OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.

Computing for Data Analysis

2.5 Filled star Filled star Half star Empty star Empty star
Based on 6 ratings
see reviews
Richard (Rich) Vuduc

The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.

The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.

Read more

The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.

The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.

In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. Together, they form the foundations of numerical and data-intensive computing.

The hands-on component of this course will develop your proficiency with modern analytical tools. You will learn how to mash up Python, R, and SQL through Jupyter notebooks, among other tools. Furthermore, you will apply these tools to a variety of real-world datasets, thereby strengthening your ability to translate principles into practice.

Enroll now

What's inside

Learning objective

The goal of this course, part of the analytics: essential tools and methods micromasters program, is for you to learn how to build these components and connect them using modern tools and techniques.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops the modern data analysis pipeline: collection, preprocessing, storage, analysis, and interactive visualization of data
Suitable for those starting an Analytics or Data Science career
No explicit prerequisites, however, it's recommended learners have a high school level math background and some basic programming knowledge
Hands-on component is learner-paced and self-directed through Jupyter Notebooks, strengthening understanding of Python, R, and SQL
Underscores the mathematical foundation of data analysis, namely numerical linear algebra and numerical optimization
Part of the Analytics: Essential Tools and Methods MicroMasters program

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Foundation in data analysis computing tools

According to learners, this course provides a solid foundation in the computational aspects of data analysis, integrating multiple tools like Python, R, and SQL. Many found the hands-on assignments and projects particularly valuable for applying concepts. While the course content is generally seen as comprehensive, covering necessary mathematical and algorithmic backgrounds, some students noted the difficulty and demanding nature, particularly in combining the different tools and handling the theoretical depth. Reviewers often recommend having a strong prerequisite background in math or programming to succeed.
Covers Python, R, and SQL together.
"I really appreciated learning how Python, R, and SQL can be used together in the same workflow."
"The integration of different programming languages and tools like Jupyter notebooks was a key takeaway for me."
"Understanding how to connect Python, R, and SQL was a core strength of the course."
Solid background in underlying math/algorithms.
"The course does a good job of explaining the 'why' behind the tools by covering linear algebra and optimization basics."
"Understanding the numerical computing foundations was crucial for me, and the course delivered here."
"Provides a deeper look under the hood than many other intro courses, which is valuable."
Assignments and labs are practical.
"The labs and assignments were very practical and helped solidify the concepts taught in the lectures."
"Working with real-world datasets in the projects made the learning feel applicable immediately."
"The practical exercises were the most beneficial part for me to gain proficiency."
Requires prior math/programming background.
"Without a decent background in calculus and linear algebra, some parts will be quite difficult."
"I recommend having some programming experience before starting this course; it's not for absolute beginners."
"Success in the course heavily depends on your comfort level with mathematical concepts and coding."
Course material is demanding and requires effort.
"This course was definitely challenging, requiring a significant time commitment."
"I found the pace quite fast, especially when diving into the mathematical underpinnings."
"Be prepared for a steep learning curve if you don't have a strong background in computing or math."

