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edX: Introduction to Analytics Modeling

 with  Joel Sokol

Please note that the verified certificate option for this course is limited to 250 learners. The verified certificate option will close when this limit is reached.

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.
2 Student
reviews
Cost Free Online Course
Subject Data Science
Provider edX
Language English
Hours 5-10 hours a week
Calendar 10 weeks long
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What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
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How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

2 reviews for edX's Introduction to Analytics Modeling

Write a review
3 months ago
Spalthoff Daniel completed this course, spending 20 hours a week on it and found the course difficulty to be very hard.
I just finished this course as a verified learner. The content is excellent (5 stars), but for two reasons my overall evaluation of the course is a 4 stars: This is the first time this course is offered via edX, and there were many technical glitches. But course staff was very responsive, and I assume that these probl Read More
I just finished this course as a verified learner. The content is excellent (5 stars), but for two reasons my overall evaluation of the course is a 4 stars:

This is the first time this course is offered via edX, and there were many technical glitches. But course staff was very responsive, and I assume that these problems will be fixed in future versions of the course.

The course announcement wasn't clear about required knowledge and skills to take the course. Most homework requires proficiency in R.

A positive point was the very active Slack group of students, which helped a lot. (The edX forum, on the other hand, was difficult to use.)

Bottom line is, this course is extremely interesting, one of the best I have ever taken, very ambitious with the topics covered. You will enjoy the course if you have basic proficiency in R.
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3 months ago
Leo Steeghs audited this course, spending 10 hours a week on it and found the course difficulty to be hard.
Very interesting for somebody new to statistics and R programmering like me. Nice teaching, homework is quite tough.. overall very good experience!
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