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Data Science in Real Life, Johns Hopkins University

4.4
1,335 ratings
157 reviews

About this Course

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses. This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager. The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include: 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb...
Highlights
Statistics review
(44 Reviews)

Top reviews

By SM

Aug 20, 2017

A very good and concise course that helps to understand the basics of the Data Science and its applications. The examples are very relevant and helps to understand the topic easily.

By ES

Nov 12, 2017

Highly educational course on the realities of data analysis. Many good tips for your own analyses as well as for managing others responsible for coherent and accurate analyses.

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156 Reviews

By ELINGUI Pascal Uriel

Nov 08, 2018

Great practice example, great team at Zillow, and that to DataCamp

By Jason Campbell

Nov 06, 2018

I found this course to be notably worse than all of the others in the series. There is very very little practical content provided within the lectures. Way too many summaries or over-views of what's to come next without really getting into the nuances of what is discussed as a course topic. Way too much repetition of the exact same content, there is even repetition of content in this course that was presented in another one of the courses in the series. Many of the examples are purely meant as a comedic aside rather than actually functioning to discuss the topic with depth. E.g. - talking about statistical modeling and putting up a picture of Ben Stiller from Zoolander - then keeping the picture up there for the entire explanation. There's literally a Nic Cage example provided for the confounding factor lecture only for the instructor to say directly after "This isn't actually the best example" - then proceeds to not explain why it was brought up aside from mentioning there's a spurious correlation. Way too much repetition of similar examples - showing photos of a muscular v. skinny Christian Bale. This pop-culturey reference isn't needed in the first place and doesn't need to be shown in triplicate. I don't mind repetition if there is additional nuance or content provided through them, but that isn't the case in this course. I find there is too much focus on side tangents, where the instructor seems to change thoughts mid-sentence but forgets to come back to the original idea. I think that every single video could be cut down by 25%, purely by being more concise, and should include more nuanced descriptions. I found it particularly odd that instrumental variables were noted as a rather clever technique, yet an explanation was intentionally avoided, however an example was still provided. Bringing up a topic, intentionally refusing to define it, then providing an example directly after just doesn't make sense. I think that more time needs to be spent refining the lectures so that they're designed to teach content. It has the feel of someone who's talking about a field to get people interested in it rather than a practical training course. Many key terms are very poorly defined with examples (on many cases the audience is referred to wikipedia for explanations) in which the basics are repetitively explained while the nuances are glossed over. There seems to be an odd theme where summaries and over-generalizations are far too frequent and yet the key terms and how they relate to examples are an afterthought. I don't think the summaries are necessary given the fact that users can literally re-watch every single video and there isn't enough total content to justify a summary in the first place. Additionally, this course also seems to deviate from the others in that there is an assumption that the student has a heavy amount of programming experience already built in (or that's my assumption since many of the term explanations aren't discussed too heavily). Prior lectures break down the basics more and indicate that potential managers should pursue the data specialization courses.

By Humna Asghar

Oct 30, 2018

Awesome course! the only negative thing is that all the examples are related to biostatistics. Examples related to other fields like economics, social science, psychology etc should have been included. Besides that it was a great experience

By Emmanuelle Marshall

Oct 10, 2018

Great course, although, if you are not already working or have knowledge in this particular filed/topic, it is challenging.

By Wladimir Rodriguez

Sep 30, 2018

Excellent

By Roque Azpurua

Sep 24, 2018

Very easy to follow with good examples. The focus on this course was on practicality and I really appreciated that

By Jocelyn Delisle

Sep 08, 2018

Excellent course! Very interesting!

By Peter Leijten

Aug 14, 2018

The course is valuable but highly focussed on scientific applications (inference) and less on business application (i.e. prediction). I hoped for a more even mix.

By Nachum Sokolic

Jul 13, 2018

Good, a bit long for the material.

By 龚子轩

Jun 28, 2018

I really enjoy this class!