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 Computing for Data Analysis with these activities:
Connect with Experienced Data Analysts
Expand your knowledge and gain valuable insights by connecting with experienced data analysts who can provide guidance, support, and industry perspectives.
Show steps
  • Identify potential mentors
  • Reach out and introduce yourself
  • Set up regular meetings or communication
Hands-on Python Programming for Data Analysis
Develop fluency in using Python for data analysis by practicing data manipulation, wrangling, and cleaning techniques, enhancing your ability to work with real-world datasets.
Browse courses on Python Programming
Show steps
  • Install and set up Python
  • Learn fundamental Python data structures
  • Practice data manipulation and cleaning
  • Apply Python for data analysis tasks
Practice Data Visualization with Jupyter Notebooks
Enhance your data visualization skills by using Jupyter Notebooks to create interactive graphs, charts, and dashboards, strengthening your ability to communicate insights from data.
Browse courses on Data Visualization
Show steps
  • Create a Jupyter notebook
  • Import and explore your data
  • Create basic data visualizations
  • Customize and enhance your visualizations
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build Your Own Linear Regression Model
Gain practical experience in building and using linear regression models to understand relationships between variables and improve your analytical skills.
Browse courses on Linear Regression
Show steps
  • Gather and prepare your data
  • Choose appropriate independent and dependent variables
  • Fit a linear regression model to your data
  • Interpret the results of your model
Develop a Data Storytelling Presentation
Enhance your communication skills by creating a presentation that effectively communicates data insights to a non-technical audience, improving your ability to convey complex technical information clearly and persuasively.
Browse courses on Data Storytelling
Show steps
  • Gather and analyze your data
  • Craft a compelling narrative
  • Design visually appealing slides
  • Practice and refine your presentation
Explore Advanced Machine Learning Algorithms
Expand your knowledge of machine learning by exploring advanced algorithms such as support vector machines, decision trees, and ensemble methods, enhancing your ability to handle complex data analysis challenges.
Browse courses on Machine Learning
Show steps
  • Identify suitable algorithms for your data
  • Learn the underlying concepts and theory
  • Implement the algorithms using Python or R
  • Evaluate and compare algorithm performance
Attend a Data Analytics Hackathon
Put your skills to the test and collaborate with others in a competitive environment, solving real-world data analysis challenges and broadening your perspective on data-driven solutions.
Browse courses on Data Analytics
Show steps
  • Find and register for a suitable hackathon
  • Form a team or work individually
  • Gather and analyze data
  • Develop and implement a solution
  • Present your results and get feedback

