Tools for Data Science

Tools for Data Science

This course is part of multiple programs.

Instructors: Aije Egwaikhide

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.

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Assessments

8 assignments

Taught in English

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There are 4 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 videos3 readings2 assignments2 plugins

6 videosTotal 39 minutes

Course Introduction4 minutesPreview module
Categories of Data Science Tools7 minutes
Open Source Tools for Data Science - Part 17 minutes
Open Source Tools for Data Science - Part 25 minutes
Commercial Tools for Data Science6 minutes
Cloud Based Tools for Data Science8 minutes

3 readingsTotal 25 minutes

Learning goals for the course10 minutes
Model Development10 minutes
Module 1 Summary5 minutes

2 assignmentsTotal 40 minutes

Graded Quiz - Data Science Tools30 minutes
Practice Quiz - Data Science Tools10 minutes

2 pluginsTotal 20 minutes

Open source tool board15 minutes
Summary: Open Source Tools for Data Science5 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

6 videos1 reading2 assignments2 plugins

6 videosTotal 31 minutes

Libraries for Data Science5 minutesPreview module
Application Programming Interfaces (APIs)4 minutes
Data Sets - Powering Data Science5 minutes
Sharing Enterprise Data - Data Asset eXchange4 minutes
Machine Learning Models – Learning from Models to Make Predictions7 minutes
The Model Asset eXchange4 minutes

1 readingTotal 3 minutes

Module 3 Summary3 minutes

2 assignmentsTotal 42 minutes

Graded Quiz - Libraries, APIs, Data Sets, Models30 minutes
Practice Quiz - Libraries, APIs, Data Sets, Models12 minutes

2 pluginsTotal 20 minutes

Additional Sources of Datasets5 minutes
Reading: Getting started with the Model Asset eXchange and the Data Asset Exchange15 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 videos1 reading2 assignments3 app items2 plugins

6 videosTotal 21 minutes

Introduction to Jupyter Notebooks3 minutesPreview module
Getting Started with Jupyter4 minutes
Jupyter Kernels2 minutes
Jupyter Architecture2 minutes
Additional Anaconda Jupyter Environments5 minutes
Additional Cloud Based Jupyter Environments4 minutes

1 readingTotal 2 minutes

Module 4 Summary2 minutes

2 assignmentsTotal 40 minutes

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

2 pluginsTotal 20 minutes

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

Instructors

Instructor ratings

4.5 (4,877 ratings)

Aije Egwaikhide

IBM

6 Courses661,256 learners

Offered by

IBM

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Reviewed on Apr 12, 2020

It serves perfecty its aim that is giving a first glance of the open course tools for data science. Of course each tool is briefly touched and it hands over the student the duty to deepen each tool.

Reviewed on Nov 17, 2020

Some of the lab assignments had instructions that didn't line up with how the programs actually worked. This was particularly the case for modular flow where auto-numerics seemed impossible to use.

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.

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