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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.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/cwnqah/computing
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.
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.
Good to know
Good to know
Know what's good ,
what to watch for , 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
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Reviews summary
Middling computing for data analysis
According to students, Computing for Data Analysis is a middling course with both positive and negative qualities. Learners generally found the lectures to be of good quality. However, support from instructors and TAs appears to be insufficient, making this course more suited for students with programming experience who can self-teach.
Learners often mention good quality lectures.
"For the most part, this was an excellent course."
"The lectures were well done, and I learned some valuable information."
This course relies heavily on Jupyter Notebooks for hands-on learning.
"This course offers very high quality Jupyter Notebooks."
"Spent most of my time reading the 3 paragraph question, trying to decipher what was even being asked."
"The notebooks are written in the most convoluted and unclear way possible."
Unfortunately, support from instructors and teaching assistants may be insufficient.
"Course support was not sufficient."
"I got no response."
"The instructor and the TAs don't monitor the discussion forums."
Exams may be difficult and include technical issues with the exam platform.
"The exam questions were programming problems that had some overlap with the homework exercises."
"In some cases, the required data files were not uploaded to Vocareum."
"The final exam the Vocareum system crashed."
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Connect with Experienced Data Analysts
Show steps
Expand your knowledge and gain valuable insights by connecting with experienced data analysts who can provide guidance, support, and industry perspectives.
Show steps
Show steps
Show steps
- Identify potential mentors
- Reach out and introduce yourself
- Set up regular meetings or communication
Hands-on Python Programming for Data Analysis
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
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.
Data Scientist
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.
Statistician
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.
Machine Learning Engineer
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.
Health Data Analyst
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.
Business Analyst
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.
Operations Research 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.
Software 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.
Financial 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.
Quantitative 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.
Market Researcher
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.
Database Administrator
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.
Data Engineer
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.
Information Security Analyst
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.
Computer Scientist
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.
For more career information including salaries, visit:
OpenCourser.com/course/cwnqah/computing
Featured in The Course Notes
This course is mentioned in our blog,
The Course Notes. Read
one article that features
Computing for Data Analysis:
To read more articles from OpenCourser, visit:
OpenCourser.com/notes
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.
Provides a comprehensive overview of pattern recognition and machine learning, covering both the basics and advanced techniques.
Provides a comprehensive overview of statistical learning, covering both the basics and advanced techniques.
Save
Provides a comprehensive overview of data mining concepts and techniques, covering both the basics and advanced techniques.
Provides a comprehensive guide to data analysis with Python, covering both the basics and advanced techniques.
Provides a comprehensive overview of statistical learning, covering both the basics and advanced techniques.
Save
Provides a comprehensive overview of deep learning, covering both the basics and advanced techniques.
Introduces key concepts and tools in data analysis and provides guidance for applying them in business contexts.
Save
Provides a practical introduction to data analysis with R, covering both the basics and advanced techniques.
Provides a comprehensive overview of mathematical statistics and data analysis, covering both the basics and advanced techniques.
Provides a hands-on introduction to data analysis with Python, using the popular Pandas library.
Save
Provides a comprehensive guide to data analysis with SQL, covering both the basics and advanced techniques.
Provides a practical guide to data visualization, emphasizing the importance of choosing the right chart for the right story.
Save
Provides a practical introduction to data visualization, covering both the basics and advanced techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cwnqah/computing
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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
January 8, 2024
- May 12, 2024
Language
English
Transcript
English
This course belongs to a
collection
called
Analytics: Essential Tools and Methods. It's comprised of three courses:
- Introduction to Analytics Modeling
- Computing for Data Analysis (viewing)
- Data Analytics for Business
Good to know
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:
To read more articles from OpenCourser, visit:
OpenCourser.com/notes
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