Career center

Learners who complete Computing for Data Analysis will develop knowledge and skills that may be useful to these careers:
Data Analyst
A Data Analyst is an expert in aggregating and interpreting large datasets using a variety of analytical techniques, including mathematical modeling and statistical analysis. With the help of Computing for Data Analysis, you will develop a strong foundation in the tools and techniques used by Data Analysts, including Python, R, and SQL. The hands-on component of this course will also give you the opportunity to apply these tools to real-world datasets, building your confidence in using them to solve business problems.
See salaries and explore the career path for Data Analyst
Data Scientist
Data Scientists use their knowledge of data analysis and machine learning to extract insights from data that can be used to improve business decision-making. Computing for Data Analysis will provide you with a strong foundation in the tools and techniques used by Data Scientists, and the hands-on component of the course will give you the opportunity to apply these tools to real-world datasets. Completing this course will help prepare you for a career as a Data Scientist.
See salaries and explore the career path for Data Scientist
Statistician
Statisticians collect, analyze, interpret, and present data. They use their knowledge of statistical methods to draw conclusions about the world around them. Computing for Data Analysis will provide you with a strong foundation in the statistical methods used by Statisticians. The hands-on component of the course will also give you the opportunity to apply these methods to real-world datasets, building your confidence in using them to solve real-world problems.
See salaries and explore the career path for Statistician
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning systems. Machine learning systems use data to learn how to perform tasks, such as recognizing objects in images or translating languages. Computing for Data Analysis will provide you with a strong foundation in the machine learning algorithms used by Machine Learning Engineers. The hands-on component of the course will also give you the opportunity to apply these algorithms to real-world datasets, building your confidence in using them to solve business problems.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of computer science to solve problems and create new solutions. Computing for Data Analysis will provide you with a strong foundation in the computer science concepts used by Software Engineers, including data structures, algorithms, and software design. The hands-on component of the course will also give you the opportunity to apply these concepts to real-world projects, building your confidence in using them to solve real-world problems.
See salaries and explore the career path for Software Engineer
Business Analyst
Business Analysts use their knowledge of business and data analysis to help organizations improve their performance. They use data to identify problems and opportunities, and then develop and implement solutions. Computing for Data Analysis will provide you with a strong foundation in the data analysis techniques used by Business Analysts. The hands-on component of the course will also give you the opportunity to apply these techniques to real-world datasets, building your confidence in using them to solve business problems.
See salaries and explore the career path for Business Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and data analysis to help organizations make investment decisions. They use data to identify and evaluate investment opportunities, and then make recommendations to their clients. Computing for Data Analysis will provide you with a strong foundation in the data analysis techniques used by Financial Analysts. The hands-on component of the course will also give you the opportunity to apply these techniques to real-world datasets, building your confidence in using them to solve financial problems.
See salaries and explore the career path for Financial Analyst
Market Researcher
Market Researchers use their knowledge of marketing and data analysis to help organizations understand their customers. They use data to identify and understand customer needs and wants, and then develop and implement marketing strategies. Computing for Data Analysis will provide you with a strong foundation in the data analysis techniques used by Market Researchers. The hands-on component of the course will also give you the opportunity to apply these techniques to real-world datasets, building your confidence in using them to solve marketing problems.
See salaries and explore the career path for Market Researcher
Health Data Analyst
Health Data Analysts use their knowledge of healthcare and data analysis to help improve the quality and efficiency of healthcare delivery. They use data to identify and understand healthcare trends, and then develop and implement solutions to improve patient care. Computing for Data Analysis will provide you with a strong foundation in the data analysis techniques used by Health Data Analysts. The hands-on component of the course will also give you the opportunity to apply these techniques to real-world datasets, building your confidence in using them to solve healthcare problems.
See salaries and explore the career path for Health Data Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics and data analysis to help investment firms make investment decisions. They use data to develop and test financial models, and then make recommendations to their clients. Computing for Data Analysis will provide you with a strong foundation in the data analysis techniques used by Quantitative Analysts. The hands-on component of the course will also give you the opportunity to apply these techniques to real-world datasets, building your confidence in using them to solve financial problems.
See salaries and explore the career path for Quantitative Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and data analysis to help organizations improve their operations. They use data to identify and understand operational problems, and then develop and implement solutions to improve efficiency. Computing for Data Analysis will provide you with a strong foundation in the data analysis techniques used by Operations Research Analysts. The hands-on component of the course will also give you the opportunity to apply these techniques to real-world datasets, building your confidence in using them to solve operational problems.
See salaries and explore the career path for Operations Research Analyst
Data Engineer
Data Engineers design, build, and maintain the infrastructure that stores and processes data. They use their knowledge of computer science and data engineering to ensure that data is reliable, secure, and accessible. Computing for Data Analysis will provide you with a strong foundation in the data engineering concepts used by Data Engineers. The hands-on component of the course will also give you the opportunity to apply these concepts to real-world projects, building your confidence in using them to solve real-world problems.
See salaries and explore the career path for Data Engineer
Database Administrator
Database Administrators design, build, and maintain databases. They use their knowledge of database management systems to ensure that databases are reliable, secure, and accessible. Computing for Data Analysis will provide you with a strong foundation in the database management concepts used by Database Administrators. The hands-on component of the course will also give you the opportunity to apply these concepts to real-world projects, building your confidence in using them to solve real-world problems.
See salaries and explore the career path for Database Administrator
Information Security Analyst
Information Security Analysts design, implement, and maintain security measures to protect organizations from cyber attacks. They use their knowledge of information security to identify and mitigate security risks. Computing for Data Analysis may be useful for this role, as it will provide you with a strong foundation in the data analysis techniques used to identify and investigate cyber attacks.
See salaries and explore the career path for Information Security Analyst
Computer Scientist
Computer Scientists research, design, and develop computer systems and applications. They use their knowledge of computer science to solve problems and create new solutions. Computing for Data Analysis may be useful for this role, as it will provide you with a strong foundation in the computer science concepts used to design and develop computer systems and applications.
See salaries and explore the career path for Computer Scientist

Featured in The Course Notes

This course is mentioned in our blog, The Course Notes. Read one article that features Computing for Data Analysis:
25 Free Online Programming Courses for Beginners - 2017
4 minute read

