Free Big Data Education: A Data Science Perspective

As the third and final part of my Free Big Data Education series (following Free Big Data Education: A Management Perspective and Free Big Data Education: A Technical Perspective), I’d like to finish up with the area of big data closest to my heart: data science.

Resources to get up to speed with data science
The following list of resources will let you join the data revolution by getting up to speed with data science. As you can tell from reading all the articles and posts here on Big Data Republic, this whole area is exploding in popularity and attention.

Data science -- and the driving force behind it, machine learning -- is the process of deriving added value from data assets. Commerce and research are being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels span a variety of disciplines and are not easy to obtain through conventional curricula. These include algorithms for machine learning (e.g., neural networks and clustering), parallel algorithms, basic statistical modeling (logistic regress and linear/non-linear regression), and proficiency with a complex ecosystem of tools and platforms.

Meetup groups
A good place to start is with meetup groups. Two of my favorite data science groups deal with the primary ingredients of data science work: R, which is the programming environment of choice for building algorithms, and machine learning. The LA area R user group is excellent; try to find one near you. The LA Machine Learning group has regular meetings that are extremely useful.

For more insight into these groups, visit the Field Report category at my blog, Radical Data Science, where I write up intimate accounts of the various meetings I attend.

Open courseware
The Massive Open Online Course (MOOC) movement is very active in the data science space and constitutes a superb educational resource. These free courses (some offer certifications) offer an excellent path toward obtaining the requisite background for becoming a data scientist. I’ve put together a BDR Data Science “pseudo degree program” for you to follow.

• Lower-Division Courses
• Data Science 101 – Statistics One
• Data Science 102 – Computing for Data Analysis
• Data Science 103 – Data Analysis
• Data Science 104 – Introduction to Data Science
• Upper-Division Courses
  • Data Science 201 – Machine Learning I
  • Data Science 202 – Machine Learning II
  • Data Science 203 – Neural Networks for Machine Learning
• Graduate Courses
  • Data Science 301 – Learning from Data (Caltech course CS101)
  • Data Science 302 – Machine Learning III (MIT course 6.867)

Free data science books
To go along with the coursework, there also are a number of excellent free books available:

• Mining of Massive Datasets
• Bayesian Reasoning and Machine Learning (pdf)
• Information Theory, Inference, and Learning Algorithms
• Gaussian Processes for Machine Learning (pdf)
• The Elements of Statistical Learning
• Introduction to Machine Learning (pdf)
• Think Bayes (pdf)

As the interest in data science continues to grow, and as the shortage in talent becomes apparent, the timing is excellent to retool yourself and climb aboard the data science gravy train.

Related posts:

• Free Big Data Education: A Technical Perspective
• Free Big Data Education: A Management Perspective

— Daniel D. Gutierrez, Data Scientist, AMULET Analytics