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Joel Sokol
Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.
In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.
Read more
Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.
In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.
Read more
Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.
In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.
You’ll learn about analytics modeling and how to choose the right approach from among the wide range of options in your toolbox.
You will learn how to use statistical models and machine learning as well as models for:
- classification;
- clustering;
- change detection;
- data smoothing;
- validation;
- prediction;
- optimization;
- experimentation;
- decision making.
Register for this course and see more details by visiting:
OpenCourser.com/course/gj91x5/introduction
What's inside
Learning objectives
- Fundamental analytics models and methods
- How to use analytics software, including r, to implement various types of models
- Understanding of when to apply specific analytics models
- Fundamental analytics models and methods
- How to use analytics software, including r, to implement various types of models
- Understanding of when to apply specific analytics models
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores analytical models, which are essential in data analysis, predictions, and business decisions
Taught by Joel Sokol, who is recognized for their work in analytics
Examines a wide range of analytics models, including statistical models, machine learning, and models for various applications
Develops skills in using industry-standard tools like R for implementing analytics models
Provides a strong foundation for beginners in analytics modeling
Explicitly requires students to come in with extensive background knowledge first
Save this course
Save Introduction to Analytics Modeling to your list so you can find it easily later:
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Reviews summary
Analytics modeling fundamentals
Learners say Introduction to Analytics Modeling is a well-executed course with excellent content that serves as a solid foundation in the field. While its introductory nature means many advanced topics are just touched on, the practical homework assignments and supportive TA sessions make this course a standout, especially for those with some coding experience.
Course is a solid foundation in analytics modeling.
"It was pretty challenging, it's an introductory course in that the professor touches on many different topics to give you a feel for the techniques needed to solve many types of problems, and the common terms and tools used."
"Since the material alone is challenging though, and it would have been too much if it was my first encounter with R/python/machine learning."
Very responsive TAs and active messaging board.
"While there were some technical difficulties at times but they were easily compensated by the ever responsive TAs, messaging board, office-hours etc."
Practical, real-world homework assignments.
"Definitely the stand out to me was the combination of homework, all problems with practical applications"
"That by itself makes the notes for this class incredibly useful, and something that I'll refer to and build upon as I encounter these topics in other classes."
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 Introduction to Analytics Modeling with these
activities:
Review statistical concepts
Show steps
Refresh prior knowledge and prepare for course content.
Browse courses on
Statistical Concepts
Show steps
-
Review probability distributions
-
Practice hypothesis testing
Review linear algebra
Show steps
Strengthen foundational understanding for course concepts.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of linear algebra
-
Solve practice problems
Read about model selection and validation
Show steps
Establish a foundational understanding of the theory and methodology behind the approaches taught in this course.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read chapters 7 and 8 of Introduction to Statistical Learning
-
Complete the corresponding exercises in the book
Six other activities
Expand to see all activities and additional details
Show all nine activities
Guided tutorials on R for data analytics
Show steps
Gain proficiency with the tools used in this course to put theory into practice.
Browse courses on
R Programming
Show steps
-
Follow the RStudio tutorials on data manipulation and visualization
-
Complete the exercises in the tutorials
Practice data analysis in R
Show steps
Build proficiency in using R for data analysis.
Browse courses on
R Programming
Show steps
-
Find datasets online and import them into R
-
Perform exploratory data analysis and visualization
-
Fit statistical models to the data
Peer discussion on model selection
Show steps
Enhance understanding through discussion and exchange of ideas.
Browse courses on
Model Selection
Show steps
-
Join a study group or online forum
-
Discuss different model selection criteria and techniques
-
Share experiences and insights
Create a data analysis project
Show steps
Apply knowledge and skills to a real-world problem.
Browse courses on
Data Analysis
Show steps
-
Identify a problem or question to solve
-
Gather and prepare data
-
Analyze the data using R
-
Present the results and insights
Volunteer at a data analytics organization
Show steps
Gain practical experience and contribute to the community.