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Computing for Data Analysis.
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning, covering both the basics and advanced techniques.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive overview of statistical learning, covering both the basics and advanced techniques.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Data Mining
Save
Provides a comprehensive overview of data mining concepts and techniques, covering both the basics and advanced techniques.
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Data Mining: Concepts and Techniques (The Morgan...
Hardcover
$$$
Data Mining: Concepts and Techniques, Second...
Hardcover
$$
Data Mining: Know It All
Hardcover
$$$
Cover image
Python for Data Analysis
Save
Provides a comprehensive guide to data analysis with Python, covering both the basics and advanced techniques.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive overview of statistical learning, covering both the basics and advanced techniques.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning, covering both the basics and advanced techniques.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Data Science for Business
Save
Introduces key concepts and tools in data analysis and provides guidance for applying them in business contexts.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
R for Data Science
Save
Provides a practical introduction to data analysis with R, covering both the basics and advanced techniques.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Numerical Methods in Software and Analysis
Save
Provides a comprehensive overview of mathematical statistics and data analysis, covering both the basics and advanced techniques.
Numerical Methods in Software and Analysis
Paperback
$$$
Numerical Methods in Software and Analysis
Kindle Edition
$$$
Cover image
Python for Data Analysis
Save
Provides a hands-on introduction to data analysis with Python, using the popular Pandas library.
Python for Data Analysis: Data Wrangling with...
Paperback
$$
Python for Data Analysis
Kindle Edition
$$
Python for Data Analysis: Data Wrangling with...
Paperback
$$$
Cover image
Doing Data Science
Save
Provides a comprehensive guide to data analysis with SQL, covering both the basics and advanced techniques.
Doing Data Science: Straight Talk from the Frontline
Paperback
$$
(中文) 数据科学实战 (图灵程序设计丛书) (Chinese Edition)
Kindle Edition
$
Cover image
Data Visualization with JavaScript
Save
Provides a practical guide to data visualization, emphasizing the importance of choosing the right chart for the right story.
Data Visualization with JavaScript
Kindle Edition
$$
Cover image
Data Visualization
Save
Provides a practical introduction to data visualization, covering both the basics and advanced techniques.
Data Visualization: A Successful Design Process: A...
Paperback
$
Data Visualization: Representing Information on...
Kindle Edition
$$$
Data Visualization Design and Utilization (Korean...
Paperback
$$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Similar courses are unavailable at this time. Please try again later.
Rating
2.5 Filled star Filled star Half star Empty star Empty star
Effort
8 to 10 hours per week for 15 weeks
Level
Advanced
Via
edX
Institution
The Georgia Institute of Technology
Instructor
Richard (Rich) Vuduc
Sessions
August 17, 2026 - January 1, 2027 January 5, 2026 - May 10, 2026 August 18, 2025 - January 1, 2026 January 6, 2025 - May 10, 2025 August 19, 2024 - January 1, 2025
Language
English
Transcript
English
This course belongs to a collection called Analytics: Essential Tools and Methods. It's comprised of three courses:
  1. Introduction to Analytics Modeling
  2. Computing for Data Analysis (viewing)
  3. Data Analytics for Business
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Develops the modern data analysis pipeline: collection, preprocessing, storage, analysis, and interactive visualization of data
Suitable for those starting an Analytics or Data Science career
No explicit prerequisites, however, it's recommended learners have a high school level math background and some basic programming knowledge
Hands-on component is learner-paced and self-directed through Jupyter Notebooks, strengthening understanding of Python, R, and SQL
Underscores the mathematical foundation of data analysis, namely numerical linear algebra and numerical optimization
Part of the Analytics: Essential Tools and Methods MicroMasters program
Featured in The Course Notes
Read one article that features this course, Computing for Data Analysis from our blog, The Course Notes:
25 Free Online Programming Courses for Beginners - 2017
4 minute read
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Computing for Data Analysis.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser