What will you learn in Tools for Data Science Course
Identify and use the most common open-source tools in data science.
Navigate and perform basic operations using Jupyter Notebooks and RStudio.
Work with cloud-based tools like Watson Studio.
Understand how different tools integrate into the data science lifecycle.
Program Overview
Module 1: Introduction to Open Source Tools
⏱️ 1 week
Topics: Overview of data science tools, open source philosophy
Hands-on: Explore tool ecosystems used in the field (e.g., Python, R, Git)
Module 2: Jupyter Notebooks and JupyterLab
⏱️ 1 week
Topics: Working with Jupyter Notebook and JupyterLab
Hands-on: Run basic code cells, markdown, and outputs in notebooks
Module 3: RStudio and GitHub
⏱️ 1 week
Topics: Introduction to RStudio, Git, and GitHub basics
Hands-on: Create R scripts, clone repositories, and make commits
Module 4: IBM Watson Studio
⏱️ 1 week
Topics: IBM Cloud, Watson Studio environment setup
Hands-on: Build a data science project workspace in Watson Studio
Module 5: Final Assignment
⏱️ 1 week
Topics: Integration of learned tools into a real-world scenario
Hands-on: Complete a mini-project using various tools introduced
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Job Outlook
Proficiency in open-source tools like GitHub, Jupyter, and RStudio is essential for entry-level data science roles.
Job titles include Data Analyst, Junior Data Scientist, and AI Developer.
Strong demand in finance, tech, and healthcare industries.
Median salaries for data science roles range from $70K–$120K depending on experience and geography.
Specification: Tools for Data Science
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