Note: for the most recent updates, click here.

At the request of many prospective participants, here's an update about our DSA (Data Science Apprenticeship). Also, I have added a few large data sets, new projects and more material. Click here for details.

If you have already earned a data science certificate or diploma, but was not requested to develop and use your own API in batch mode, and harvest/work on a data set with at least 50 million observations in a distributed environment, then it's time to learn the real stuff that will land you a real job!

Klein Bottle

Click here for a general overview of our apprenticeship. We have published the data and source code for our big data keyword correlation API. Read the material and download the three files (and post your comments if you have questions, I'll reply ASAP): it will teach you how API's work, and how to write your first API from scratch!

Our next API example will come with the source code of a web crawler, and will illustrate how to detect copyright infringement or how to detect the original, first version of an article published in multiple news outlets (doing a better job than Google).

All the training material will be offered for free to everyone. We have not yet put everything into a nice booklet, but some of the content is already available:

A few data sets available for download, from the following articles:

• Source code for our Big Data keyword correlation API
• Great statistical analysis: forecasting meteorite hits
• Fast clustering algorithms for massive datasets
• 53.5 billion clicks dataset available for benchmarking and testing
• Over 5,000,000 financial, economic and social datasets
• New pattern to predict stock prices, multiplies return by factor 5
• 3.5 billion web pages
• Another large data set - 250 million data points - available for do...
• 125 Years of Public Health Data Available for Download
• Two big datasets to challenge your data science expertise *
• From the trenches: real data science project (Google Analytics) *
• Data sets and other machine learning resources from UC Irvine *

The following articles will be included in our curriculum, so you can start reading them now

• Fast clustering algorithms for massive datasets
• Fast Combinatorial Feature Selection with New Definition of Predict...
• Jackknife logistic and linear regression for clustering and predict... *
• Discussion about outliers and criticizing charts *
• Learn experimental design with our live, real-time ongoing analysis *
• The best kept secret about linear and logistic regression *
• Attribution Modeling
• Internet Topology Mapping 
• Correlation and R-Squared for Big Data 
• From chaos to clusters - statistical modeling without models
• Example of architecture for AaaS (Analytics as a Service)
• Other source code posted on Analyticbridge
• Simple solutions to make videos with R
• What Map Reduce can't do
• The curse of big data
• Correlation vs. causation
• Three classes of metrics: centrality, volatility, and bumpiness
• Data Science dictionary
• Data Science tools
• A new random number generator
• 66 job interview questions for data scientists
• Data Science eBook (2nd Edition)
• The art of steganography 
• Eight worst predictive modeling techniques
• Hidden decision trees: a modern scoring methodology 
• Why and how to build a data dictionary for big data sets
• How to build simple, accurate, data-driven, model-free confidence i...
• A Couple Good Python Resources for Data Science
• When a data glitch turns great data into worthless gibberish
• How to detect a pattern? Problem and solution
• R in your Browser
• A new, fast Excel for big data
• Marrying computer science, statistics and domain expertize
• Building better search tools: problems and solutions
• The Data Science Formula and Clustering data scientists (student project)
• Detecting Patterns with the Naked Eye

List of potential projects for students:

• Click here for some suggestions

Starred items (*) are recent additions.

We are still in the process of writing our small booklet to teach you all the fundamentals (computer science, statistics, business analytics, Python, Map Reduce, big data etc.) in 20 pages. Also, feel free to check our Data Science eBook -  2nd Edition. A much more comprehensive, curated and easy-to-read version is published by Wiley (April 2014) and costs less than $30.