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Avis et commentaires pour d'étudiants pour Data Science in Real Life par Université Johns-Hopkins

4.4
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2,287 évaluations
276 avis

À propos du cours

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...
Points forts
Statistics review
(44 avis)

Meilleurs avis

SM
19 août 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
11 nov. 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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1 - 25 sur 279 Avis pour Data Science in Real Life

par Peter E

12 mars 2017

Brian Caffo does a terrific job teaching some of more advanced material, I very much appreciate his jokes and humor, as well as his helpful explanations of the material.

par Paul F

12 févr. 2018

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

par Liping L

29 janv. 2019

This is a recommended course where you understand what are the requirements for a data engineer, data manager and data scientis

par Sohail B

23 sept. 2017

Brief Profile: Sohail Butt

I am a man of 58 years old and having an experience of almost 30 years of Business Management of Pharmaceutical & Nutraceutical Industries of Pakistan. Presently I am having my own Consulting Company " AIMMS CONSULTING" and extending my services as Management Consultant to different companies of said sectors.

I am of the conviction that learning is never ending and have a habit of learning new ideas about my favorite subject about Data Science. Although I have the limited usage of this subject in my working areas but I love to know about new areas of different specialties.

I really appreciate highly the efforts of Mr. BRIAN CAFFO and enjoyed the course material and videos presentation of this course. Mind blowing approach was adopted especially in the basic components of Data Science in Real Life.

SUGGESTION:

MY PERSONAL HUMBLE REQUEST, Please make also the important components of course material as a part of this Certificate with % AGGREGATE so that it has a much more worth & impact for the courses participated.

A separate Transcript must be issued with having Aggregate % Score and important Components of participated course.

Hard Copy of this certificate mail to my home address in Pakistan. Please use my credit card,I am ready to pay all expenses in this connection.

Thanks & best wishes to all Coursera Team.

par Sandip M

20 août 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.

par Elliot S

12 nov. 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.

par Robert A

4 févr. 2016

Brian, Jeff, and Roger: Thank you very much for all the data science courses, really great. I generally rate them 5 stars. But for this one, I'm giving 3 stars, not because the content is not good (it is; it provides good practical and experiential information), but rather because the material seems repetitive at times either within the same course or with topics in the other courses. Also, the sequencing and lectures seem sometime a bit disjointed.

May I humbly suggest an idea: Integrate the key points of this course relating to real-world examples and the sharing of real-world experiences into one of the other courses.

Thank you.

Robert Al-Jaar, PhD

robert.aljaar@rassociates.biz

par Jason C

6 nov. 2018

I found this course to be notably worse than all of the others in the series. There is very very little practical content provided within the lectures. Way too many summaries or over-views of what's to come next without really getting into the nuances of what is discussed as a course topic. Way too much repetition of the exact same content, there is even repetition of content in this course that was presented in another one of the courses in the series. Many of the examples are purely meant as a comedic aside rather than actually functioning to discuss the topic with depth. E.g. - talking about statistical modeling and putting up a picture of Ben Stiller from Zoolander - then keeping the picture up there for the entire explanation. There's literally a Nic Cage example provided for the confounding factor lecture only for the instructor to say directly after "This isn't actually the best example" - then proceeds to not explain why it was brought up aside from mentioning there's a spurious correlation. Way too much repetition of similar examples - showing photos of a muscular v. skinny Christian Bale. This pop-culturey reference isn't needed in the first place and doesn't need to be shown in triplicate. I don't mind repetition if there is additional nuance or content provided through them, but that isn't the case in this course. I find there is too much focus on side tangents, where the instructor seems to change thoughts mid-sentence but forgets to come back to the original idea. I think that every single video could be cut down by 25%, purely by being more concise, and should include more nuanced descriptions. I found it particularly odd that instrumental variables were noted as a rather clever technique, yet an explanation was intentionally avoided, however an example was still provided. Bringing up a topic, intentionally refusing to define it, then providing an example directly after just doesn't make sense. I think that more time needs to be spent refining the lectures so that they're designed to teach content. It has the feel of someone who's talking about a field to get people interested in it rather than a practical training course. Many key terms are very poorly defined with examples (on many cases the audience is referred to wikipedia for explanations) in which the basics are repetitively explained while the nuances are glossed over. There seems to be an odd theme where summaries and over-generalizations are far too frequent and yet the key terms and how they relate to examples are an afterthought. I don't think the summaries are necessary given the fact that users can literally re-watch every single video and there isn't enough total content to justify a summary in the first place. Additionally, this course also seems to deviate from the others in that there is an assumption that the student has a heavy amount of programming experience already built in (or that's my assumption since many of the term explanations aren't discussed too heavily). Prior lectures break down the basics more and indicate that potential managers should pursue the data specialization courses.

par tommy c

23 mai 2016

great for existence human and android based life-form simulation internal lifestyle...

