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Data Science as a Field
  • Level Professional
  • Duration 11 hours
  • Course by University of Colorado Boulder
  • Offered by

About

This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Modules

Course Overview

    • 1 Videos
    • 3 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Introduce Yourself!

1 Videos

  • Data Science as a Field Course Introduction

3 Readings

  • Earn Academic Credit for your Work!
  • Course Support
  • Assessment Expectations

What is Data Science?

    • 3 Videos
Show info about module content

3 Videos

  • Where Does Data Science Come From?
  • The Current State of the Field
  • Where is Data Science Going?

Week 1 Assessment

    • 1 Discussion
Show info about module content

1 Discussions

  • Data Science and Privacy Concerns

Week 2 Overview

    • 1 Videos
    • 1 Discussion
Show info about module content

1 Discussions

  • Applications of Data Science

1 Videos

  • Introduction to "Data Science in Business, Industry, and the Professional World"

Data Science in Industry and Government

    • 2 Videos
    • 2 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Data Science at AirBnB

2 Videos

  • Brian Brown & Rinaldo Maldera
  • Natalie Jackson

2 Readings

  • Introducing Brian Brown and Rinaldo Maldera
  • Introducing Natalie Jackson

Data Science in Academia

    • 5 Videos
    • 5 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Application Areas and Skills

5 Videos

  • Vilja Hulden
  • Robin Burke
  • Seth Spielman
  • Katharina Kann
  • Dan Larremore

5 Readings

  • Introducing Vilja Hulden
  • Introducing Robin Burke
  • Introducing Seth Spielman
  • Introducing Katharina Kann
  • Introducing Dan Larremore

Week 2 Assessment

    • 1 PeerReview
Show info about module content

1 Peer Review

  • Data Science in Industry, Government, and Academia

Reproducibility

    • 3 Videos
    • 3 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Reproducibility

3 Videos

  • Importance and Process of Reproducibility
  • Knit to PDF
  • Intro to R Markdown

3 Readings

  • Before You Watch The Next Video...
  • Knit the Template
  • Use R Markdown to Create a Document

Steps in the Data Science Process through Data Cleaning

    • 3 Videos
    • 4 Readings
    • 1 Quiz
Show info about module content

3 Videos

  • Overview of Steps in the Data Science Process
  • Importing Data
  • Tidying and Transforming Data

4 Readings

  • For More Info On Tidyverse Packages...
  • Project Files
  • Project Step 1: Start an Rmd Document
  • Project Step 2: Tidy and Transform Your Data

1 Quiz

  • File Unlocking Quiz

Visualizing, Analyzing, and Modeling Data

    • 3 Videos
    • 1 Readings
Show info about module content

3 Videos

  • Visualizing Data
  • Analyzing Data
  • Modeling Data

1 Readings

  • Project Step 3: Add Visualizations and Analysis

Pitfalls

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Bias sources
  • Intro to Data Ethics course with Bobby Schnabel

1 Readings

  • Project Step 4: Add Bias Identification

Submit Your Project

    • 1 PeerReview
Show info about module content

1 Peer Review

  • NYPD Shooting Incident Data Report

Communicating Your Results

    • 1 Videos
    • 1 Discussion
Show info about module content

1 Discussions

  • Elevator Pitch

1 Videos

  • Do’s and Don’ts for Good Reports and Presentations

Course Conclusion

    • 1 Videos
    • 1 Readings
    • 2 Discussion
Show info about module content

2 Discussions

  • Attend a Meetup
  • Imposter Syndrome

1 Videos

  • CU Boulder’s MS in Data Science: Where to Go from Here?

1 Readings

  • Imposter Syndrome

Week 4 Assessment

    • 1 PeerReview
Show info about module content

1 Peer Review

  • Communicating your Results

Auto Summary

Explore the exciting world of Data Science with this comprehensive course designed for aspiring data scientists and professionals. Created by CU Boulder and industry experts, it covers essential skills, processes, and practices in both professional and academic settings. Part of the MS-DS degree on Coursera, this 660-hour course offers flexible, performance-based admissions. Ideal for individuals with diverse backgrounds in computer science, mathematics, and statistics. Join now to advance your career in Big Data and Analytics!

Jane Wall
Instructor

Jane Wall