Name: Data Science in Real Life
Brand: Johns Hopkins University
Availability: OnlineOnly
Rating: 4.5 (288 reviews)

返回到 Data Science in Real Life

学生对 Johns Hopkins University 提供的 Data Science in Real Life 的评价和反馈

4.5

星

2,376 个评分

课程概述

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...

突出显示

Statistics review

（44 条评论）

276 - Data Science in Real Life 的 289 个评论（共 289 个）

创建者 Marcelo H G

•

Jul 29, 2017

It is good but demands statistics and some knowledge in research area.

创建者 Julià D A

•

Jun 13, 2017

Too qualitative, I would had liked some hands-on examples.

创建者 Shafeeq I

•

Jan 8, 2019

Not that engaging content.Too much theoretical approach.

创建者 Peter P

•

Jun 20, 2016

Too much focus on technicalities - not management based.

创建者 Hiteshwar G

•

Jan 5, 2018

The content and examples seem irrelevant.

创建者 Varun M

•

Sep 19, 2016

very boring videos.

创建者 GIacomo V

•

Feb 28, 2016

The course tests are at times partially unrelated to the content of the lessons. In the test of Lesson 7 we are asked if removing jargon from an analysis makes the analysis clearer. This is never mentioned in the course.

The question does not have a unique yes/no solution. It depends on the context, in particular on the audience of the analysis and report. If I'm talking to technical people who knows a lot about the topic jargon can be useful, on the other hand if jargon is not documented it can be confusing.

How are we supposed to know this?

This is just one example, but all the courses of the EDS specialisation had these issues. I don't know if it is a language barrier or what but I feel that I didn't have a chance to study more to get a better score. You either happen to have the same idea of the teacher or you don't, and this is not professional.

创建者 Kevin K

•

Jun 12, 2020

The course content is good, but there were no instructions how to complete the capstone in order to obtain a certifiate. This was really disappointing after completing the course work. Eventually, I just stopped my subscription.