At a Glance

Name: Technologies & Tools for Data Science
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: skills_network_team

Master essential data science tools, including Jupyter Notebooks, Rstudio, and GitHub. Learn to work with libraries, packages, data sets, and machine learning models while using programming languages like Python, R, and SQL. Gain hands-on experience and create a final project to demonstrate your skills.

This course equips you with the necessary skills to effectively use the tools that data science professionals rely on in their daily work. You’ll explore a wide range of tools, including Jupyter Notebooks, JupyterLab, Rstudio IDE, Git, GitHub, and IBM Watson Studio, understanding their features, programming language compatibility, and limitations.

You will dive into the data scientist’s toolkit, learning to work with libraries, packages, data sets, machine learning models, and various big data and cloud-based tools. Through hands-on experience, you’ll gain practical knowledge, executing code in Python, R, or Scala on cloud-hosted environments.

Additionally, you’ll learn to create and manage source code using Git repositories and share your work with others. In the final project, you will prepare a Jupyter Notebook, demonstrating your expertise in Markdown and collaboration.

By completing this course, you’ll earn an IBM skill badge that verifies your knowledge and capabilities in using essential data science tools.

You’ll learn about:

The essential data science tools such as, Jupyter Notebooks, RStudio IDE, Git, and GitHub.

Libraries, packages, data sets, and machine learning models.

Programming languages commonly used in data science, including Python, R, and SQL.

Big Data tools and cloud-based platforms like IBM Watson Studio.

Code execution in Python, R, or Scale.

Source code creation using Git repositories.

Jupyter Notebook preparation and sharing in Markdown and collaborative work.

Enroll today and kickstart your data analytics career... You have a lot to look forward to!

PREREQUISITES: This is a beginner-friendly introduction to data analysis; therefore, no prior experience is necessary. However, basic knowledge of using a computer, navigating files and folders, and using basic software applications is recommended.

IBM Data Analyst Professional Certificate

This course is part of the IBM Data Analyst Professional Certificate. If you’re keen to kickstart a career in data analytics, we recommend you enroll for the full Professional Certificate program and work through the courses in order. Within just a few months, you’ll have job-ready skills and practical experience on your resume that will catch the eye of an employer!

Course Syllabus

Module 1: Overview of Data Science Tools

Course Introduction
Categories of Data Science Tools
Open Source Tools for Data Science
Commercial Tools for Data Science
Cloud-Based Tools for Data Science

Module 2: Languages of Data Science

Languages of Data Science
Introduction to Python
Introduction to R Language
Introduction to SQL
Other Languages for Data Science

Module 3: Packages, API, Data Sets, and Models

Libraries for Data Science
Application Programming Interfaces (APIs)
Data Sets – Powering Data Science
Sharing Enterprise Data – Data Asset eXchange
Machine Learning Models – Learning from Models to Make Predictions
The Model Asset eXchange

Module 4: Jupyter Notebooks and Jupyter Lab

Introduction to Jupyter Notebooks
Getting Started with Jupyter
Jupyter Kernels
Jupyter Architecture
Additional Anaconda Jupyter Environments
Additional Cloud Based Jupyter Environments

Module 5: RStudio & GitHub

Introduction to R and RStudio
Plotting in RStudio
Overview of Git/GitHub
Introduction to GitHub
GitHub Repositories
GitHub -Getting Started
GitHub – Working with Branches

Module 6: Create and Share your Jupyter Notebook

Module 7 (Optional): IBM Watson Studio

Introduction to Watson Studio
Optional: Creating an account on IBM Watson Studio
Jupyter Notebooks in Watson Studio
Linking GitHub to Watson Studio

What You'll Learn

Explain the components of a data scientist’s toolkit, including libraries, packages, data sets, machine learning models, and Big Data tools.
Discuss the programming languages data scientists use, such as Python, R, SQL, and Julia.
Describe the features of Jupyter Notebooks and its significance in data science.
Demonstrate proficiency with key tools like Jupyter Notebooks, RStudio IDE, and GitHub, and how to utilize their features in data science workflows.
Create, manage, and share source code for data science projects using Git repositories and GitHub.
Navigate and leverage IBM Watson Studio, outlining its features and capabilities for data science projects.

Recommended Skills Before Taking this Course

To get the most out of this course, you need to have basic computer skills, foundational mathematics and statistics, and familiarity with spreadsheets.

Offered By: IBMSkillsNetwork

Technologies & Tools for Data Science

At a Glance

Course Syllabus

What You'll Learn

Recommended Skills Before Taking this Course

This is part of the following learning path

Offered By: IBMSkillsNetwork

Technologies & Tools for Data Science

At a Glance

Course Syllabus

What You'll Learn

Recommended Skills Before Taking this Course

This is part of the following learning path

Stay Ahead in AI – Subscribe to Our Newsletter