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Introduction to Data Science

Course

Learn the data science process, including how to build effective data visualizations, and how to communicate with various stakeholders.

Learn the data science process, including how to build effective data visualizations, and how to communicate with various stakeholders.

Built in collaboration with

IBM

Advanced

3 weeks

Real-world Projects

Completion Certificate

Last Updated January 16, 2024

Skills you'll learn:
Blog posts • Predictive analytics • Data storytelling • AI algorithms in Python
Prerequisites:
Predictive analytics • scikit-learn • NumPy

Course Lessons

Lesson 1

Welcome to the Course

This lesson will give you an overview of the course, discuss pre-requisites and stakeholders.

Lesson 2

The Data Science Process

In this lesson, you will learn about CRISP-DM and how you can apply it to many data science problems.

Lesson 3

Communicating to Stakeholders

In this lesson, you will be creating a post to communicate your findings via Medium.

Lesson 4 • Project

Project: Writing a Data Scientist Blog Post

In this project, you will create a blog post and Github repository that you can use as you build your data science portfolio.

Taught By The Best

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Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

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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.

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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.

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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.

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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.

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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.

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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.

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

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Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.

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