programming languages they can execute, and what their features and limitations are. Using tools hosted in the cloud, you will then learn how to test each tool and run simple code in Python or R. Plus, you will complete a final project with a Jupyter Notebook on IBM Watson Studio that will demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers.

If you’re keen to pursue a career in the world of data science, this course on data science tools will introduce you to some critical technologies. Plus, it will provide you with important knowledge you need to progress through the remaining courses of the IBM Data Science Professional Certificate to enable you to earn professional certification.

This course comprises four purposely designed modules that take you on a carefully defined learning path. If you are thinking about taking the course separately, it is worth noting that it is part of the IBM Data Science Professional Certificate Program and you may want to consider enrolling for the whole program rather than just enrolling for one course at a time.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 4-5 hours per week, you will complete the course in 4-5 weeks. However, as long as the course is completed before the end date, you can work at your own pace.

The materials for each module will become available when you start the particular module. Methods of learning and assessment will include videos, reading material, and online exams questions.

Once you have successfully completed the course, you will earn your IBM Certificate.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.

You will have:

  • Introductory knowledge of the languages and tools used in data science.
  • An understanding of common open source tools used to perform data science tasks.
  • An introductory understanding of IBM tools for data science.
  • Individuals looking to extend their introductory knowledge of data science.
  • Individuals who would like to explore the tools and technologies used in data science.
  • Individuals who would like to begin understanding how to use a particular data science approach in their work.

    No prerequisites are required for taking this course.

Course Outline

Introduction to Tools for Data Science
Course Overview
Prerequisite
Changelog
Learning Objectives
Syllabus
Grading Scheme
Languages of Data Science (2:16)
Introduction to Python (3:50)
Introduction to R Language (3:48)
Introduction to SQL (3:35)
Other Languages (6:35)
Practice Quiz - Languages
Categories of Data Science Tools (2:27)
Open Source Tools for Data Science - Part 1 (7:20)
Open Source Tools for Data Science - Part 2 (5:15)
Commercial Tools for Data Science (5:59)
Cloud Based Tools for Data Science (8:08)
Practice Quiz - Tools
Libraries for Data Science (4:31)
Application Programming Interfaces (API) (4:30)
Data Sets - Powering Data Science (6:10)
Machine Learning Models (7:03)
The Model Asset Exchange (5:59)
Practice Quiz - Packages, APIs, Data Sets, Models
Lab: Explore Data Sets and Models (1hr)
Module 1 - Graded Quiz
Introduction to R and RStudio (3:00)
Plotting in RStudio (3:44)
Lab: RStudio - The Basics (30 mins)
Lab: Basic plots in RStudio
Lab: Plotting with ggplot
Practice Quiz - RStudio
Introduction to Jupyter Notebooks (3:32)
Getting Started with Jupyter (3:34)
Jupyter Kernals (1:58)
Jupyter Architecture (1:59)
Lab: Jupyter Notebooks - The Basics (1hr)
Lab: Jupyter Notebooks - More Features (20 mins)
Lab: Jupyter Notebooks - Advanced Features (20 mins)
Reading: Jupyter Notebooks on the Internet
Practice Quiz - Jupyter Notebooks
Overview of Git/GitHub (4:27)
GitHub - Getting Started (3:26)
Lab: Getting Started with GitHub (20 mins)
GitHub - Working with Branches (5:26)
Lab: Branching, Merging and Pull Requests on GitHub (Optional)
Git and GitHub via command line (Optional)
Lab: Git and GitHub via command line
Configuring SSH access to repository (Optional)
Git and GitHub via command line instructions (Optional)
Branching and merging via command line (Optional) (5:34)
Lab 2: Branching and merging via command line (Optional)
Contributing to repositories via pull request (Optional) (8:42)
Lab 3: Contributing to repositories via pull request (Optional)
Practice Quiz - GitHub
Module 2 - Graded Quiz
What is IBM Watson Studio? (4:09)
Watson Studio Introduction (4:32)
Creating an Account on IBM Watson Studio (2:31)
Jupyter Notebooks in Watson Studio - Part 1 (2:41)
Jupyter Notebooks in Watson Studio - Part 2 (3:40)
Lab: Creating a Watson Studio Project with Jupyter Notebooks (1hr)
Linking GitHub to Watson Studio (2:38)
Practice Quiz - Watson Studio
IBM Watson Knowledge Catalog (6:25)
Data Refinery (7:18)
SPSS Modeler Flows in Watson Studio (6:11)
Lab: Modeler Flows in Watson Studio (1hr)
IBM SPSS Modeler (7:15)
IBM SPSS Statistics (7:46)
Model Deployment with Watson Machine Learning (4:50)
Auto AI in Watson Studio (4:20)
IBM Watson OpenScale (7:21)
Practice Quiz - Other IBM Tools
Module 3 - Graded Quiz
Peer-graded Assignment - Create and Share Your Jupyter Notebook
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Course Certificate

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Once you have completed this course, you will earn your certificate.

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Data Science Tools

FAQs

You will discover how to use data science tools such as Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages each one can execute, their features and limitations, and how data scientists use these tools today.

Yes, to complete the course you will create a final project with a Jupyter Notebook on IBM Watson Studio, on the cloud, and you will demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers.

You will learn how to use Jupyter Notebooks, and you will discover key details about its features and why it's so popular. You will also learn to create and share a Jupyter Notebook.

Data Science Tools is a course that runs online. To access the course materials, you will need access to the internet. When you enroll for this course (please note, this course is also part of the IBM Data Science Professional Certificate, though you don’t have to be enrolled on the program to take this course), you will find you have access to the course materials in your dashboard. You will therefore be able to see the course materials as soon as you enroll. 

Yes, if you successfully complete this course, you will earn an IBM Professional Certificate. Please note, this course is part of the IBM Applied Data Science Certificate Program, thus you will also be one step closer to obtaining IBM Data Science Professional Certification if that is something you are seeking to achieve. 

Data Science Tools has been developed as part of the IBM Data Science Professional Certificate Program. However, it is possible to enroll on this course without having enrolled on the program. You will gain an IBM Professional Certificate for this even if you’re not enrolled on the full program. There are no prerequisites for enrolling onto the course.