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Introduction to Analytics Modeling

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Based on 5 ratings
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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.

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.
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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

Traffic lights

Read about what's good
what should give you pause
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

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Reviews summary

Introduction to analytics modeling

According to learners, this course serves as a solid introduction to analytical modeling techniques. Many students found the lectures clear and well-structured, offering a good overview of key concepts. The hands-on exercises in R were frequently highlighted as a major strength, helping learners apply theoretical knowledge effectively. However, some reviewers noted that the course assumes a certain level of prior knowledge in R and statistics, which could be a challenge for complete beginners without that background. The pace was generally considered appropriate for an introductory course. Overall, it is viewed as a valuable foundational course for further study in data analytics.
Lectures are well-structured and easy to follow.
"The instructor explained the concepts very clearly, making complex ideas accessible."
"Video lectures were high quality and easy to digest."
"I appreciated how the lectures broke down difficult topics into manageable parts."
"Lectures were concise and easy to understand."
Provides a good foundational overview of analytics.
"Excellent overview of analytics modeling for someone starting out."
"Provides a solid base to build upon for more advanced topics."
"I felt like I got a good grasp of the fundamental ideas and methods."
"This course provided me with a strong foundation in using models for data analysis."
Practical exercises in R are highly valuable.
"The R labs were the most beneficial part of the course, really helped me grasp the concepts."
"Practicing with R examples provided was key to understanding the models."
"I found the practical exercises in R very helpful for applying the methods taught."
"The hands-on coding and projects are the strongest part of the course for me."
Some topics could benefit from deeper coverage.
"It's a good intro, but don't expect deep dives into complex algorithms."
"While it covers many topics, the explanation of validation techniques felt a bit rushed."
"Could use more in-depth coverage on complex topics or optimization techniques."
"Some subjects were covered too quickly to fully grasp without external resources."
Course assumes some background in R or statistics.
"Be warned, this course is not for absolute beginners in R. You need some familiarity."
"Coming in with no stats background made some parts quite challenging."
"I wish they had been clearer about the prerequisite level for R proficiency."
"It assumes you already know R programming basics."

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
Refresh prior knowledge and prepare for course content.
Browse courses on Statistical Concepts
Show steps
  • Review probability distributions
  • Practice hypothesis testing
Review linear algebra
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
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
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
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
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
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
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
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

Career center

Learners who complete Introduction to Analytics Modeling will develop knowledge and skills that may be useful to these careers:
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.
See salaries and explore the career path for Data Analyst
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Machine Learning Engineer
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.
See salaries and explore the career path for Operations Research 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.
See salaries and explore the career path for Quantitative Analyst
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.
See salaries and explore the career path for Actuary
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.
See salaries and explore the career path for Statistician
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.
See salaries and explore the career path for Data Engineer
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.
See salaries and explore the career path for Business 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.
See salaries and explore the career path for Risk 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.
See salaries and explore the career path for Fraud 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.
See salaries and explore the career path for Financial Analyst
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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Product Manager
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.
See salaries and explore the career path for Marketing Analyst

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.
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive introduction to statistical learning methods, including supervised and unsupervised learning, and valuable reference for both beginners and experienced practitioners.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive and advanced treatment of statistical learning methods, and valuable reference for those with a strong foundation in statistics and mathematics.
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
$$$
Cover image
Tidy Modeling with R
Save
Provides a practical guide to building predictive models using R, and is particularly useful for those interested in applying machine learning techniques to real-world problems.
Tidy Modeling with R
Paperback
Check
Tidy Modeling with R
Kindle Edition
Check
Pattern Recognition and Machine Learning
Save
Provides a comprehensive and theoretical treatment of pattern recognition and machine learning algorithms, and valuable reference for those with a strong foundation in mathematics and computer science.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Probabilistic Machine Learning
Save
Provides a comprehensive and probabilistic treatment of machine learning algorithms, and valuable reference for those with a strong foundation in probability and statistics.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Multivariate Statistical Modelling Based on...
Save
Provides a comprehensive and intermediate-level treatment of statistical methods for data analysis, and valuable reference for those with a foundation in statistics.
Multivariate Statistical Modelling Based on...
Paperback
Check
Multivariate Statistical Modelling Based on...
Kindle Edition
Check
Data Mining
Save
Provides a comprehensive and advanced treatment of data mining concepts and techniques, and valuable reference for those with a strong foundation in computer science and statistics.
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Data Mining: Concepts and Techniques (The Morgan...
Hardcover
$$$
Data Mining: Concepts and Techniques, Second...
Hardcover
$$
Data Mining: Know It All
Hardcover
$$$
Cover image
Machine Learning Q and AI
Save
Provides a practical and hands-on introduction to machine learning using Python, and is particularly useful for those interested in implementing models in Python.
Machine Learning Q and AI: 30 Essential Questions...
Paperback
$$
Machine Learning Q and AI: 30 Essential Questions...
Kindle Edition
$$
Cover image
Deep Learning
Save
Provides a comprehensive and advanced treatment of deep learning algorithms and techniques, and valuable reference for those with a strong foundation in mathematics and computer science.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Reinforcement Learning, second edition
Save
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.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Bayesian Data Analysis, Third Edition
Save
Provides a comprehensive and advanced treatment of Bayesian data analysis methods, and valuable reference for those with a strong foundation in statistics and mathematics.
Bayesian Data Analysis (Chapman & Hall/CRC Texts in...
Kindle Edition
$$$$
Cover image
Causal Inference
Save
Provides a comprehensive and advanced treatment of causal inference methods, and valuable reference for those with a strong foundation in statistics and mathematics.
Causal Inference
Paperback
Check
Causal Inference
Kindle Edition
Check
Cover image
Statistical Rethinking
Save
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.
Statistical Rethinking
Paperback
Check
Statistical Rethinking
Kindle Edition
Check

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Course image
Rating
4.4 Filled star Filled star Filled star Filled star Empty star
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
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 (viewing)
  2. Computing for Data Analysis
  3. Data Analytics for Business
Traffic lights
Read about what's good
what should give you pause
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
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