Key Data Science Concepts: Periodic Table Visual Guide

Written by Coursera • Updated on

Learn data science with the periodic table guide. Discover core concepts, advanced techniques, and essential skills quickly.

Periodic Table of Data Science Principles

Explore data science concepts with the periodic table guide. This visualization arranges fundamental techniques and theories into a user-friendly layout, making it accessible to everyone. Whether you're a novice, a seasoned professional, or curious about data science, this periodic table will clearly guide you through each concept. Discover topics from machine learning algorithms to statistical methods and see how these elements integrate to power data science.

Read more: What Is Data Science?

Core Principles

At the foundation, explore key concepts of data science—Artificial Intelligence (A), Machine Learning (M), Deep Learning (D), and Statistics (S)—that are fundamental to data science. Understanding these concepts sets the stage for advanced exploration and innovation.

Read more: What Is Artificial Intelligence?

Tools & Frameworks

The Tools & Frameworks category provides essential technical tools: Python (PY) and R (RR) for data science, SQL (SQ) for database management, and Spark (SK) for big data processing. Each tool equips you to handle, analyze, and process data at scale.

Data Handling

In the Data Handling category, learn about Cleaning (CL) noisy data, Wrangling (WR) raw datasets into usable formats, Ingesting (IN) data from diverse sources, and choosing the proper Storage (ST) solutions.

Read more: What Is Data Cleaning?

Modeling Techniques

Discover modeling techniques that power predictions and insights. Master Regression (RG), Classification (CS), Clustering (CL), and Dimensionality Reduction (DR) techniques.


Explore algorithms such as Neural Networks (NN), Ensembles (ES), Reinforcement Learning (RL), and Bayesian Methods (BM).

Visualization & Performance

Transform numbers into narratives with visualization tools like Plotting (PL), Dashboarding (DB), and Graphs (GP). Ensure projects are Scalable (SC), Efficient (EF), and Speedy (SP) with performance principles.

This guide helps you develop your knowledge and skills in data science. By understanding and applying these principles, you'll be equipped to tackle data science challenges and derive insights from data. Explore data science courses.

Coursera Plus
Build job-ready skills with a Coursera Plus subscription
  • Get access to 7,000+ learning programs from world-class universities and companies, including Google, Yale, Salesforce, and more
  • Try different courses and find your best fit at no additional cost
  • Earn certificates for learning programs you complete
  • A subscription price of $59/month, cancel anytime

Updated on
Written by:



Coursera is the global online learning platform that offers anyone, anywhere access to online course...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.