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

4.4
1,545 ratings
185 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 184 Reviews for Data Science in Real Life

By jose c

Jun 11, 2019

Claridad del contenido entregado del curso

By Carlos A H

Jun 30, 2019

Excellent overview of implementing practical data science; however, an area of improvement is emphasizing machine learning as a practical solution for finding answers especially with large and complex data sets.

By Fayez A S

Jul 07, 2019

It opened new understanding for me. Loved every bit of it.

By Paulo B M d S

Jul 08, 2019

The authors really present real situation and challenges that data scientists face in their daily activities. Very good.

By Manuel E

Jul 08, 2019

Good review of everything that can go wrong… and eventually will.

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 Gustavo V

Apr 14, 2019

Help me understand what can I expect from a real data science project.

By Nachum S

Jul 13, 2018

Good, a bit long for the material.

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 SANTOSH K R

Jan 07, 2017

More real world examples are required

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 Paulose B

Oct 31, 2016

Short session need more handson excercise

By Setia B

Dec 07, 2017

I really enjoyed the course :)

By Paul C

Nov 04, 2016

A solid course with lots of practical advice.

By Warren L

May 05, 2017

Appreciated the anecdotes as they allowed me to remember the learnings in context

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 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 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 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 TCHUENTE D

Oct 19, 2016

good course, but focus more on inferential analysis than predictive analysis

By Andrew W

Nov 02, 2017

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

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 Deepak G

Jun 28, 2016

Quality of this course is better than the rest of the specialization.

By Udaypal S N

Nov 25, 2017

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

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.