Browse courses on
Data Analytics
Show steps
-
Find a data analytics organization
-
Inquire about volunteer opportunities
-
Assist with data collection, analysis, or visualization
Contribute to an open-source data analytics project
Show steps
Gain experience collaborating and contributing to real-world projects.
Browse courses on
Data Analytics
Show steps
-
Find an open-source data analytics project
-
Identify areas where you can contribute
-
Submit a pull request with your contributions
Review statistical concepts
Show steps
Refresh prior knowledge and prepare for course content.
Browse courses on
Statistical Concepts
Show steps
Show steps
Show steps
- Review probability distributions
- Practice hypothesis testing
Review linear algebra
Show steps
Strengthen foundational understanding for course concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of linear algebra
- Solve practice problems
Read about model selection and validation
Show steps
Establish a foundational understanding of the theory and methodology behind the approaches taught in this course.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 7 and 8 of Introduction to Statistical Learning
- Complete the corresponding exercises in the book
Six other activities
Expand to see all activities and additional details
Show all nine activities
Guided tutorials on R for data analytics
Show steps
Gain proficiency with the tools used in this course to put theory into practice.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Follow the RStudio tutorials on data manipulation and visualization
- Complete the exercises in the tutorials
Practice data analysis in R
Show steps
Build proficiency in using R for data analysis.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Find datasets online and import them into R
- Perform exploratory data analysis and visualization
- Fit statistical models to the data
Peer discussion on model selection
Show steps
Enhance understanding through discussion and exchange of ideas.
Browse courses on
Model Selection
Show steps
Show steps
Show steps
- Join a study group or online forum
- Discuss different model selection criteria and techniques
- Share experiences and insights
Create a data analysis project
Show steps
Apply knowledge and skills to a real-world problem.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a problem or question to solve
- Gather and prepare data
- Analyze the data using R
- Present the results and insights
Volunteer at a data analytics organization
Show steps
Gain practical experience and contribute to the community.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Find a data analytics organization
- Inquire about volunteer opportunities
- Assist with data collection, analysis, or visualization
Contribute to an open-source data analytics project
Show steps
Gain experience collaborating and contributing to real-world projects.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Find an open-source data analytics project
- Identify areas where you can contribute
- Submit a pull request with your contributions
Career center
Learners who complete Introduction to Analytics Modeling will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
In the field of data science, you will be designing new and innovative ways to use data. This course, by providing an understanding of the fundamentals of analytics, can jumpstart your path to becoming a Data Scientist. This course can help you develop the tools and techniques to effectively collect, analyze, and interpret data so as to solve a wide range of business problems. By taking this Introduction to Analytics Modeling course, you will gain an understanding of the types of models available to you as well as how and when to apply them to maximize results.
Data Analyst
Data Analyst
A Data Analyst is a professional that collects and interprets data in order to help organizations make better decisions. By developing your skills in analytics modeling, you can position yourself well for a career as a Data Analyst. This course will help you learn how to use common analytics tools such as R. It will also give you the background to know which analytics approach is best for a particular business problem.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for developing and implementing machine learning algorithms to solve real-world problems. An understanding of analytics modeling will greatly benefit your career as a Machine Learning Engineer. This course can help provide you with a background in fundamental analytics models and methods which you can use in your work.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. An understanding of analytics modeling is essential for a career as a Statistician, and this course can help you develop the skills you need.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to help organizations improve their efficiency and effectiveness. An understanding of analytics modeling is essential for a career as an Operations Research Analyst, and this course can help you develop the skills you need.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. An understanding of analytics modeling is essential for a career as a Quantitative Analyst, and this course can help you develop the skills you need.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage financial risk. An understanding of analytics modeling is essential for a career as an Actuary, and this course can help you develop the skills you need.
Fraud Analyst
Fraud Analyst
Fraud Analysts investigate and prevent fraud. An understanding of analytics modeling can help you better detect and prevent fraud, and this course can help you develop the skills you need.
