This course is part of the Vital Skills for Data Science Specialization

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Jane Wall

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Part of the Master of Science in Data Science degree

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Vital Skills for Data Science SpecializationUniversity of Colorado Boulder

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

Vital Skills for Data Science Specialization

Intermediate Level

Knowledge of R required and knowledge of tidyverse is helpful.

Approx. 11 hours to complete

English

Subtitles: English

What you will 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.

Skills you will gain

Data Science
Applied Mathematics
Information Science
Statistics
Computer Science

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

Vital Skills for Data Science Specialization

Intermediate Level

Knowledge of R required and knowledge of tidyverse is helpful.

Approx. 11 hours to complete

English

Subtitles: English

Instructor

Instructor rating

4.37/5 (9 Ratings)

Jane Wall

Faculty Director of Data Science Programs

Data Science Programs

4,590 Learners

7 Courses

Offered by

University of Colorado Boulder

CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week

Week 1

1 hour to complete

Introduction to Data Science: the Past, Present, and Future of a New Discipline

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.

1 hour to complete

4 videos (Total 15 min), 2 readings

Week

Week 2

4 hours to complete

Data Science in Industry, Government, and Academia

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.

4 hours to complete

8 videos (Total 97 min), 7 readings, 1 quiz

8 videos

Introduction to "Data Science in Business, Industry, and the Professional World"1m

Brian Brown & Rinaldo Maldera16m

Natalie Jackson11m

Vilja Hulden16m

Robin Burke9m

Seth Spielman16m

Katharina Kann15m

Dan Larremore10m

7 readings

Introducing Brian Brown and Rinaldo Maldera10m

Introducing Natalie Jackson10m

Introducing Vilja Hulden10m

Introducing Robin Burke10m

Introducing Seth Spielman10m

Introducing Katharina Kann10m

Introducing Dan Larremore10m

Week

Week 3

4 hours to complete

Data Science Process and Pitfalls

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.

4 hours to complete

11 videos (Total 64 min), 9 readings, 2 quizzes

11 videos

Importance and Process of Reproducibility4m

Knit to PDF3m

Intro to R Markdown8m

Overview of Steps in the Data Science Process2m

Importing Data6m

Tidying and Transforming Data8m

Visualizing Data6m

Analyzing Data7m

Modeling Data5m

Bias sources4m

Intro to Data Ethics course with Bobby Schnabel5m

9 readings

Before You Watch The Next Video...10m

Knit the Template10m

Use R Markdown to Create a Document10m

For More Info On Tidyverse Packages...10m

Project Files10m

Project Step 1: Start an Rmd Document10m

Project Step 2: Tidy and Transform Your Data10m

Project Step 3: Add Visualizations and Analysis10m

Project Step 4: Add Bias Identification10m

1 practice exercise

File Unlocking Quiz1m

Week

Week 4

2 hours to complete

Communicating Your Results

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.

2 hours to complete

2 videos (Total 8 min), 1 reading, 1 quiz

Reviews

4.5

8 reviews

5 stars
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4 stars
6.66%
3 stars
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1 star
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TOP REVIEWS FROM DATA SCIENCE AS A FIELD

by MWJun 25, 2021

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

by AHJun 13, 2021

Exercises and lectures were hands-on and informative. I enjoyed the practical advice on data science as a profession.

by XXAug 16, 2021

Good course for exercise R programming, data analysis and communication skills.

by MMJun 2, 2021

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

View all reviews

About the Vital Skills for Data Science Specialization

Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss.

This specialization 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. Logo image by JJ Ying on Unsplash

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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.
What will I get if I subscribe to this Specialization?
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. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
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.

More questions? Visit the Learner Help Center.

Data Science as a Field