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Data Science Capstone

This course is a part of Data Science, a 11-course Specialization series from Coursera.

The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.
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Johns Hopkins University

Rating 4.1 based on 171 ratings
Length 8 weeks
Effort 4-9 hours/week
Starts May 25 (11 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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What people are saying

According to other learners, here's what you need to know

data science in 27 reviews

So disappointing, it feels somewhat unrelated to the material covered in the 9 courses in the Data Science Specialization, so I didn't feel adequately prepared for tackling the Capstone even though I carefully completed all pre-req courses.

Also, the level of complexity of the problem (ie having to read multiple academic papers on NLP and computational linguistics) is not appropriate for a course that should be focused on teaching general, practical, applicable data science skills.

Thanks It is very good course, I learned lot of Data Science algorithm from these course.

I would recommend the course set to my colleagues if they have interest on data science.

Unfortunately, the Data Science Capstone was the worst of all the courses in the specialization.

Very good for anyone wanting to get into the field of Data Science using R It was tough... learning about something completely new for the final project was a challenge.

Great course, I learned a great deal about data science.

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natural language processing in 10 reviews

Well, most of the class was just about learning natural language processing (NLP), which wasn't covered.

natural language processing, markov models, etc.

A very tough and challenging project, but a great way to learn a lot about Natural Language Processing and algorithm coding in R, and in the end to have a cool Shiny app to add to your portfolio.

It also gives the student a glimpse of what Data Science in real life is and touches on Natural Language Processing as part of AI.

Fantastic exposure to Natural Language Processing!

The Capstone Project makes you summarizes what you have learnt so far and take it to the next level, natural language processing .

Your final product will be displayed for everyone via ShinyApps and a presentation using R Presentation (also published via RPubs).On a(nother) negative note, the topic of Natural Language Processing is not an easy one to just walk into and feel confident in providing a working next-word prediction algorithm in about eight (8) weeks.

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data scientist in 9 reviews

The assigned project is quite unexpected but it really tests on the skills of an aspiring data scientist!

I get that you will always see new data formats as a data scientist, but having the whole course cover numeric data and then having the final project be on text data where you can't apply what you learned seems sub-optimal.

Great course for becoming a data scientist Interesting assignment!

Really challenging but satisfying enough!Thank you for Cousera team who patiently developed such a beautiful program for upskilling us, the so-called data scientist!

I am looking for a new Data Scientist career (https://www.linkedin.com/in/joseantonio11)I did this specialization to get new knowledge about Data Science and better understand the technology and your practical applications.

Learnt a ton about various NLP algorithms for anyone who aspires to be a Data Scientist !

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previous courses in 7 reviews

The negative is that there is no explanation whatsoever about NLP, which was never mentioned in the previous courses, so there's not much teaching or guidance.

LOLI have a software development background (and completed the previous courses in the specialization), so translating approaches I found described in various sources into code wasn't "easy"; but it wasn't a barrier, either.

Also, in my opinion the materials / resources given to this course are scarce compared with previous courses of the specialization.

Self pace and with a lot examples and discussion forum support This class was a huge challenge for me, but it pushed me to learn a whole lot and practice many of the skills that I had learned in previous courses!

On a positive note, you will use all of the skills from the previous courses: writing R functions, performing exploratory analysis and publishing it via RPubs.

Well-paced, highly structured Capstone that allowed me to put to the test the skills I honed during the 9 previous courses in the JHU Data Science specialization.

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real world in 6 reviews

nice course, and the project task is a quite interesting one Very instructive, since it presents you with a real world problem, that you need to solve by yourself, in all of its complexity.

It resembled a real world task, where an idea is presented and it is up to the user to research methods and processes for the best outcome.

Very Dirty Data Sources made it a very Real World problem to solve.

Having completed this project, I feel more confident about my skills as a data scientist in solving real world problems.

Some of my fellow learners complained about the new data type and little information provided, but I feel this is a good simulation of real world experience as a data scientist!

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machine learning in 6 reviews

I think a machine learning project that tied together everything that we'd worked on up until this point would have been a lot more fun and rewarding.

This course significantly challenged my skills in programming, probability, machine learning and applied mathematics (eg Katz's backoff theory-equations).

The instructors (specially Peng) spent way too much time detailing R syntax that could have been picked up by the students on their own from other resources available on the web...The regression models and statistical inference courses are exceptions though: Together with the machine learning course, these are probably the most useful from the whole specialization.The materials in this capstone project are way sloppier than materials in other courses by the way.

Also, most of what we learned in the first 9 courses about statistics and machine learning turned out to be irrelevant to the capstone project.

I would prefer a a large scale machine learning capstone where we could make models and it would fit better to real life situation!

The capstone project doesn't fully utilise d knowledge from earlier modules such as Machine Learning, statistical analysis, regression models n etc.

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Online Faculty - Multimedia Capstone $27k

Capstone Advisor, Masters in Real Estate $57k

Assistant Capstone Project Manager $58k

Postdoctoral Capstone Researcher $59k

Capstone Experience Researcher $64k

Capstone Project Manager $73k

Planner/Project Manager- Capstone Experience $86k

Project Architect, Project Manager $94k

Senior Capstone Researcher $99k

Industrial Engineer Capstone $103k

Capstone Project Mechanical Engineer $131k

Project Manager, Project Engineering $143k

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Coursera

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Johns Hopkins University

Rating 4.1 based on 171 ratings
Length 8 weeks
Effort 4-9 hours/week
Starts May 25 (11 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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