Data Science as a Field
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Data Science as a Field
This course is part of Vital Skills for Data Science Specialization
Instructor: Jane Wall
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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.
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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 videos•Total 15 minutes
- Data Science as a Field Course Introduction •3 minutes
- Where Does Data Science Come From?•3 minutes
- The Current State of the Field•7 minutes
- Where is Data Science Going?•2 minutes
4 readings•Total 31 minutes
- Course Updates and Accessibility Support•1 minute
- Earn Academic Credit for your Work!•10 minutes
- Course Support•10 minutes
- Assessment Expectations•10 minutes
2 discussion prompts•Total 20 minutes
- Introduce Yourself!•10 minutes
- Data Science and Privacy Concerns•10 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 videos•Total 97 minutes
- Introduction to "Data Science in Business, Industry, and the Professional World"•1 minute
- Brian Brown & Rinaldo Maldera•16 minutes
- Natalie Jackson•12 minutes
- Vilja Hulden•16 minutes
- Robin Burke•10 minutes
- Seth Spielman•17 minutes
- Katharina Kann•15 minutes
- Dan Larremore•10 minutes
7 readings•Total 70 minutes
- Introducing Brian Brown and Rinaldo Maldera•10 minutes
- Introducing Natalie Jackson•10 minutes
- Introducing Vilja Hulden•10 minutes
- Introducing Robin Burke•10 minutes
- Introducing Seth Spielman•10 minutes
- Introducing Katharina Kann•10 minutes
- Introducing Dan Larremore•10 minutes
1 peer review•Total 60 minutes
- Data Science in Industry, Government, and Academia•60 minutes
3 discussion prompts•Total 30 minutes
- Applications of Data Science•10 minutes
- Data Science at AirBnB•10 minutes
- Application Areas and Skills•10 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 videos•Total 64 minutes
- Importance and Process of Reproducibility•5 minutes
- Knit to PDF•4 minutes
- Intro to R Markdown•8 minutes
- Overview of Steps in the Data Science Process•2 minutes
- Importing Data•6 minutes
- Tidying and Transforming Data•8 minutes
- Visualizing Data•6 minutes
- Analyzing Data•8 minutes
- Modeling Data•6 minutes
- Bias Sources•4 minutes
- Intro to Data Ethics Course with Bobby Schnabel•6 minutes
9 readings•Total 90 minutes
- Before You Watch The Next Video...•10 minutes
- Knit the Template•10 minutes
- Use R Markdown to Create a Document•10 minutes
- For More Info On Tidyverse Packages...•10 minutes
- Project Files•10 minutes
- Project Step 1: Start an Rmd Document•10 minutes
- Project Step 2: Tidy and Transform Your Data•10 minutes
- Project Step 3: Add Visualizations and Analysis•10 minutes
- Project Step 4: Add Bias Identification•10 minutes
1 assignment•Total 1 minute
- File Unlocking Quiz•1 minute
1 peer review•Total 60 minutes
- NYPD Shooting Incident Data Report•60 minutes
1 discussion prompt•Total 10 minutes
- Reproducibility•10 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 videos•Total 8 minutes
- Do’s and Don’ts for Good Reports and Presentations•5 minutes
- CU Boulder’s MS in Data Science: Where to Go from Here?•3 minutes
1 reading•Total 10 minutes
- Imposter Syndrome•10 minutes
1 peer review•Total 60 minutes
- Communicating your Results•60 minutes
3 discussion prompts•Total 30 minutes
- Elevator Pitch•10 minutes
- Attend a Meetup•10 minutes
- Imposter Syndrome•10 minutes
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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
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Reviewed on Aug 16, 2021
Good course for exercise R programming, data analysis and communication skills.
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.
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.
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