Brian Caffo, PhD
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 …
Instructor Details
Courses : 7
Specification: Data Science in Real Life
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49 reviews for Data Science in Real Life
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 6 hours |
| Year | 2015 |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
MARIO L –
Challenging
JOSEPH A –
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.
Alberto D E –
A crash course on what can go wrong in real Data Science projects, and how to improve your chances of success.
Edgar A C V –
I just finished this course but I cant enroll to the last one (I have 4/5 course in this moment). Can you please help me?? thanks!!!
Gabriel U U –
Great Learning experience
Bauyrzhan S –
Perfect
Martin B –
Enjoyed it very much. Keep it up guys ; ) Greetings from germany
Nachum S –
Good, a bit long for the material.
Peter L –
The course is valuable but highly focussed on scientific applications (inference) and less on business application (i.e. prediction). I hoped for a more even mix.
Jocelyn D –
Excellent course! Very interesting!
Roque A –
Very easy to follow with good examples. The focus on this course was on practicality and I really appreciated that
Wladimir R –
Excellent
Emmanuelle M –
Great course, although, if you are not already working or have knowledge in this particular filed/topic, it is challenging.
Humna A –
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
Jason C –
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.
ELINGUI P U –
Great practice example, great team at Zillow, and that to DataCamp
Elton K –
Interesting for a Non Data Science Executive despite some minor spelling errors in video transcripts.
Kim K R –
GREAT COURSE!!!
Shafeeq S –
Not that engaging content.Too much theoretical approach.
Alzum S M –
It’s an excellent course. I have learnt a lot.
Ng T C –
Good learning
Liping L –
This is a recommended course where you understand what are the requirements for a data engineer, data manager and data scientis
Neil N –
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
Jean–Michel M –
I would drop some of the cartoons. They are funny but they seem to distract Bryan and overall it’s distracting for us students too.
BAHAR A –
It is a helpful course about a statistical area. I recommend it.
Hector R C C –
thanks!
Jason G –
Very informative and a good introduction into the aspects faced while doing Data Science!
Alberto M B –
It wasn’t as focus on Managing Data Scientists as I was expecting, but rather focus on tips for Data Scientist.
Angelina –
The material is too long and boring.
pietbartolo –
Very useful course! I really enjoyed the technical not so much the statistical part of the course.
Priyanka F P –
Excellent technical information!
Gustavo V –
Help me understand what can I expect from a real data science project.
Georgios P –
Very good introductory topics!
Omid F –
Thank you very much for your excellent course. Best Regards Omid Faseli
Yonathan M P –
Great course!!!!! Tons of useful insights!
jose c –
Claridad del contenido entregado del curso
Mauricio L –
Great course. It delivers a fantastic framework to assess the process of successful Data Science.
Carlos A H –
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.
Fayez A S –
It opened new understanding for me. Loved every bit of it.
Paulo B M d S –
The authors really present real situation and challenges that data scientists face in their daily activities. Very good.
Manuel E –
Good review of everything that can go wrong& and eventually will.
Michail C –
This course is an excellent effort to document the issues faced in real life data science. However, the flow of the videos seems to be a bit confusing and some of the content is explained in a weird manner.
Aline N O –
This course for me was the most difficult to understand. Using as example situations with health area was hard to understand how I can apply in my case. But in general, the other courses were very nice for me.
Gilson F –
Nao gostei muito da didatica do instrutor e os slides nao ajudam no entendimento
Ryan M S –
I found this course to be the most enjoyable and knowledge benefiting of all the courses I’ve taken thus far.
Chander W –
Amazing
Patricia S –
good content but could be simplified and presented in a more focused man
JERRY O –
Good course with vibrant instructors.
Sheetal K –
The pace and the content of the course is just apt and keeps the participants engaged