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
MA
Excellent course! Highly recommend it.Dr. Peng covers the essentials of Managing Data Analysis in a balanced way, and empowers the data science manager/executive to be able to apply these right away.
ST
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
CT
Excellent course. Really enjoyed the instructor. Finally getting into some analysis. Made me want to refresh my stat skills and get working on a challenging project!
JF
This felt a lot more detailed that the previous courses (which was great!) and I feel like I've genuinely learned some stuff (the six type of questions) that I can use!
BC
Like other reviewers said, this course is larger than the two previous courses. The content is excellent but I am giving 4/5 stars because I found many misspelled words throughout the course lectures.
RD
This course is not for a person without any idea of Data analysis or Statistics. This course isn't for beginners. The content could have been presented even better with lucid examples.
GK
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!
KC
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.
CM
I really liked the extensive readings to supplement the video presentation. I am a visual learner, seeing the words and graphs were a very beneficial supplement.
SS
Great examples about difference between 'association' and 'predictive' understanding and pitfalls of decision making without knowing which one to be used where!
SK
Good course for the fundamentals of making sure data analysis is done correctly. These are good things to keep in mind when you are managing a data science team.
VG
Good overview of the process. Helped me in bridging data analysis processes with things that I already do as part of project management or business analytics/decision support projects.
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This course gives me a general understanding of the whole process of data analyses. Pretty good!
A course heavy focused on processes. Sometimes it seems like there are a lot of low-level details, but if you have management experience, you'll understand that you need to know some specific solid details to successfully manage the team. This will help you to formalize the structure of your data analysis process.
Lots of talking head videos without enough visual aids to solidify the adult learning process. Not very "Executive level" as the course title and description imply. I would expect more "TED Talk" level lectures and materials, with plenty of real-world examples. Content in these courses seem more "new manager / new supervisor" level, if not actually geared toward an undergrad audience.
too lengthy, technical and dry
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 my instructor and enjoyed the course material and videos presentation of this course. Mind blowing approach was adopted especially in the basic components of Managing Data Analysis.
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.
Thanks & best wishes to all Coursera Team.
This course provided me with a lot of direction for some data I'm trying to understand at work. It also made me aware of some important considerations I need to make and structure I should follow when looking at the data, so that my results will be more meaningful. I honestly thought the last section about presentation wouldn't be useful at all, but even that section provided some important insight that I hadn't previously considered, and made me feel excited about eventually presenting the results of my data analysis to my organization.
Honestly, this is one of the best online courses I have ever taken in my life. I want to express my heartfelt gratitude to Dr. Roger D. Peng for brilliantly conducting this course. I found his teaching style of using examples and analogies to explain important Data Analysis concepts to be fascinating. I highly recommend this course to anyone who's getting into data analysis/science and even an expert in the field solely because this course will teach you to look at data analysis in a new perspective.
Big thanks to the our course instructors for this amazing learning platform!
The course is very informative and appropriate to the level and pace of the learners. It also builds confidence, and provides for considerable number of scaffolds to its learners.
As a teacher, I can really say that assessment strategies were highly relevant, appropriate to the course modules.
I didn't expect that I'll be able to cover all topics assigned.
Bonus : Free books and free certificates!
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 ...
A data science course rich in theoretical concepts, like what all to see in the cleansing of data,, theories in iterating with data samples etc. It manages to clarify rather lucidly,when to go in for inferential models and when for predictive machine learning ones. An invaluable course not only for data managers but for data scientists as well.
I've taken the first three courses at this point and this has easily been my favourite. I particularly enjoy the focus on practical examples. Breaking down the analysis conceptually first then highlighting how this can be applied with a more complex and practical context.
Intense but manageable exposure to the different types and methods of data analysis that one will encounter in any data science projects. Helps to identify areas of additional knowledge or required concepts from statistics that need to be revisited.
This course gives a really good guide on how to define data science questions correctly and how to work with sharp goals during the hole Data Analysis funnel from the definition of the Data Science question to the communication of it's results.
Very good delivery, and with many simple examples. Easy and make people excited to learn more
More simple examples about interpreting data would be more helpful.
Personally not a big fan of Roger Peng's approach while teaching. The lessons get a bit confusing, and so does the questions from the quiz.
Jeff Leek's approach is more calm and simple.
Nevertheless, the course in general is really good.
Some of the sections are a long, however, the overall organization and presentation of this class is excellent. The formulas are not so hard as to loose people, like myself, who did not take calculus or Statistics to be able to follow them.. even if I probably could not solve them. The programming is helpful for programmers, but not needed to understand what is being taught.
I highly recommend getting the book and downloading it as a pdf, then opening it in Word so that you can take notes, highlight, bold things and underline. The quizzes follow well with the subject and are not needlessly tricky for the sake of being tricky.
Roger Peng really knows his stuff and is an excellent communicator. He is clear, organized, and engaging to listen to! I really felt like it was an honor to learn from him in this class! Even if you take this for general business knowledge, this class is really worth your time, and can apply to a lot in life!
Excellent course. Would like to see more courses like this regarding machine learning for example - courses not focused on the implementation and tools (for example I am with strong Java background, have read several AI/ML books, implemented several ML algorithms, including deep learning (DBNN, SdA, Dropout FFNN) - but all courses are built around python/octave/R tools which I have no interest right now to learn and it is nearly impossible for me to learn both the implementation tool/language and ML theory in parallel.
Therefore I do not search for the course in the specific implementation language, I search for the course that can teach & verify my skills one level higher - and I think the way the examples were done in the quizzes - is the right (and perfect) way to do it.
Hth.
Tomas
My name is Jose Antonio. I am looking for a new Data Scientist career (https://www.linkedin.com/in/joseantonio11)
I did this course to get new knowledge about Data Science 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.
Jose Antonio.
The information provided in this course is extremely important, and sometimes overlooked in favor of the most technical features of data analysis. The topics from the course provide a framework that allows data execs and analysts alike to work efficiently and always be aware of which stage they are in. This knowledge will definitively help me professionally.