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
This course is part of Vital Skills for Data Science Specialization
Instructor: Jane Wall
5,702 already enrolled
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(35 reviews)
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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 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 videos3 readings2 discussion prompts
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
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 quiz1 peer review1 discussion prompt
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
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Recommended if you're interested in Data Analysis
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
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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.
Reviewed on Dec 12, 2024
It is very good course. Thank you very much professor.
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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:
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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.
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