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Johns Hopkins University

Data Science in Real Life

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

Status: Data Analysis
Status: Statistical Analysis
Course7 hours

Featured reviews

LW

4.0Reviewed Aug 20, 2020

Slightly difficult for non data science background people, but is manageable to have a dip into this course and stimulate a "real life" experiences shared by course insructor.

KL

4.0Reviewed Aug 12, 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.

PF

5.0Reviewed Feb 11, 2018

Another excellent Executive Data Science course. Brian gives clear and concise explanations of the ideal versus real world of the data science workplace.

AK

4.0Reviewed Apr 18, 2020

It was kind of hard to understand as I did not have any professional experience in data science. But, I am sure I can work in a professional environment now with the teachings of the professor.

ML

4.0Reviewed Oct 26, 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!

ES

5.0Reviewed Nov 11, 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.

MJ

4.0Reviewed Jan 18, 2021

Gives directions on how to deal with a situation where a clear conclusion may not be forthcoming from the analysis--- a situation that more often than not is likely to hold true in real world

JA

4.0Reviewed May 8, 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.

AG

5.0Reviewed Nov 13, 2016

An amazing course for those who are not very familiar with statistics and a very refreshing perspective for those who actually knows statistics!

NJ

4.0Reviewed Mar 4, 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.

RF

4.0Reviewed Dec 22, 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.

SM

5.0Reviewed Aug 19, 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.

All reviews

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