Business Analyst
Business Analyst
Business Analysts are responsible for bridging the gap between technical teams and business stakeholders. With an understanding of analytics modeling, you will be better equipped to make recommendations to business stakeholders and provide them with the insights they need to make better decisions.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks to organizations. An understanding of analytics modeling can help you better quantify and manage risk, and this course can help you develop the skills you need.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. An understanding of analytics modeling can help you better design and build data systems that meet the needs of your organization. This course will help you learn how to use analytics modeling to inform your data engineering decisions.
Software Engineer
Software Engineer
Software Engineers design and develop computer applications and software systems. By taking this course, you can learn how to use analytics modeling to help you build software solutions that are more effective and efficient. This is a course that may be useful for you to increase your value as a Software Engineer.
Financial Analyst
Financial Analyst
Financial Analysts use data to make informed investment decisions. With an understanding of analytics modeling, you will be able to better analyze financial data and make more informed recommendations.
Marketing Analyst
Marketing Analyst
Marketing Analysts are responsible for analyzing marketing data to help organizations understand their customers and make better marketing decisions. An understanding of analytics modeling can be helpful for Marketing Analysts, and this course can provide you with a foundation in the fundamentals of analytics modeling.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. By understanding analytics modeling, you can better understand the needs of your customers and develop products that meet those needs. This course will help you learn how to use analytics modeling to inform your product decisions.
For more career information including salaries, visit:
OpenCourser.com/course/gj91x5/introduction
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
Introduction to Analytics Modeling.
Provides a comprehensive introduction to statistical learning methods, including supervised and unsupervised learning, and valuable reference for both beginners and experienced practitioners.
Provides a comprehensive and advanced treatment of statistical learning methods, and valuable reference for those with a strong foundation in statistics and mathematics.
Save
Provides a practical guide to building predictive models using R, and is particularly useful for those interested in applying machine learning techniques to real-world problems.
Provides a comprehensive and theoretical treatment of pattern recognition and machine learning algorithms, and valuable reference for those with a strong foundation in mathematics and computer science.
Provides a comprehensive and probabilistic treatment of machine learning algorithms, and valuable reference for those with a strong foundation in probability and statistics.
Provides a comprehensive and intermediate-level treatment of statistical methods for data analysis, and valuable reference for those with a foundation in statistics.
Save
Provides a comprehensive and advanced treatment of data mining concepts and techniques, and valuable reference for those with a strong foundation in computer science and statistics.
Provides a practical and hands-on introduction to machine learning using Python, and is particularly useful for those interested in implementing models in Python.
Save
Provides a comprehensive and advanced treatment of deep learning algorithms and techniques, and valuable reference for those with a strong foundation in mathematics and computer science.
Provides a comprehensive and advanced treatment of reinforcement learning algorithms and techniques, and valuable reference for those with a strong foundation in mathematics and computer science.
Provides a comprehensive and advanced treatment of Bayesian data analysis methods, and valuable reference for those with a strong foundation in statistics and mathematics.
Save
Provides a comprehensive and advanced treatment of causal inference methods, and valuable reference for those with a strong foundation in statistics and mathematics.
Provides a practical and hands-on introduction to Bayesian statistics using R and Stan, and is particularly useful for those interested in applying Bayesian methods to real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gj91x5/introduction
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Effort
8 to 10 hours per week for 16 weeks
Level
Advanced
Via
edX
Institution
The Georgia Institute of Technology
Instructor
Joel Sokol
Sessions
August 17, 2026
- January 1, 2027
May 11, 2026
- August 9, 2026
January 5, 2026
- May 10, 2026
August 18, 2025
- January 1, 2026
May 12, 2025
- August 10, 2025
January 6, 2025
- August 10, 2025
August 19, 2024
- January 1, 2025
May 13, 2024
- August 11, 2024
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 (viewing)
- Computing for Data Analysis
- Data Analytics for Business
Good to know
Explores analytical models, which are essential in data analysis, predictions, and business decisions
Taught by Joel Sokol, who is recognized for their work in analytics
Examines a wide range of analytics models, including statistical models, machine learning, and models for various applications
Develops skills in using industry-standard tools like R for implementing analytics models
Provides a strong foundation for beginners in analytics modeling
Explicitly requires students to come in with extensive background knowledge first
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