What are MOOCs?

How do I register?

To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.

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.

» Browse more Data Science courses

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2 weeks ago

by
**completed** this course, spending **15 hours** a week on it and found the course difficulty to be **medium**.

Summary of Course Review:

I was fairly satisfied with the course as an introduction to the math for data science specifically and given there aren't many math courses built specifically for an introduction to Data Science provided by a credible University and professor, I'm glad this one exists as I suspect many of the concepts studied will come up again in my studies of data science.

Course Intended Audience:

Note that this course is specifically targeted at business persons working with data scientists and people who lack the math background who would like an introduction to the math in data science so as such the explanations of the math are left fairly high level which is fair enough for its target audience. To put things in context, I am a non-math person looking to study data science so the detail level of this course feels fairly basic.

Key Criticisms of Course:

The firs three weeks are fairly easy to follow but on

Read more
I was fairly satisfied with the course as an introduction to the math for data science specifically and given there aren't many math courses built specifically for an introduction to Data Science provided by a credible University and professor, I'm glad this one exists as I suspect many of the concepts studied will come up again in my studies of data science.

Course Intended Audience:

Note that this course is specifically targeted at business persons working with data scientists and people who lack the math background who would like an introduction to the math in data science so as such the explanations of the math are left fairly high level which is fair enough for its target audience. To put things in context, I am a non-math person looking to study data science so the detail level of this course feels fairly basic.

Key Criticisms of Course:

The firs three weeks are fairly easy to follow but on

Summary of Course Review:

I was fairly satisfied with the course as an introduction to the math for data science specifically and given there aren't many math courses built specifically for an introduction to Data Science provided by a credible University and professor, I'm glad this one exists as I suspect many of the concepts studied will come up again in my studies of data science.

Course Intended Audience:

Note that this course is specifically targeted at business persons working with data scientists and people who lack the math background who would like an introduction to the math in data science so as such the explanations of the math are left fairly high level which is fair enough for its target audience. To put things in context, I am a non-math person looking to study data science so the detail level of this course feels fairly basic.

Key Criticisms of Course:

The firs three weeks are fairly easy to follow but once we get into Week 4, the material leading into Bayes' Theorem, Bayes' Theorem and Binomial Theorem were quite hard to follow and understand.

This is in comparison to how perhaps someone like Sal Khan from Khan Academy or Kalid Asad from Better Explained would explain how "M choose N" works etc. Basically the entire week 4 had me asking why? or how? which required further explanations from the above two sources.

E.g. Bayes' Theorem (Part 2) the formula to updating probabilities is not explained but rather just provided which is a bit hard to follow.

I do note that the entire Bayes equation for inverse probability is brought together quite nicely at the end of Bayes' Theorem (Part 2) but it would have been good to perhaps hint that the last part of part 1 and the entire part 2 would be brought together at the end of part 2.

E.g.2 Bayes' Theorem (Part 1) the final equation to solve for Urn 1 given Event 3 Whites in a row had two conversions first using sum rule, then product rule which would be completely new to students at this point, it would have been handy if Dr. Egger reminded of these two key conversions and went through it step by step. I found myself going back to the earlier topics and having to break it down on my own which took more time.

Key Strengths of Course:

Provides a credible source of math foundations for Data Science in a messy Internet world that is easy for math beginners to understand.

Video companion PDFs are extremely very useful in scanning the material each week before hand and for revision.

Dr. Egger makes many of the topics in the four weeks easy to understand and showcases them in context to data science.

Areas of Possible Growth:

If the programme provided ideas of where students could go next in their data science studies that would be helpful.

Some supplementary videos on how key methods are used in actual data science outside of the examples provided.

Changes to Course in 2018:

A previous reviewer noted the 'second lecturer' may have been less easy to follow, doing this course in Jan 2018, it appears to be entirely run by Dr. Daniel Egger now.

I was fairly satisfied with the course as an introduction to the math for data science specifically and given there aren't many math courses built specifically for an introduction to Data Science provided by a credible University and professor, I'm glad this one exists as I suspect many of the concepts studied will come up again in my studies of data science.

Course Intended Audience:

Note that this course is specifically targeted at business persons working with data scientists and people who lack the math background who would like an introduction to the math in data science so as such the explanations of the math are left fairly high level which is fair enough for its target audience. To put things in context, I am a non-math person looking to study data science so the detail level of this course feels fairly basic.

Key Criticisms of Course:

The firs three weeks are fairly easy to follow but once we get into Week 4, the material leading into Bayes' Theorem, Bayes' Theorem and Binomial Theorem were quite hard to follow and understand.

This is in comparison to how perhaps someone like Sal Khan from Khan Academy or Kalid Asad from Better Explained would explain how "M choose N" works etc. Basically the entire week 4 had me asking why? or how? which required further explanations from the above two sources.

E.g. Bayes' Theorem (Part 2) the formula to updating probabilities is not explained but rather just provided which is a bit hard to follow.

I do note that the entire Bayes equation for inverse probability is brought together quite nicely at the end of Bayes' Theorem (Part 2) but it would have been good to perhaps hint that the last part of part 1 and the entire part 2 would be brought together at the end of part 2.

E.g.2 Bayes' Theorem (Part 1) the final equation to solve for Urn 1 given Event 3 Whites in a row had two conversions first using sum rule, then product rule which would be completely new to students at this point, it would have been handy if Dr. Egger reminded of these two key conversions and went through it step by step. I found myself going back to the earlier topics and having to break it down on my own which took more time.

Key Strengths of Course:

Provides a credible source of math foundations for Data Science in a messy Internet world that is easy for math beginners to understand.

Video companion PDFs are extremely very useful in scanning the material each week before hand and for revision.

Dr. Egger makes many of the topics in the four weeks easy to understand and showcases them in context to data science.

Areas of Possible Growth:

If the programme provided ideas of where students could go next in their data science studies that would be helpful.

Some supplementary videos on how key methods are used in actual data science outside of the examples provided.

Changes to Course in 2018:

A previous reviewer noted the 'second lecturer' may have been less easy to follow, doing this course in Jan 2018, it appears to be entirely run by Dr. Daniel Egger now.

Was this review helpful to you?
Yes

3 months ago

by
**partially completed** this course, spending **2 hours** a week on it and found the course difficulty to be **medium**.

The beginning of the course was great, the first teacher explained everything slowly and thoroughly - even trivial basics, but it seems necessary in this kind of the course.

The second teacher on the other hand was explaining thing in complicated way assuming you understand everything at first sight, juggeling quickly with formulas and not explaining the background of what is happening in the formula.

The second teacher on the other hand was explaining thing in complicated way assuming you understand everything at first sight, juggeling quickly with formulas and not explaining the background of what is happening in the formula.

Was this review helpful to you?
Yes

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