Вернуться к Managing Data Analysis

4.5

Оценки: 2,225

•

Рецензии: 293

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.
This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. 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 basic data analysis iteration
2. Identify different types of questions and translate them to specific datasets
3. Describe different types of data pulls
4. Explore datasets to determine if data are appropriate for a given question
5. Direct model building efforts in common data analyses
6. Interpret the results from common data analyses
7. Integrate statistical findings to form coherent data analysis presentations
Commitment: 1 week of study, 4-6 hours
Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD...

Основные моменты

Helpful quizzes

(рецензий: 3)Well-organized content

(рецензий: 24)Mar 01, 2017

A long course compared to others in the specialization, but a lot of great material. Very well presented, the instructors know how to present this material and make it easy to grasp and understand.

Nov 23, 2016

The course is full of the cases and the real life examples coupled with the theory background. Its very simple to understand and the course will definitely be of an value for people looking for

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автор: TCHUENTE D

•Oct 10, 2016

good course to understand keys questions and methodology to manage each step of data analysis process

автор: Jens P

•Apr 11, 2016

Excellent focus on what makes managing Data Analysis teams different from managing managing other teams. This course has the most impact if combined with general management background/classes. Speech fillers, like "um," "ah," "like," etc. prove distracting at times.

автор: Daniel C d F

•Nov 20, 2016

Great course overall. Should only explain better a few related concepts such as the p-value and confidence interval.

автор: Abhi k

•Dec 08, 2015

If you can make the assignments nearer to the real world scenario and a little more detailed explanations for some parts, it would be great!

автор: Janusz Z

•Oct 04, 2015

It was a great experience a big amount of knowledge in a short presentation. Overall the summary papers are great, however I missed a more interactive videos with more bullets points and etc.

Thank you!

автор: Anqi Z

•Aug 14, 2017

Very practical lecture. Ideal for pre-project preparation and progress control.

автор: Maybin M

•Mar 15, 2016

Very good and quick lessons on Data analysis.

Managers should appreciate this course!

автор: Seyey C

•Sep 11, 2016

Very good... very important...

Maybe too fast for a single week...

автор: Acini G T

•Nov 16, 2015

wonderful course

автор: Niels v G

•Feb 08, 2016

Nice course on what the most important

автор: George K

•Sep 16, 2017

sometime it was not easy to understand the lecturer. also, it would be good to try some things out versus reading the expamples. other than that - a great course!

автор: Debasish M

•Feb 02, 2017

Data Analysis and the technicalities of getting to the right results and presenting them in a meaningful way

автор: Christos G

•Aug 29, 2017

Very interesting insights and ideas about how to manage Data Analysis, especially the part with the communication. I think there could be some more emphasis on the troubleshooting side, as it overall appeared to be a finite, engineering process which can always end successfully if the instructions are followed closely.

автор: Cristian F

•Nov 12, 2017

The topics in the course shows that there is a set of steps to counduct a data science project since the definition of the question to solve to the apropiate way to communicate the results. The content of some videos could be considered technical.

автор: Ioannis L

•Apr 03, 2017

Some of the videos were a bit fast (too much content), but a very interesting course.

автор: Frederic B

•Aug 29, 2017

Very good as going deeper into the details.

автор: Pranjal S S

•May 20, 2017

Great course for a beginner!. Love the method of teaching and illustration by Robert Peng

автор: SATISH R

•Jun 02, 2017

excellent

автор: Lacour

•Mar 03, 2017

clear an

автор: serge a

•Dec 21, 2017

Very valuable, however, in particular the section on inference vs prediction included material not explained before and hard to follow. Also examples with t-values and interpretation of values when adding confounders was difficult to grasp.

автор: JOSEPH A

•Apr 09, 2018

Brilliant course - fantastic overview. What's lacking for 5* is the EDA exercise in R should've been within an R IDE to enable total beginners to get more hands on. After all EDA is mostly about DOING. Hope course designers fix this for the next iteration.

автор: Kevin C

•Oct 31, 2017

Well defined strategies for getting a handle on the data analysis process. Short and concise class that hit on relevant points required to be successful in this area.

автор: Christopher L

•May 01, 2018

Pretty good, but I would have liked more math. I understand that others would not, but many times one equation can cut through 3-4 paragraphs and be more clear than the text. It can be frustrating knowing that if you just had the equation things would be 100% clear, but with just a bunch of text, you just get a vague idea, for more work, ie reading time.

автор: Abhishek S

•Sep 18, 2017

The course was good but as a suggestion, walkthrough of an example for the modelling would have helped. I was little confused when the equation was used during the course to explain the confounder, predictor and outcome. Instead of using X, Y, Z - may be use an actual example and show they all relate would have made the course

автор: Brian N

•Apr 11, 2018

Good for introduction in Data Science Process

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