Course Details
This comprehensive course introduces you to the most widely used data science tools in the industry. Whether you’re a beginner or looking to expand your toolkit, this course provides practical, hands-on experience with essential platforms and technologies.
Tools You’ll Master:
- Jupyter Notebooks – Interactive computing environment for data analysis and visualization
- RStudio – Integrated development environment for R programming
- Git and GitHub – Version control and collaborative development platform
- IBM Watson Studio – Cloud-based data science platform for enterprise solutions
Learn how to use these tools in practical, real-world data analysis scenarios with guided projects and hands-on exercises that mirror industry practices.
What You’ll Learn
This comprehensive course covers the complete data science toolkit. Here are the key tools and technologies you’ll master:
- Introduction to Data Science Ecosystem – Overview of the complete landscape and how different tools work together
- Working with Jupyter Notebooks – Interactive development environment for data analysis and documentation
- Using RStudio for Statistical Computing – Professional IDE for R programming and statistical analysis
- Understanding IBM Watson Studio – Enterprise-grade cloud platform for collaborative data science
- Data Refinement Tools (Data Refinery & OpenRefine) – Clean, transform, and prepare data for analysis
- Data Visualization with Tableau / Power BI – Create compelling visual stories from your data
- Python Libraries – NumPy, Pandas, Matplotlib, Seaborn – Essential Python tools for data manipulation and visualization
- SQL and NoSQL Tools – Database querying and management for structured and unstructured data
- Version Control with Git/GitHub – Professional code management and collaboration workflows
- Machine Learning with Scikit-learn / TensorFlow – Build and deploy predictive models and AI solutions
- Cloud-based Platforms (Azure, AWS, GCP) – Scale your data science projects using cloud infrastructure
Pro Tip: This course focuses on practical application rather than theory. You’ll work on real projects that you can add to your portfolio immediately.
Course Content
Requirements
This course is designed to be beginner-friendly and is open to anyone, regardless of prior experience or background.
Course Description
The Data Science Tools course provides a comprehensive introduction to the key tools and technologies used in the data science lifecycle. Designed for beginners, this course requires no prior experience and is ideal for anyone looking to build a strong foundation in data science. Learners will gain hands-on experience with essential tools such as Jupyter Notebooks, RStudio, IBM Watson Studio, and various Python libraries including Pandas, NumPy, and Matplotlib. The course also covers data cleaning and visualization using tools like Data Refinery, Tableau, and Power BI, as well as data management using SQL and NoSQL databases. By the end of the course, participants will be equipped to navigate the data science ecosystem, analyze and visualize data effectively, and collaborate on projects using version control and cloud-based platforms.
Course Content
Our comprehensive curriculum is organized into 7 core modules plus bonus content, designed to take you from beginner to proficient in data science tools:
- Introduction to Tools for Data Science
- General Information
- Learning Objectives and Syllabus
- Grading Scheme
- Languages of Data Science
- Introduction to Python
- Introduction to R Language
- Introduction to SQL
- Graded Quiz – Language of Data Science (6 Questions)
- Categories of Data Science Tools
- Open Source Tools for Data Science – Part 1
- Open Source Tools for Data Science – Part 2
- Commercial Tools for Data Science
- Cloud Based Tools for Data Science
- Graded Quiz – Data Science Tools (6 Questions)
- Libraries for Data Science
- Application Programming Interfaces (API)
- Data Sets – Powering Data Science
- Sharing Enterprise Data – Data Asset eXchange
- Machine Learning Models
- The Model Asset Exchange
- Lab: Explore Data Sets and Models
- Graded Quiz – Packages, APIs, Datasets, Models (6 Questions)
- Overview of Git/GitHub
- GitHub – Part 1
- GitHub – Part 2 (Optional)
- GitHub – Part 3 (Optional)
- Lab: Getting Started with GitHub
- Graded Quiz – GitHub (3 Questions)
- Getting Started with Jupyter Notebooks
- Getting Started with JupyterLab
- Jupyter Architecture
- Lab: Jupyter Notebooks – The Basics
- Lab: Jupyter Basics – on Cloud
- Lab: Jupyter Notebooks – Advanced Features 1
- Lab: Jupyter Notebooks – Advanced Features 2
- Reading: Jupyter Notebooks on the Internet
- Graded Quiz – Jupyter Notebooks (3 Questions)
- What is RStudio IDE?
- Installing Packages and Loading Libraries in RStudio IDE
- Plotting within RStudio IDE
- Lab: RStudio – The Basics
- Lab: RStudio Basics on Cloud
- Lab: Creating an Interactive Map in R
- Graded Quiz – RStudio (3 Questions)
- What is IBM Watson Studio?
- Watson Studio Introduction
- Creating an Account on IBM Watson Studio
- Jupyter Notebooks in Watson Studio – Part 1
- Jupyter Notebooks in Watson Studio – Part 2
- Lab: Creating a Watson Studio Project with Jupyter Notebooks
- Linking GitHub to Watson Studio
- Graded Quiz – Watson Studio (10 Questions)
- IBM Watson Knowledge Catalog
- Data Refinery
- SPSS Modeler Flows in Watson Studio
- Lab: Modeler Flows in Watson Studio (1hr)
- IBM SPSS Modeler
- IBM SPSS Statistics
- Model Deployment with Watson Machine Learning
- Auto AI in Watson Studio
- IBM Watson OpenScale
- Practice Quiz – Other IBM Tools
- Bonus Module Questions
- Module 1 – Graded Quiz
- Module 2 – Graded Quiz
- Module 3 – Graded Quiz
- Final Exam Instructions
- Final Exam (24 Questions)
- Timed Exam
- (Optional) Re-attempt Final Exam
- Feedback Form/Survey
- Course Certificate and Badge
- Instructor Info/Announcements
Course Requirements:
This course is designed to be beginner-friendly and is open to anyone, regardless of prior experience or background.