Data Science as a Field from Coursera

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.

What's inside

Syllabus

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.

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.

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.

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores data science's origins, current standing, and future potential. This provides historical and industry context for learners

Strengthens foundational skills in data analysis and reproducibility. These are fundamental skills for data scientists at any level

Covers topics used in data science across industry, government, and academia. This gives learners a multi-faceted understanding of data science in the real world

Emphasizes effective communication of results. This is a valuable skill for data scientists who need to convey insights to various stakeholders

Features videos and discussions that add variety to learning. This diversification can be beneficial for learners with different preferences

Part of an interdisciplinary data science degree program. This may help learners connect with others in different fields

Reviews summary

Engaging data science overview

Learners say Data Science as a Field is a great introduction to its namesake. Hands-on practice is a strong feature of this course students highly value. Many students feel this course is well-organized and includes informative videos. Students also like that they are able to learn from guest speakers within the field. Students say that while R programming knowledge isn't a prerequisite, familiarity with the software is very helpful. Overall, reviewers are very positive about this course and its practical approach to learning about data science.

Guest speakers from reputable organizations share their industry experiences and insights.

"Good course, with broad overview and several specific discussions from people working in the field."

"The class fails to teach most of what it asks for the assignments."

"real world examples and professionals from companies as well-known as Google."

Practical exercises and assignments are a key feature of this course and are highly valued by students.

"Exercises and lectures were hands-on and informative."

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

"got good knowledge of R."

Some students felt the course was well-organized while others felt it was somewhat disorganized.

"Good course, with broad overview and several specific discussions from people working in the field."

"In my opinion the course is a little bit desorganize."

"While the content of this course is interesting, it feels a bit disorganized."

Familiarity with R prior to taking this course is very helpful.

"It looked easy, but it isn't easy for anyone who is new to R programming or programming in general."

"just a little bit more support ("go here to install R", "here's a quick tutorial on tidyverse") would make it much easier to reach the level necessary for understanding the lectures and project."

"While the content of this course is interesting, it feels a bit disorganized."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science as a Field with these activities:

Mathematics and Statistics Review

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Fill in any gaps in your math and statistics knowledge before starting the course.

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Review basic concepts such as calculus, linear algebra, and probability.
Work through practice problems to test your understanding.
Seek help from a tutor or online resources if needed.

Coding Challenges

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Sharpen your programming and problem-solving skills in a data science context.

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Find a coding challenge website, such as LeetCode or HackerRank.
Select a challenge that aligns with your skill level.
Solve the challenge using your coding skills and knowledge of data science concepts.
Review your solution and identify areas for improvement.

Kaggle for Data Science

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Push past learning fundamentals and test your skills with hands-on datasets and guided tutorials.

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Sign up for Kaggle and explore the website.
Find a beginner-friendly competition and join.
Follow the guided tutorials and work through the challenges.
Submit your results and compare them to others.

Five other activities

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Attend Data Science Meetups

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Connect with professionals in the field and expand your knowledge network.

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Find local or virtual data science meetups using Meetup.com or other platforms.
Attend meetups and introduce yourself to others.
Engage in discussions and ask questions to learn from experienced data scientists.
Exchange contact information with potential mentors or collaborators.

Study Group Formation

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Collaborate with classmates to reinforce concepts and work through problems together.

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Reach out to classmates and form a study group.
Meet regularly to discuss course material, work on assignments, and prepare for exams.
Take turns leading discussions and presenting concepts to the group.
Provide feedback and support to each other.

Data Cleaning and Visualization Project

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Apply data science techniques to a real-world dataset, solidifying your understanding.

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Find a dataset that interests you and download it.
Clean and prepare the data for analysis.
Visualize the data using charts, graphs, or dashboards.
Write a report summarizing your findings and insights.

An Introduction to Statistical Learning

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Gain a deep dive into statistical learning beyond course materials.

View An Introduction to Statistical Learning: with... on Amazon

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Contribute to Open-Source Data Science Projects

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Gain hands-on experience and contribute to the data science community.

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Identify open-source data science projects on platforms like GitHub.
Select a project that aligns with your interests and skill level.
Review the project documentation and contribute code or documentation based on the project's needs.
Engage with the project community and seek feedback on your contributions.
Attend online or in-person events related to the open-source project.

Career center

Learners who complete Data Science as a Field will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists provide expertise in acquiring, modeling, and interpreting large volumes of data. With the recent boom in data-driven decision-making, Data Scientists are in high demand. This course, which focuses on introducing aspiring Data Scientists to the field, will help build a foundation of fundamental concepts and skills that will be critical to a Data Scientist's success.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use their skills with data management, statistics, and visualization to analyze data and extract meaningful insights, which can be used to improve a wide range of business processes and outcomes. This course, with its emphasis on data analysis and interpretation, may be useful to an aspiring Data Analyst.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers are responsible for building, training, and deploying machine learning models. This course's coverage of the data science process and pitfalls may be useful to Machine Learning Engineers, as they need to be able to understand and identify potential issues in their models.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to understand business needs and drive decision-making. This course, with its focus on data analysis and interpretation, can help build a foundation of skills for aspiring Business Analysts.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers build and maintain the infrastructure that stores and processes data. This course's coverage of the data science process and pitfalls may be useful to Data Engineers, as they need to understand the different stages of data analysis and the potential issues that can arise at each stage.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians apply statistical methods to collect, analyze, interpret, and present data. This course's coverage of data analysis and interpretation may be useful to aspiring Statisticians, who need to be able to understand and use statistical methods to draw meaningful conclusions from data.

See salaries and explore the career path for Statistician

Information Scientist

Information Scientists research and develop new information technologies. This course's coverage of data management and storage may be useful to aspiring Information Scientists.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course's coverage of data management and storage may be useful to Software Engineers who work on data-intensive systems.

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Database Administrator

Database Administrators manage and maintain databases, ensuring that data is stored, organized, and accessible. This course's coverage of data management and storage may be useful to aspiring Database Administrators.

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Computer Scientist

Computer Scientists research and develop new computing technologies. This course's coverage of data management and storage may be useful to Computer Scientists who work on data-intensive computing.

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Financial Analyst

Financial Analysts use data to analyze and forecast financial trends. This course's coverage of data analysis and interpretation may be useful to aspiring Financial Analysts.

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Data Visualization Specialist

Data Visualization Specialists create visual representations of data to communicate insights and trends. This course's coverage of data analysis and interpretation may be useful to aspiring Data Visualization Specialists.

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Operations Research Analyst

Operations Research Analysts use data to analyze and improve business operations. This course's coverage of data analysis and interpretation may be useful to aspiring Operations Research Analysts.

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Market Researcher

Market Researchers conduct research to understand consumer behavior and market trends. This course's coverage of data analysis and interpretation may be useful to Market Researchers.

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Risk Analyst

Risk Analysts use data to analyze and manage risk. This course's coverage of data analysis and interpretation may be useful to aspiring Risk Analysts.

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