The list of Data Science Bootcamps is now live at http://datascience.community/bootcamps
The list currently contains 11 programs. The programs range from full-time 12 week programs to part-time online training.
Data Science is one field that has definitely adopted the newer, innovative forms of learning. MOOCs are full of data science related courses and the List of Data Science Bootcamps definitely shows the variety of new techniques being used. For example, Zipfian Academy uses a 12-week immersive program to train students and work on projects together. Insight, Persontyle, and The Data Incubator focus on filling in the gaps of recent PhDs, and other programs such as Statistics.com and Leada are focusing on online programs. Leada will be an interesting program to watch in the coming months and years. The program is definitely different and could be a game-changer if it continues to grow.
Go see the full list of Data Science Bootcamps.
As always, if you know of another program that is not on the list, feel free to leave a comment.
Last week, I got the opportunity to spend some time with the team from Insight Data Engineering. They offer a free program that trains people to be data engineers. Then they help those people connect with a job at an impressive company. The program runs a few times a year and consists of 6 intense weeks learning about and working on a data engineering project.
Although the program is free, it does have a highly-selective application process. Once accepted, you can expect the following:
- A beautiful office space in sunny Palo Alto, CA
- Mentoring from experts in the field
- Meet and Greets with some of the biggest names in data science
- Introductions to some of the leading data engineering companies
- Access to a growing network of program alumni
- A bright future as a data engineer
Insight Data Engineering is the same company that has run Insight Data Science, a similar type of program but for scientists instead of engineers, for the past 2 years. That program has 100% placement so far, and I don’t see that number ever changing. The program has an excellent advisory board that is actively involved in the program.
The Data Engineering program is actively accepting applications for the next session scheduled to start in September. Hurry, the deadline for applications is July 7, 2014.
Recently, Persontyle launched their School of Data Science. The goal is to produce data science training and education for professionals. Here is a brief list of the type of programs being offered.
The offerings a not free, but they look very good and are taught in cities around the globe.
They are different than Coursera and Udacity because the training is more specific and individualized. Plus, it is targeted at businesses and working professionals. A number of other companies offer data science training, but Persontyle appears to be the only ones offering data science training without trying to push their own products. If you or your organization is looking for training in data science, I would highly recommend The School of Data Science from Persontyle.
Recently, both NYU and Columbia launched academic programs in data science. Well, another school in New York City is entering the mix. The City University of New York (CUNY) is now offering an online masters degree in data analytics. If you would like more information, there will be an online information session on May 22.
Coursera has some excellent courses coming up in 2013. Here are some potential curriculum paths for someone looking to learn data science.
Either sequence requires/recommends some basic programming experience. If you are unfamiliar with programming, you still have a couple weeks to get familiar with some basic programming concepts. Some good places to start would be either Coursera’s Computer Science 101 or Codecademy’s Python tutorial.
Data Science Curriculum #1
If you are new to programming, this would be the recommend sequence. The first course focuses on programming.
Data Science Curriculum #2
Neither of the Coursera machine learning (Stanford or U of Washington) courses are scheduled for 2013, but either of them would be a great (maybe necessary) follow up course. Hopefully, one of those courses will be starting in July or shortly there after.
After completing one of the above sequences combined with a machine learning course, a person should be skilled enough to begin doing useful data science work. (Note: A new job as a data scientist is not guaranteed, but the courses won’t hurt your chances.) Plus, Coursera offers numerous other classes that could be taken at a later time to increase depth in certain areas of data science (Natural Language Processing, Image Processing, and more).
Happy Learning in 2013!
If you are interested in more ways to learn data science, please check out Data Science 201, coming in 2013.
Code School is offering a course title Try R. The course is completely free and can be completed online with the interactive tutorial. You will learn by doing. If you have been looking to learn R or need a quick refresher, this is probably a very good option.
I recently read, Big Data Education: 3 Steps Universities must take
Here are the 3 steps listed:
- Data Science cannot be an undergraduate degree
- A graduate degree should contain math, stats and computer science
Step 2 seems obvious. Math, stats, and computer science are some of the key areas for data science. I would add communication and presentation skills to the list because people with just math, stats, and CS skills are not known to be naturally good communicators. I agree with step 3. More research needs to be done, but most of the research will need to be interdisiplinary. Universities need to put more effort into interdisiplinary research.
Step 1 confused me a bit. The argument was data science has too many necessary skills and an applied focus area. Of course a person cannot learn everything about data science in an undergraduate degree. Earning a computer science degree does not mean you will know everything about computer science. It just means you know the fundamentals about algorithms, architecture, and operating systems. You know enough about computer science to understand the field and learn more as you go. I think 4 years should be enough time to do the same for data science.
What are your thoughts?
Recently, a great comment was posted on the list of Colleges With Data Science Degrees. Currently, many of the data science related college programs are being built in the Business Department. While it is great that colleges are starting to build data science programs, data science is so much bigger than just business. This was a nice reminder that data science is used in many fields.
I thought the comment by Bernice Rogowitz said it very well. Here is a copy of the comment:
It’s not all about business!
Some of the posts align data science and analytics with business applications. It’s important to keep in mind that scientists and researchers of all stripes are using statistical approaches, optimization, clustering, outlier detection and data cleansing methods, etc., not just analysts in the business world. In fact, some of the most sophisticated models come from outside of business. And, don’t forget the importance of analytics for finding features in non-structured data, such as images, text, 3-D models and simulations, etc. The large scope for data science and analytics was recently explored in a workshop: http://www.radcliffe.harvard.edu/exploratory-seminars/new-multidisciplinary-approach-data-understanding
Once again, California Institute of Technology will be broadcasting Learning From Data online. This is an introductory course on machine learning. All of the videos are broadcast live, and online homework and discussion forums are available. I have not participated in the course, so I would love some comments about how this class compares with the Coursera machine learning course.
Just Announced, Coursera adds 17 new universities. Those universities include Columbia and Brown, as well as a few international universities.
A few notable courses for data science are: a new machine learning course from the University of Washington, Linear Algebra from Brown, and Natural Language Processing by Michael Collins from Columbia.
See the following pages to seed what other courses are now available.