A Sequence of 9 Courses on Data Science Starts on Coursera on 2 June and 7 July 2014

May 26, 2014
By

(This article was first published on blog.RDataMining.com, and kindly contributed to R-bloggers)

A sequence of 9 courses on Data Science will start on Coursera on 2 June and 7 July 2014, to be lectured by(Associate/Assistant) Professors of Johns Hopkins University. The courses are designed for students to learn to become Data Scientists and apply their skills in a capstone project.

You can take the courses for free. However, if you want to get a Verified Certificate in the course, the Specialization Certificate or taking the Capstone Project, you will have to pay for it. The cost is
$49 each × 9 courses + $49 Capstone project = $490 Specialization Certificate.

Below is course information picked up from the courses homepage on Coursera website, and more details can be found at https://www.coursera.org/specialization/jhudatascience/1.

Course 1: The Data Scientist’s Toolbox
Upcoming Session: 2 June, 7 July
Duration: 4 weeks
Estimated Workload: 3-4 hours/week
URL: https://www.coursera.org/course/datascitoolbox
Description: Upon completion of this course you will be able to identify and classify data science problems. You will also have created your Github account, created your first repository, and pushed your first markdown file to your account.

Course 2: R Programming
Upcoming Session: 2 June, 7 July
Duration: 4 weeks
Estimated Workload: 3-5 hours/week
URL: https://www.coursera.org/course/rprog
Description: The course will cover the following material each week:
Week 1: Overview of R, R data types and objects, reading and writing data
Week 2: Control structures, functions, scoping rules, dates and times
Week 3: Loop functions, debugging tools
Week 4: Simulation, code profiling

Course 3: Getting and Cleaning Data
Upcoming Session: 2 June, 7 July
Duration: 4 weeks
Estimated Workload: 3-5 hours/week
URL: https://www.coursera.org/course/getdata
Description: Upon completion of this course you will be able to obtain data from a variety of sources. You will know the principles of tidy data and data sharing. Finally, you will understand and be able to apply the basic tools for data cleaning and manipulation.

Course 4: Exploratory Data Analysis
Upcoming Session: 2 June, 7 July
Duration: 4 weeks
Estimated Workload: 3-5 hours/week
URL: https://www.coursera.org/course/exdata
Description: After successfully completing this course you will be able to make visual representations of data using the base, lattice, and ggplot2 plotting systems in R, apply basic principles of data graphics to create rich analytic graphics from different types of datasets, construct exploratory summaries of data in support of a specific question, and create visualizations of multidimensional data using exploratory multivariate statistical techniques.

Course 5: Reproducible Research
Upcoming Session: 2 June, 7 July
Duration: 4 weeks
Estimated Workload: 3-5 hours/week
URL: https://www.coursera.org/course/repdata
Description: In this course you will learn to write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, and organize a data analysis so that it is reproducible and accessible to others.

Course 6: Statistical Inference
Upcoming Session: 2 June, 7 July
Duration: 4 weeks
Estimated Workload: 3-5 hours/week
URL: https://www.coursera.org/course/statinference
Description: In this class students will learn the fundamentals of statistical inference. Students will receive a broad overview of the goals, assumptions and modes of performing statistical inference. Students will be able to perform inferential tasks in highly targeted settings and will be able to use  the skills developed as a roadmap for more complex inferential challenges.

Course 7: Regression Models
Upcoming Session: 2 June, 7 July, 4 August
Duration: 4 weeks
Estimated Workload: 3-5 hours/week
URL: https://www.coursera.org/course/regmods
Description: In this course students will learn how to fit regression models, how to interpret coefficients, how to investigate residuals and variability.  Students will further learn special cases of regression models including use of dummy variables and multivariable adjustment. Extensions to generalized linear models, especially considering Poisson and logistic regression will be reviewed.

Course 8: Practical Machine Learning
Upcoming Session: 2 June, 7 July, 4 August
Duration: 4 weeks
URL: https://www.coursera.org/course/predmachlearn
Description: Upon completion of this course you will understand the components of a machine learning algorithm. You will also know how to apply multiple basic machine learning tools. You will also learn to apply these tools to build and evaluate predictors on real data.

Course 9: Developing Data Products
Upcoming Session: 2 June, 7 July, 4 August
Duration: 4 weeks
Estimated Workload: 3-5 hours/week
URL: https://www.coursera.org/course/devdataprod
Description: Students will learn how communicate using statistics and statistical products. Emphasis will be paid to communicating uncertainty in statistical results. Students will learn how to create simple Shiny web applications and R packages for their data products.

Capstone Project
Duration: 4 weeks
Description: 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. The capstone project will be four weeks long, offered in conjunction with the series. The capstone class will be offered thrice yearly. The Capstone Project is available after you’ve completed all courses in the Specialization.


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