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Intro to Data Science

Free Course

Learn What It Takes to Become a Data Scientist

Related Nanodegree Program

Introduction to Programming

About this course

The Introduction to Data Science class will survey the foundational topics in data science, namely:

  • Data Manipulation
  • Data Analysis with Statistics and Machine Learning
  • Data Communication with Information Visualization
  • Data at Scale -- Working with Big Data

The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science.

This course is also a part of our Data Analyst Nanodegree.

What you will learn

  1. Introduction to Data Science
    • Pi-Chaun (Data Scientist @ Google): What is Data Science?
    • Gabor (Data Scientist @ Twitter): What is Data Science?
    • Problems solved by data science.
  2. Data Wrangling
    • What is Data Wrangling?
    • Acquiring data.
    • Common data formats.
  3. Data Analysis
    • Statistical rigor.
    • Kurt (Data Scientist @ Twitter) - Why is Stats Useful?
    • Introduction to normal distribution.
  4. Data Visualization
    • Effective information visualization.
    • An analysis of Napoleon's invasion of Russia!
    • Don (Principal Data Scientist @ AT&T): Communicating Findings.
  5. MapReduce
    • Introduction to Big Data and MapReduce.
    • Learn the basics of MapReduce.
    • Mapper.

Prerequisites and requirements

The ideal students for this class are prepared individuals who have:

  • Strong interest in data science
  • Background in intro level statistics
  • Python programming experience
  • Or understanding of programming concepts such as variables, functions, loops, and basic python data structures like lists and dictionaries

If you need to brush up on your programming, we highly recommend Introduction to Computer Science: Building a Search Engine. If you need a refresher on statistics, enroll in Intro to Descriptive Statistics and Intro to Inferential Statisitics. All three are on Udacity!

See the Technology Requirements for using Udacity.

Why take this course?

You will have an opportunity to work through a data science project end to end, from analyzing a dataset to visualizing and communicating your data analysis.

Through working on the class project, you will be exposed to and understand the skills that are needed to become a data scientist yourself.

Learn with the best.

  • Dave Holtz
    Dave Holtz


  • Cheng-Han Lee
    Cheng-Han Lee