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Data Science Capstone Final Project
This presentation is a short description of a project that will predict the next word of a sentence fragment or phrase.
The application is a capstone project for the Coursera Data Science Specialization provided by Johns Hopkins University with support by Swiftkey.
The main goal is to develop a predictive algorithm. The front end will be as a shiny application and the backend will utilize R.
The application was developed using a sample of twitter tweets (English). This sample was provided by Swiftkey.
There are German, English, Finn, and Russian version. This application will only use the English version.
After loading all of the English data, the algorithm pulled the number of lines, removed profanity and tokenization. The tokenization was organized into n-gram sequences.
The result is a bigram, trigram and a quadrigram models and converted into frequency dictionaries sorted by freq number.
Application
The application relied on functionality and simplicity. By default when loading the application it will check for a word and a message will show requiring entering a word or phrase.
The user can now enter a word or phrase. The application will require the user hit submit. When this happens 3 items will display.
- The next word
- The R application display of what has transpired. This is for debugging.
The application started at quadrigram and worked its way down to determine if it can find a predictive word.
The prediction app is hosted on the shinyapps.io location: https://zagnut.shinyapps.io/shiny/
The code for this frontend application is hosted here: https://github.com/motticus/capstone
The data for this application is hosted here: https://d396qusza40orc.cloudfront.net/dsscapstone/dataset/Coursera-SwiftKey.zip
- Coursera Data Science Capstone Final Project
- by Manuel A. Diaz R.
- Last updated over 2 years ago
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JHU-Data-Science-Capstone
Coursera Data Science Specialization
View the Project on GitHub
JHU Data Science Capstone Project
The completed project.
A Shiny App for predicting the next word in a string. The App
The Project
Project Overview Sylllabus
Project Tasks - Instructions
Task 0: Understanding the Problem Task 1: Getting and Cleaning the Data Task 2: Exploratory Data Analysis Task 3: Modeling Task 3A: Milestone Report Task 4: Prediction Model Task 5: Creative Exploration Task 6: Data Product Task 7: Slide Deck Task 8: Final Project
Project Scripts - Solutions
Task 0: Exploring the tm Package Task 1: Getting and Cleaning the Data Task 2: Exploratory Data Analysis Task 3A: Milestone Report Task 4: Working toward a Prediction Model Task 04A: Fast Ngram Files Task 05: Prediction Model Task 06: Shiny App Task 06A: Shiny App Source Code Task 07: Slide Presentation
Course Quizzes
Quiz 1 Quiz 2 Quiz 3
Tidy Data Text Mining with R: A Tidy Approach
Capstone Projects
Education is one of the pillars of the data science institute..
Through educational activities, we strive to create a community in Data Science at Columbia. The capstone project is one of the most lauded elements of our MS in Data Science program. As a final step during their study at Columbia, our MS students work on a project sponsored by a DSI industry affiliate or a faculty member over the course of a semester.
Faculty-Sponsored Capstone Projects
A DSI faculty member proposes a research project and advises a team of students working on this project. This is a great way to run a research project with enthusiastic students, eager to try out their newly acquired data science skills in a research setting. This is especially a good opportunity for developing and accelerating interdisciplinary collaboration.
2022-2023 Academic Year FALL 2022: July 15, 2022 SPRING 2023: TBA
Project Archive
- Spring 2022
- Spring 2020
- Spring 2019
- Spring 2018
- Spring 2016
- Art & Design
- Computer Science
- Data Science
- Education & Teaching
- Health & Medicine
- Mathematics
- Programming
- Social Sciences
Professional and Lifelong Learning
In-person, blended, and online courses, data science: capstone.
- Professional Certificate in Data Science
- Data Analysis
- Data Visualization
- Probability
Associated Schools
Harvard T.H. Chan School of Public Health
What you'll learn.
- How to apply the knowledge base and skills learned throughout the series to a real-world problem
- Independently work on a data analysis project
Course description
To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.
Unlike the rest of our Professional Certificate Program in Data Science, in this course, you will receive much less guidance from the instructors. When you complete the project you will have a data product to show off to potential employers or educational programs, a strong indicator of your expertise in the field of data science.
Rafael Irizarry
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