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Data Science as a Field

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Gain insight into a topic and learn the fundamentals.
4.3

39 reviews

Intermediate level

Recommended experience

Flexible schedule
1 week at 10 hours a week
Learn at your own pace
Build toward a degree

Gain insight into a topic and learn the fundamentals.
4.3

39 reviews

Intermediate level

Recommended experience

Flexible schedule
1 week at 10 hours a week
Learn at your own pace
Build toward a degree

What you'll learn

  • By taking this course, you will be able explain what data science is and identify the key disciplines involved.

  • You will be able to use the steps of the data science process to create a reproducible data analysis and identify personal biases.

  • You will be able to identify interesting data science applications, locate jobs in Data Science, and begin developing a professional network.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

Build your subject-matter expertise

This course is part of the Vital Skills for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

This week we will talk about the past, present and future of data science. The growth of data science has been fueled by the growth of the internet, social media and online shopping as well as by the rapid increases in data storage capabilities. You will watch several short videos and participate in discussions about the future of data science.

What's included

4 videos4 readings2 discussion prompts

4 videosTotal 15 minutes
  • Data Science as a Field Course Introduction 3 minutes
  • Where Does Data Science Come From?3 minutes
  • The Current State of the Field7 minutes
  • Where is Data Science Going?2 minutes
4 readingsTotal 31 minutes
  • Course Updates and Accessibility Support1 minute
  • Earn Academic Credit for your Work!10 minutes
  • Course Support10 minutes
  • Assessment Expectations10 minutes
2 discussion promptsTotal 20 minutes
  • Introduce Yourself!10 minutes
  • Data Science and Privacy Concerns10 minutes

This week you will watch videos and have a reading on some applications of data science in industry and academia. You will hear from data scientists in different fields to find out how they use data science.

What's included

8 videos7 readings1 peer review3 discussion prompts

8 videosTotal 97 minutes
  • Introduction to "Data Science in Business, Industry, and the Professional World"1 minute
  • Brian Brown & Rinaldo Maldera16 minutes
  • Natalie Jackson12 minutes
  • Vilja Hulden16 minutes
  • Robin Burke10 minutes
  • Seth Spielman17 minutes
  • Katharina Kann15 minutes
  • Dan Larremore10 minutes
7 readingsTotal 70 minutes
  • Introducing Brian Brown and Rinaldo Maldera10 minutes
  • Introducing Natalie Jackson10 minutes
  • Introducing Vilja Hulden10 minutes
  • Introducing Robin Burke10 minutes
  • Introducing Seth Spielman10 minutes
  • Introducing Katharina Kann10 minutes
  • Introducing Dan Larremore10 minutes
1 peer reviewTotal 60 minutes
  • Data Science in Industry, Government, and Academia60 minutes
3 discussion promptsTotal 30 minutes
  • Applications of Data Science10 minutes
  • Data Science at AirBnB10 minutes
  • Application Areas and Skills10 minutes

This week you will learn about the importance of reproducibility and how to achieve it, learn the steps in a data analysis process and learn about the possible pitfalls in data science. You will watch demonstrating the various steps in the data science process and try out these processes for yourself on a different dataset.

What's included

11 videos9 readings1 assignment1 peer review1 discussion prompt

11 videosTotal 64 minutes
  • Importance and Process of Reproducibility5 minutes
  • Knit to PDF4 minutes
  • Intro to R Markdown8 minutes
  • Overview of Steps in the Data Science Process2 minutes
  • Importing Data6 minutes
  • Tidying and Transforming Data8 minutes
  • Visualizing Data6 minutes
  • Analyzing Data8 minutes
  • Modeling Data6 minutes
  • Bias Sources4 minutes
  • Intro to Data Ethics Course with Bobby Schnabel6 minutes
9 readingsTotal 90 minutes
  • Before You Watch The Next Video...10 minutes
  • Knit the Template10 minutes
  • Use R Markdown to Create a Document10 minutes
  • For More Info On Tidyverse Packages...10 minutes
  • Project Files10 minutes
  • Project Step 1: Start an Rmd Document10 minutes
  • Project Step 2: Tidy and Transform Your Data10 minutes
  • Project Step 3: Add Visualizations and Analysis10 minutes
  • Project Step 4: Add Bias Identification10 minutes
1 assignmentTotal 1 minute
  • File Unlocking Quiz1 minute
1 peer reviewTotal 60 minutes
  • NYPD Shooting Incident Data Report60 minutes
1 discussion promptTotal 10 minutes
  • Reproducibility10 minutes

This week you will learn about important ways of communicating your results. We will discuss the important things to know about presentations and reports. You will also learn about the importance of networking and try it out.

What's included

2 videos1 reading1 peer review3 discussion prompts

2 videosTotal 8 minutes
  • Do’s and Don’ts for Good Reports and Presentations5 minutes
  • CU Boulder’s MS in Data Science: Where to Go from Here?3 minutes
1 readingTotal 10 minutes
  • Imposter Syndrome10 minutes
1 peer reviewTotal 60 minutes
  • Communicating your Results60 minutes
3 discussion promptsTotal 30 minutes
  • Elevator Pitch10 minutes
  • Attend a Meetup10 minutes
  • Imposter Syndrome10 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Instructor ratings
4.2 (18 ratings)
University of Colorado Boulder
6 Courses40,405 learners

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Showing 3 of 39

XX
·

Reviewed on Aug 16, 2021

G​ood course for exercise R programming, data analysis and communication skills.

MW
·

Reviewed on Jun 25, 2021

Very practical overview of the field. Does require knowledge of R to do 2 simple projects if you take it for credit.

MM
·

Reviewed on Jun 2, 2021

A great introduction to Data Science, with plenty of practical assignments that are flexible enough to explore our own questions of interest.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,