Course

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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  • DAYS
  • HRS
  • MIN
  • SEC
  • Estimated Time
    20 hours

  • Enroll by
    September 22, 2021

    Get access to classroom immediately on enrollment

  • Prerequisites
    Python, SQL, Statistics, Machine Learning
In collaboration with
  • Bertelsmann
  • Appen
  • IBM Watson
  • Insight
  • Kaggle
  • Starbucks

What You Will Learn

Syllabus

Introduction to Data Science

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.

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.

  • 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.

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Data Scientist roles are growing by 45% year over year!

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Introducing new Udacity Single Courses

Our students asked and we listened. You can now get the in-demand tech skills you need faster and for less money by enrolling in one of our new, one-month Single Courses. You’ll get the specific job-ready skills you need in as little as four weeks and for a fraction of the cost.

Of course if you are looking for a more robust, in-depth education, you can still enroll in one of our 3-6 month Nanodegree programs.

Both programs are part-time and online, and they both offer 24/7 support, quality Udacity-produced content, courses created with the help of top tech companies, and more. You can always start with a Single Course and upgrade to a full Nanodegree program if you like.

All Our Courses Include

Real-world projects from industry experts

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.
Technical mentor support

Technical mentor support

Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track.
Workspaces

Workspaces to see your code in action

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

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 OfferingsFull list of offerings included:
Enrollment Includes:
Class Content
Real-world projects
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Project reviews
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Project feedback from experienced reviewers
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Student Services
Technical mentor support
New
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Student community
Improved
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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
Reviews By the numbers
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Reviewer Services
  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
Mentors available to answer your questions
Mentors by the numbers
1,400+ technical mentors
0.85 hours median response time
Mentorship Services
  • Support for all your technical questions
  • Questions answered quickly by our team of technical mentors

Learn with the best

Josh Bernhard
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
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 Serrano

Instructor

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 Paster

Instructor

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
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
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
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

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    • Maximum flexibility to learn at your own pace.
    • Cancel anytime.
  • Learn

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

    On average, successful students take 20 hours to complete this program.
  • Benefits include

    • Real-world projects from industry experts
    • Technical mentor 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.
    Python:
    • 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 20 hours.

    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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