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Introduction to Data Science


Learn one of the most battle-tested, internationally-recognized processes for solving data-science problems. Upon completing this course, you’ll have a reliable system for tackling data challenges, as well as the skills & know-how to share your findings with the world.

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  • Estimated time
    1 month

  • Enroll by
    June 14, 2023

    Get access to classroom immediately on enrollment

  • Skills acquired
    Data Storytelling, Statistical Modeling Fundamentals
In collaboration with
  • Bertelsmann
  • Appen
  • IBM Watson
  • Insight
  • Kaggle
  • Starbucks

What You Will Learn

  1. Introduction to Data Science

    1 month to complete

    Data Science as a practice spans a staggering variety of industries, each of which has its own methods for working with data. To succeed professionally as a Data Scientist, you need a consistent methodology that will translate across different fields. In this course, you’ll learn the most widely-used system for conducting data science, as well as how to publish your work online so others can learn from and build on it.

    Prerequisite knowledge

    Python, SQL, Statistics, Machine Learning.

    1. The Data Science Process

      Apply the CRISP-DM process to business applications and wrangle, explore and analyze a dataset.

      • Communicating with Stakeholders

        Implement best practices in sharing your code and written summaries and learn what makes a great data science blog.

        • Course Project: Write a Data Science Blog Post

          In this project, you will choose a dataset, identify three questions and analyze the data to find answers to these questions. You will create a GitHub repository with your project and write a blog post to communicate your findings to the appropriate audience. This project will help you reinforce and extend your knowledge of machine learning, data visualization and communication.

        All Our Courses Include

        • Real-world projects from industry experts

          With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.

        • Real-time support

          On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.

        • Workspaces

          Validate your understanding of concepts learned by checking the output and quality of your code in real-time.

        • Flexible learning program

          Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

        Course offerings

        • Class content

          • Real-world projects
          • Project reviews
          • Project feedback from experienced reviewers
        • Student services

          • Student community
          • Real-time support

        Succeed with personalized services.

        We provide services customized for your needs at every step of your learning journey to ensure your success.

        Get timely feedback on your projects.

        • Personalized feedback
        • Unlimited submissions and feedback loops
        • Practical tips and industry best practices
        • Additional suggested resources to improve
        • 1,400+

          project reviewers

        • 2.7M

          projects reviewed

        • 88/100

          reviewer rating

        • 1.1 hours

          avg project review turnaround time

        Learn with the best.

        Learn with the best.

        • Josh Bernhard

          Data Scientist at Nerd Wallet

          Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.

        • Juno Lee

          Curriculum Lead at Udacity

          Juno is the curriculum lead for the School of Data Science. She has been sharing her passion for data and teaching, building several courses at Udacity. As a data scientist, she built recommendation engines, computer vision and NLP models, and tools to analyze user behavior.

        • Luis Serrano


          Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

        • Andrew Paster


          Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity’s Self-Driving Car Engineer Nanodegree program.

        • Mike Yi

          Data Analyst Instructor

          Mike is a content developer with a multidisciplinary academic background, including math, statistics, physics, and psychology. Previously, he worked on Udacity's Data Analyst Nanodegree program as a support lead.

        • David Drummond

          VP of Engineering at Insight

          David is VP of Engineering at Insight where he enjoys breaking down difficult concepts and helping others learn data engineering. David has a PhD in Physics from UC Riverside.

        • Judit Lantos

          Senior Data Engineer at Netflix

          Judit is a Senior Data Engineer at Netflix. Formerly a Data Engineer at Split, where she worked on the statistical engine of their full-stack experimentation platform, she has also been an instructor at Insight Data Science, helping software engineers and academic coders transition to DE roles.

        Introduction to Data Science

        Get started today

          • Learn

            One of the most battle-tested, internationally-recognized processes for solving data-science problems.

          • Average Time

            On average, successful students take 1 month to complete this program.

          • Benefits include

            • Real-world projects from industry experts
            • Real-time support

          Program Details

          • Do I need to apply? What are the admission criteria?

            No. This Course accepts all applicants regardless of experience and specific background.

          • What are the prerequisites for enrollment?

            Machine Learning:

            • Supervised and Unsupervised methods equivalent to those taught in the Intro to Machine Learning Nanodegree Program.
            • Experience with Python Programming including writing functions, building basic applications, and common libraries like NumPy and pandas
            • SQL programming including querying databases, using joins, aggregations, and subqueries
            • Comfortable using the Terminal and Github
            Probability and Statistics:
            • Descriptive Statistics including calculating measures of center and spread
            • Inferential Statistics including sampling distributions, hypothesis testing
          • How is this course structured?

            The Introduction to Data Science course is comprised of content and curriculum to support one project. We estimate that students can complete the program in 1 month.

            The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.

          • How long is this course?

            Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.

          • Can I switch my start date? Can I get a refund?

            Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.

          • What software and versions will I need in this course?

            You’ll need access to the Internet, and a 64 bit computer. Additional software: need to be able to download and run Python 3.7.

          Introduction to Data Science

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