Overview

Class Central Tips

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.

Syllabus

Overview, Understanding the Problem, and Getting the Data

This week, we introduce the project so you can get a clear grip on the problem at hand and begin working with the dataset.

Exploratory Data Analysis and Modeling

This week, we move on to the next tasks, exploratory data analysis and modeling. You'll also submit your milestone report and review submissions from your classmates.

Prediction Model

This week, you'll build and evaluate your prediction model. The goal is to make your model efficient and accurate.

Creative Exploration

This week's goal is to improve the predictive accuracy while reducing computational runtime and model complexity.

Data Product

This week, you'll work on developing the first component of your final project, your data product.

Slide Deck

This week, you'll work on developing the second component of your final project, a slide deck to accompany your data product.

Final Project Submission and Evaluation

This week, you'll submit your final project and review the work of your classmates.

Taught by

Jeff Leek, Roger Peng and Brian Caffo

Reviews

3.0 rating, based on 4 Class Central reviews

4.5 rating at Coursera based on 1224 ratings

Start your review of Data Science Capstone

Colin Khein
Dwayne D

The positive: Completion of this project requires most of the skills you will have learned in completing the prequisite courses. The negative: If you're "like me" (inexperienced with NLP), you should start reading up on the basics (enough to know t…

The positive: Completion of this project requires most of the skills you will have learned in completing the prequisite courses.

The negative: If you're "like me" (inexperienced with NLP), you should start reading up on the basics (enough to know the challenges, available tools, prevalent techniques) 1-2 weeks before Week 1 of the course. If I hadn't done this, then I would not have given myself enough foundational understanding to understand what work(s) I should use as as my guiding examples (and why). Reading the course forums and reviews, I think many people who had trouble completing the project were not able to take sufficient time before that first week to get oriented.

Suggestion to improve: Give people a heads up, as well as a list of digestable reading material and suggest they start reading 2 weeks before Week 1 of the course.

Details:

The domain of subject matter central to the capstone project (text mining and NLP) differs from that of examples used in the prerequisite courses, but the prerequisite courses gave me the tools to tackle the new domain. Now, I did have to read up on text mining and NLP. I'm a slow reader, so it took me a couple of weeks to read enough that I felt comfortable with the concepts that would help me complete this project. Once I took the time to get that background, though, it became apparent that "predicting the next word" is an NLP challenge that has been thoroughly studied and there are LOTS of examples freely available, where people describe and show how they have tackled the same challenge.

Since I have a software development background and completed the prerequisite courses in the specialization, I was able to translate approaches described in English into software. I was helped along greatly by the existence of R packages such as tm and tokenizers, and was always able to find guidance on addressing thorny issues by using "good ole Google Search". Most often, my searches would lead me to StackOverflow and writeups from capstone project alumni. I did my own writeups and wrote my own code, but I also very much learned lessons from those who went before me.

I would recommend this specialization by JHSU, which (as it should be) is a package deal with the capstone project. Applying what I learned to a new domain helped me better understand how to practically apply sampling, statistical inference, cross-validation and more (just to another domain).
Anonymous

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.
Mark Henry Butler

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