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edX: Statistics and Probability in Data Science using Python

 with  Alon Orlitsky

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL. 

2 Student
reviews
Cost Free Online Course
Subject Data Science
Provider edX
Language English
Certificates $350 Certificate Available
Hours 8-10 hours a week
Calendar 15 weeks long

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Reviews for edX's Statistics and Probability in Data Science using Python
2.5 Based on 2 reviews

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3.0 4 weeks ago
Anonymous partially completed this course.
I have received a passing grade on the course and finished 9 out of 10 weeks now on the verified track (10th week is not available now due to a delay, which is very typical for majority of the weeks on first iteration of this class).

I have to say the course is quite comprehensive and both broad and deep in probability and statistics. However, in my believe it is too theoretical rather than practical.

In order to pass the course (and this is consistent with a ton of feedback on the learners forums on the verified track) - one needs to spend 20-30 hours per week, which is far more than 10 hours indicated on the course page. The staff/TA support is almost non-existent, my valid issue about auto-grader bug for one of the programming assignments was never addressed or acknowledged after being raised multiple times. The programming assignments are extremely time consuming (often due to non-clearly explained task or unnecessarily complex issue requiring additional r
Read more
I have received a passing grade on the course and finished 9 out of 10 weeks now on the verified track (10th week is not available now due to a delay, which is very typical for majority of the weeks on first iteration of this class).

I have to say the course is quite comprehensive and both broad and deep in probability and statistics. However, in my believe it is too theoretical rather than practical.

In order to pass the course (and this is consistent with a ton of feedback on the learners forums on the verified track) - one needs to spend 20-30 hours per week, which is far more than 10 hours indicated on the course page. The staff/TA support is almost non-existent, my valid issue about auto-grader bug for one of the programming assignments was never addressed or acknowledged after being raised multiple times. The programming assignments are extremely time consuming (often due to non-clearly explained task or unnecessarily complex issue requiring additional research in probability concept proofs), typically not related to anything discussed on the lessons and the auto-grader only expects 1 specific way of approaching the problems (which is not discussed on the task itself, so if you approach the problem differently, and get correct result while spending long hours on that - you will not pass and no one will assist you, as the staff seems to not care).

I believe the main benefit of online learning is in possibility of getting instant feedback. In this case teaching staff is not supportive, forum questions are answered very rarely by TA's and in most cases never, emails to provided email address are never answered. This costs USD 350, I am sure one can find better options to learn statistics & probability for that money while putting in smaller level of time commitment (one don't necessarily need to know all of the proofs of all of the theorems for the applied field of data science, while that sort of boring academic style of teaching is prevalent on the course). Programming aspect of the course is very rarely brought up in the lessons (
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2.0 2 months ago
by Ronny De Winter partially completed this course, spending 7 hours a week on it and found the course difficulty to be medium.
This review was written after finishing 7 of the 10 sessions.

The schedule is unreliable, with many delays (3 weeks of delay after 7 weeks). This is a boring course, too many mathematical proofs, with long-winded videos and dull scholarly examples with coins, dices and cards. The slides and the python notebooks that are discussed in the videos are not always available for download.

The assignments are not practical and engaging enough. No interactions on the discussion forum, and absent TAs. The videos and assignments contain mistakes, what is worse is that these mistakes are not corrected by the staff when people report about them on the discussion forum.

Only 30 people have passed the 50% midpoint grade after 60% of the course completion, you can hardly call this a MOOC! This course does not seem to be able to attract the Massive audience to give it a MOOC status, despite the so popular topic. Food for thought for the instructors: MOOCs need
Read more
This review was written after finishing 7 of the 10 sessions.

The schedule is unreliable, with many delays (3 weeks of delay after 7 weeks). This is a boring course, too many mathematical proofs, with long-winded videos and dull scholarly examples with coins, dices and cards. The slides and the python notebooks that are discussed in the videos are not always available for download.

The assignments are not practical and engaging enough. No interactions on the discussion forum, and absent TAs. The videos and assignments contain mistakes, what is worse is that these mistakes are not corrected by the staff when people report about them on the discussion forum.

Only 30 people have passed the 50% midpoint grade after 60% of the course completion, you can hardly call this a MOOC! This course does not seem to be able to attract the Massive audience to give it a MOOC status, despite the so popular topic. Food for thought for the instructors: MOOCs need a completely different approach than classical classroom training (different audience, different teaching techniques, other types of student interactions, ...)

It is petty because the first course of this MicroMasters was quite ok.

The course still uses python 2.7 while the first course in this micromasters uses python3, today there is no valid reason anymore to stick to python 2, python 3 is the norm.

For the other courses in this Micromasters, watch class-central.com for sufficient amounts of positive reviews.

There must be better ways to spend your time and money (with 350$ this MOOC is far too expensive) to acquire probability and statistics skills. The world has better courses on this hot topic. You'll sure find them here on class central.

From a disappointed lifelong learner with more than 50 finished MOOCs on his counter.
Was this review helpful to you? Yes
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