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Learner Reviews & Feedback for Data Science in Real Life by Johns Hopkins University

4.4
stars
1,672 ratings
190 reviews

About the 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

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.

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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101 - 125 of 189 Reviews for Data Science in Real Life

By ellen w

Aug 07, 2017

Excellent!

By Pablo A L

Feb 08, 2016

Marvelous!

By Wladimir R

Sep 30, 2018

Excellent

By Ahmed T

Apr 25, 2017

Excellent

By Chander W

Nov 10, 2019

Amazing

By Hector R C C

Mar 17, 2019

thanks!

By Bauyrzhan S

Jun 13, 2018

Perfect

By ALAA A A

Jan 11, 2018

good

By Augustina R

Dec 30, 2016

Some of the material here was repeated from other courses but overall I felt this was my favorite course in the series. I particularly appreciated the real life examples of what can go wrong with data collection and suggestions/best practices for how to handle that. It gave me a lot of ideas for how to deal with some uncertainties I was facing in some of my own research.

By Clifton d L

Dec 06, 2017

Great that the messy reality is acknowledged and not only the perfect theoretical data science is explained, but also the things that usually go wrong (and how to mitigate these issues).

Some of the quiz with "check multiple answers" didn't seem clear to me / I found opinionated.

By Suman C

Mar 05, 2018

Expected few more real life examples and hope to see some basics of Formal modelling. Found myself lacking in understanding the formal modelling concepts and how to arrive at the formulas.

Other than that the course helped me to get started in Data Science.

By Keuntae K

Mar 25, 2018

This is a good course, overall. Maybe providing more general examples related to the topics of the course makes this course much more useful and helpful for people who do not have any backgrounds of brain or neural systems in medical science like me.

By Humna A

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 Neil N

Feb 17, 2019

Good overview of the reality of the challenges in data science. A glaring miss from my perspective was any real focus on the challenges of ML/AI based analysis. This module was really focused on traditional statistical modeling

By Scott K

Oct 11, 2015

I really enjoyed the comparison of what is ideal vs. what actually happens when it comes to data science. This was a very practical course and gave insight into what to expect from data science and analysis.

By Yani

Oct 27, 2016

Dr.Caffo is really well-versed with his field but I feel like concepts should be given more concrete examples so that they seem more interesting. Respect him all the way!

By Nishant J

Mar 05, 2018

Examples used in this course are related to Lifescience and candidates like me find it difficult to correlate. It would be beneficial to use some common life examples.

By Kian G L

Aug 13, 2016

Is good to have some data science background to enroll in this course, overall still good to learn and get the hint of how real life data scientist life is.

By JOSEPH A

May 09, 2018

Good course - I'm now confident to oversee an end-to-end data science experiment. Some interactivity would make this the perfect overview of data science.

By Reginald D F

Dec 23, 2017

I like that this course examples the many ways an experiment/analysis can go wrong and how to address these issues. Very practical for the practitioner.

By Siddharth T

Apr 03, 2016

Again a course with depth in content but the presentation of the course could improve , it seems a bit patchy and pre-reads would help.

By Karthik S N

May 01, 2016

Good concepts - apply to anyone new to data science.

Lot of good 'read further' links and materials. Learnt a lot.

By Andrew W

Nov 02, 2017

Great examples and explanations of real cases, very helpful for general understanding of concepts.

By Boris L

Oct 05, 2015

Very nice overview of what can go wrong in a data science project and what to pay attention to.

By Udaypal S N

Nov 25, 2017

Need more focus on other industries like Telecom, Banking, Manufacturing, Semi-Conductor, etc.