As a Architect I am always looking into ways of working with new technologies, innovations and fields. One of this is Data Science and as such I am currently undertaking a set of courses to get an understanding of the field of Data Science.
Having a level of understanding will allow me to work more closely with data scientists in helpling them with suitable solutions as well as increase my skills in manipulating data.

The John Hopkins University are currently running a set of courses on Data Science consisting of 9 modules. Below is a dependency chart for anyone wanting to take these
Coursera Johns Hopkins Specialization in Data Science course dependency information

There are 9 modules to take which can be done free of charge or you can pay about $30.00 per course to get a certificate and take a final capstone project to test your understanding.

Each course lasts around 4 week and consists of video based lectures, forums, projects and knowledge test quizes.

John Hopkins consider two forms of dependencis for these courses:

Hard dependency: Students will be required to know material from the prerequisite course. Taking the dependent course simultaneously will be challenging and only possible for highly motivated students willing to work ahead of the course schedule for the prerequisite. Taking hard dependent courses out of order is not possible unless the student already knows the material covered in the prerequisite course.

Soft dependency: Knowledge of material from the prerequisite course is recommended and useful. Concurrently taking the prerequisite course and the dependent course is possible. It is not recommended to take them out of order, but would be possible for highly motivated students willing to self teach components of the prerequisite course as needed.

The courses are listed below in order that they should be taken in with links to the courses.
The Data Scientist’s Toolbox
https://www.coursera.org/course/datascitoolbox

This is the primary introductory course for the specialization. It should be taken first and has no prerequisite courses. Students should be computer literate, have programmed in at least one computer language and be motivated self learners.

R Programming
https://www.coursera.org/course/rprog

This is the most crucial course for the remainder of the specialization. It is softly dependent on The Data Scientist’s Toolbox. It should be taken before the remaining courses in the series.

Getting and Cleaning Data
https://www.coursera.org/course/getdata

This course has hard dependencies on R Programming and The Data Scientist’s Toolbox.

Exploratory Data Analysis
https://www.coursera.org/course/exdata

This course has hard dependencies on R Programming and The Data Scientist’s Toolbox.

Reproducible Research
https://www.coursera.org/course/repdata

This course has hard dependencies on R Programming and The Data Scientist’s Toolbox.

Statistical Inference
https://www.coursera.org/course/statinference

This course has hard dependencies on R Programming and The Data Scientist’s Toolbox. In addition, students will need basic (non calculus) mathematics skills.

Regression Models
https://www.coursera.org/course/regmods

This course has hard dependencies on R Programming, The Data Scientist’s Toolbox and Statistical Inference.

Practical Machine Learning
https://www.coursera.org/course/predmachlearn

This course has hard dependencies on R Programming, The Data Scientist’s Toolbox and Regression Models. It has a soft dependency on Exploratory Data Analysis.

Developing Data Products
https://www.coursera.org/course/devdataprod

This course has hard dependencies on R Programming, The Data Scientist’s Toolbox and Reproducible Research. It has a soft dependency of Exploratory Data Analysis.

*material from Coursera John Hopkins University
https://www.coursera.org/jhu