The course improves life within the simulation 10 fold at least(when combined with the other specialization courses) ...

Perfect learning tool for those who have worked professionally in research field sin our simulation and yet now have a touch of "the turrings" or you know : CBI...

Special thanks to the designers of the course.

Top scores for coursera.org &John Hopkins ...

par José A R N

30 sept. 2016

My name is Jose Antonio from Brazil. I am looking for a new Data Scientist career.

Please, take a look at my LinkedIn profile: https://www.linkedin.com/in/joseantonio11

I did this course to get new knowledge about Big Data and better understand the technology and your practical applications.

The course was excellent and the classes well taught by teachers.

Congratulations to Coursera team and Instructors.

Regards

par Punam P

16 avr. 2020

Very nice and helpful course to understand how data science helpful in different perspective in our daily life. It helps me lot. Thanks to Team of Course and Coursera team.. Special thanks to Resp.Professor Brian Caffo for boosting the knowledge through different modules. I also Thank to Johns Hopkins University.. Always ready to do lots of course to enhance my skill in Data Science field.

par Scott R H

5 juin 2016

Great information. For real newbies like me, might help to review basic concepts a few times (e.g. what does "p" mean? I know it was explained, but didn't grasp it the first time, helps if I hear it a few times, etc.) But, hey, you guys have done a fantastic job pulling all this together and teaching it at the 30,000 ft. view level. So great job.

par Rumanti D P

2 nov. 2020

At first it was hard to follow, because so many terminology I could not understand as from a non-data science person. But after week 2, it's super easy and Brian delivers it with passion plus so many anecdotes regarding to topics. Which really helps me digesting those "unknown" terms. Great job, I'm so pleased with this course!!

par Martín A M A

6 nov. 2021

The topics were really well developed by the instructor, and the examples given by him were totally clear. As I am the cofounder of a computer vision and data science company, what I learned during this course is being very useful for me. I totally recommend it. You will need a basic data science knowledge though.

par Jan R

28 mars 2016

Excellent course! The only thing I would say is that many managers from the corporate field simply would find it too "academic". But this was a huge plus for me - and I think the course will serve extremely well also to anyone who works in academic research and has to handle some real-life data.

par HYUN K

13 mars 2017

This class was bit more difficult than the previous ones and it requires really focused attention on understanding the subject matter. The course is intended to improve your understanding in subjects of surrogate variables, and how to become a better database manager. Excellent course!

par Bose M

11 déc. 2020

Its an exceptional course. A must pursue course for every manager either as new learning or refresher of knowledge.

A great thanks to course trainers. Their teaching approach is very target oriented to put the concepts in student brain in simpler and efficient way.

par Marzia N

12 août 2020

I liked this course most of all the courses of this specialization course. The only difficulties I faced was all the examples were related to biomedical science, maybe a more general example would do better consideration for learners from other domains like me.

par Joseph G

28 déc. 2016

The other courses in the exec series are a little simple. This started to address some core analysis issues and how to address them in a real life situation. A good reminder and thought provoker on what should be trying to do within the enterprise

par David G V

2 nov. 2020

The course is very short and concise. It guided me on what aspects of statistics I can work on to improve my skills in statistical analysis and quickly assess some statistical studies of other people either for work or leisure purposes.

par Julien N

20 févr. 2018

I really liked the approach of this class --comparing pure theory with what could possibly happen in real life!

This gave us a nice palette of problems and some tips about corresponding techniques / tools that can be used to solve them.

par Allen J M A

19 oct. 2017

Very engaging topic. The course will provide a very good and practical application of data science in real-world scenarios. The course materials are quite enough to gain understanding on the different topics presented. Kudos JHU!

par bojana m

26 juin 2016

Necessities: practical tools and techniques for managing real life issues with data cleanliness, interpretation of results, report writing, version control. All of them complete necessities for real life commercial projects.

par pamandeep s g

17 nov. 2015

The course is a good compilation of essential concepts to avoid pitfalls during analysis. Most material is given a short intro only, so a good data science background is needed to appreciate the material presented here.

par Carlos A H

30 juin 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.