If you follow data science news, you will know that the New York Times puts out some very good content. Well, recently they ran a section called BIG DATA 2013. There are many articles and tons of information. Happy Reading!
This infographic explains the Three V’s of big data, contains a nice list of analytical techniques, related trends and some other information.
Due to the large list of Colleges with Data Science Degrees, I receive a number of email inquires with questions about choosing a program. I have not attended any of the programs, and I am not sure how qualified I am to provide guidance. Anyhow, I will do my best to share what information I do have.
Originally, the list started out with 5 schools. Now the list is well over 100 schools, so I have not been able to keep up with all the intricate details of every program. There are not very many undergraduate options, and the list only contains a few PhD programs, so the information here will be focused on pursuing a masters degree.
Start by asking 2 questions:
- What are my current data science skills?
- What are my future data science goals?
Those 2 questions can provide a lot of guidance. Understand that data science consists of a number of different topic areas:
- Mathematical Foundation (Calculus/Matrix Operations)
- Computing (DB, programming, machine learning, NoSQL)
- Communication (visualization, presentation, writing)
- Statistics (regression, trees, classification, diagnostics)
- Business (domain specific knowledge)
After seeing the above lists, this is where things get cloudy. Everyone brings a different set of existing skills, and everyone has different future goals. Here are a few scenarios that might clear things up.
The most common approach is to attempt to build knowledge in all 5 topic areas. If this is your goal, find the topic areas where you are weakest and target a graduate program to help you bolster those weak skills. In the end, you will come out with a broad range of very desired skills.
A different approach is to select one topic area and get really, really good. For example, maybe you want to be an expert on machine learning. If that is your goal, then maybe a traditional computer science graduate program is what is best. In the end, you will be well-suited to be an effective member of a data science team or pursue a PhD.
A third and also common approach is from people that want to help fill the expected void of 1.5 million data-savvy managers. These people do not necessarily want to know the deep details of the algorithms, but they would like an understanding of what the algorithms can do and when to use which algorithm. In this case, a graduate program from a business school (MBA) might be a good choice. Just make sure the program also involves coverage from the non-business topics of data science.
I think NYU is the best example of a school that can help a person achieve just about any data science goal. The NYU program is a university-wide initiative, so the program is integrated with many departments (math, CS, Stats, Business, and others). Therefore, a student could possibly tailor a program to reach a variety of future goals. Plus, New York has a lot of companies solving interesting data science problems.
There you have it. It does not narrow the choices down, but it should help to provide some guidance. Other factors to consider are length of a program and/or location.
Good Luck with your decision, and feel free to leave a comment if you have and good/bad experiences with any of the particular graduate programs.
A few days ago I posted Data Science is more than just Statistics. I did not feel the post was complete so I am adding a part 2.
I received a couple comments about data scientists being involved with the collection of data. Yes, I would agree that is true. In order for data products to work, the correct data has to be collected. I probably did not explain this very well, but the approach to data collection is different. Typically, a statistics project will run some sort of rigorous experiment to collect data. The experiment will be very controlled and well-understood. In contrast, a data science project will collect data from existing systems, new system, sites on the web, sensors, and various other places. Most of the data does not come from a very controlled environment. By controlled, I mean specific number of users, specific type of users, specific time frame, and/or set constraints on the environment. This conglomeration of data is one of the reasons data scientists deal with such large datasets (there are other reasons too such as cheap hardware). I think it is more common for a data science project to deal with the whole population rather than a certain sampling of the population.
Importance of Statistics
In the previous post, I failed to emphasize the importance of statistics. I never said that statistics is not important to data science. Statistics is a critical element of data science. However, if you only know and study statistics, I feel you are missing other key elements of data science.
Thus, I stand by my previous statement, “if you just want to do statistics, join a statistics graduate program. If you want to data science, join a data science program.” Stay tuned for a post on choosing a data science graduate program.
Do you agree/disagree?