This looks to be a great webinar! It is today.
This Spring, Harvard University ran a data science course. Technically, the name of the course was Stat 221 Statistical Computing and Visualization. The course recently finished, and all the course lecture slides are available.
The slides contain a bunch of useful information, plus they show one possible layout for a data science course.
The Institute for Data Science and Engineering at Columbia University has released their first academic offering. It is a certificate program titled, Certification of Professional Achievement in Data Sciences. The certificate program consists of 4 courses:
- Algorithms for Data Science
- Probability & Statistics
- Machine Learning for Data Science
- Exploratory Data Analysis and Visualization
Columbia is currently accepting applications for the Fall of 2013. Unfortunately, the program will not initially be offered online.
Also, Columbia is planning to start a new master’s degree in data science sometime in 2014. A PhD program is supposed to come sometime after that. Some of the future programs will also be available online. Combined with the data science program at NYU, New York City is becoming a premiere academic location for learning data science.
I thought this was a fun little video about gamification and data science, plus my 2 year-old was mesmerized by the video. It is worth 3 minutes to watch.
This is an excellent write-up for the differences between:
- Machine Learning
- Data Mining
- Big Data
- Predictive Analytics
- Data Science
This spring Columbia University is offering another course on Data Science. This one is targeted at introductory graduate students in math, and it is not intended to be an advanced machine learning course. The goal is to expose people with strong mathematical skill to some of the ideas from software development and machine learning without sacrificing the statistical theory. The course is title, Columbia Applied Data Science. The link for lecture notes(PDF) provides a great tutorial for beginning topics in data science. The lecture notes are currently still under development.
Want to learn Data Science in 12 weeks? Zipfian Academy is offering just that.
The inaugural class will begin Fall 2013. Also the schedule is five days a week from 9 a.m. to 7 p.m., so it is a very intensive program. You must be willing to relocate to San Francisco for the 12 weeks. The cost of the data science program is $14,400, but some scholarships and sponsorships are available.
At first the cost seems high, but when you consider the program will prepare you for a different career in just 12 weeks, it does not sound so bad. I think you are paying for 2 things: the immense amount of information and the condensed format. The information planned to be covered does look very extensive, everything from storing data to cleaning data to machine learning.
I am not aware of another program like this existing. If you are not concerned with getting a “university degree” and would like to learn data science, I think Zipfian Academy looks like a good choice.
In just the past month, a couple of great resources for learning python have been created.
- Getting started with Python: Tips, Tools and Resources – If you are new to python, this is a great place to start. It contains a brief description and links to books, tutorials, and MOOCs.
- Getting Started With Python for Data Scientists – This focuses more on tools specifically for data science.
Combined together, the previous links should provide a person all the resources necessary to begin doing some data science with the python language.
The University of Chicago and Argonne National Labs are hosting Data Science for Social Good Summer Fellowship 2013. The Fellowship program is open to students at all levels whom are interested in working on real-world social problems. The program takes place in Chicago and the application deadline is April 1, 2013, so apply soon.
The inaugural issue of Big Data was published a few weeks ago. The journal is excellent. The articles are relevant, readable, and free. In the first issue, most of the articles were not super technical (meaning there was not a lot of equations or algorithms). I would like to highlight just 5 of the articles (feel free to read the others as well).
- Making Sense of Big Data – A nice brief discussion of the term big data and some goals for the journal.
- Big Data For Development - This is an introduction to United Nations Global Pulse, an initiative to use data to better understand human well-being.
- Broad Data: Exploring the Emerging Web of Data – This article is all about dealing with the explosion of open data becoming available.
- Data Science and Its Relationship to Big Data and Data-Driven Decision Making – The title is pretty self-explanatory. The article points out 7 fundamental concepts of data science.
- Educating the Next Generation of Data Scientists – This is a roundtable discussion all about data science and data science education.