Monthly Archives: February 2015

Where to get your Data Science Training or Apprenticeship

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I am frequently asked for suggestions regarding academic institutions, professional organizations, or MOOCs that provide Data Science training.  The following list will be updated occasionally (LAST UPDATED: 2018 March 29) .

Also, be sure to check out The Definitive Q&A for Aspiring Data Scientists and the story of my journey from Astrophysics to Data Science. If the latter story interests you, then here are a couple of related interviews: “Data Mining at NASA to Teaching Data Science at GMU“, and “Interview with Leading Data Science Expert“.

Here are a few places to check out:

  1. The Booz Allen Field Guide to Data Science
  2. Do you have what it takes to be a Data Scientist? (get the Booz Allen Data Science Capability Handbook)
  3. http://www.thisismetis.com/explore-data-science-online-training (formerly exploredatascience.com at Booz-Allen)
  4. http://www.thisismetis.com/
  5. https://www.teamleada.com/
  6. MapR Academy (offering Free Hadoop, Spark, HBase, Drill, Hive training and certifications at MapR)
  7. Data Science Apprenticeship at DataScienceCentral.com
  8. (500+) Colleges and Universities with Data Science Degrees
  9. List of Machine Learning Certifications and Best Data Science Bootcamps
  10. NYC Data Science Academy
  11. NCSU Institute for Advanced Analytics
  12. Master of Science in Analytics at Bellarmine University
  13. http://www.districtdatalabs.com/ (District Data Labs)
  14. http://www.dataschool.io/
  15. http://www.persontyle.com/school/ 
  16. http://www.galvanize.it/education/#classes (formerly Zipfian Academy) includes http://www.galvanizeu.com/ (Data Science, Statistics, Machine Learning, Python)
  17. https://www.coursera.org/specialization/jhudatascience/1
  18. https://www.udacity.com/courses#!/data-science 
  19. https://www.udemy.com/courses/Business/Data-and-Analytics/
  20. http://insightdatascience.com/ 
  21. Data Science Master Classes (at Datafloq)
  22. http://datasciencemasters.org
  23. http://www.jigsawacademy.com/
  24. https://intellipaat.com/
  25. http://www.athenatechacademy.com/ (Hadoop training, and more)
  26. O’Reilly Media Learning Paths
  27. http://www.godatadriven.com/training.html
  28. Courses for Data Pros at Microsoft Virtual Academy
  29. 18 Resources to Learn Data Science Online (by Simplilearn)
  30. Learn Everything About Analytics (by AnalyticsVidhya)
  31. Data Science Masters Degree Programs

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Follow Kirk Borne on Twitter @KirkDBorne

Big Data Growth — Compound Interest on Steroids

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(This article was originally published on BigDataRepublic.com in June 2013 — that site no longer exists.)

Could a simple math formula be responsible for all of modern civilization? An article in 2013 hypothesized that there is one, and the Formula for Compound Interest is it. The formula is actually quite straightforward, but the mathematical consequences are huge and potentially impossible to assimilate. Let us illustrate this with a simple example, and then we will see the consequences for the current Big Data revolution.

Assuming an annual period of compounding, if your principal (asset or debt) P grows at an annual rate R, then your net accumulation A after one year is P*(1+R). The accumulation A grows by an additional (1+R) factor for each additional year. Therefore, your accumulation after N years is equal to A=P*(1+R)N.

The fact that the number of compounding periods N is in the exponent of the compound interest growth formula means two things: (1) the growth rate is exponential (by definition); and (2) because the growth rate is exponential, the total accumulation A after a modest number of compounding periods can easily dwarf the initial value P, particularly for values of R equal to several percent per annum (or greater).

Many people have experienced the power of this compound interest growth through their own personal long-term retirement contributions. If you make a one-time investment of $5000 at age 20 (with no other contributions for the rest of your working career), then an annual return rate R=8% will yield a balance of $160,000 at 65 years old (a net gain of over 3000%).  If you make more modest but systematic contributions (for example $400 each year), then the final value of your retirement fund would also be $160,000 (from a total personal investment of $18,000 over 45 years – a net gain of 800%). This compound interest growth is amazing and impressive. Most people can understand these numbers and can relate them to normal life experience.

But consider what happens if the annual rate R is not a few percent, but double-digit or triple-digit percent. For example: if R=100%, then a $1 investment each year starting at age 20 would produce a net accumulation of $1024 after 10 years (from just $10 total personal investment). The net accumulation after 45 years at age 65 (from a total personal investment of $45) would equal $35,000,000,000,000 – that is, thirty-five trillion dollars! In this case, the mathematical consequences are enormous and too mind-boggling to comprehend. It is off-the-charts and unbelievable, and yet it is a mathematical certainty – the number (1+R) in the compound interest formula when R=100% is 2, and 245 is a truly huge number.

Finally, let us connect the original historical hypothesis to our current Big Data environment.  Some conservative estimates suggest that the world’s data volume doubles every year. That is a growth rate R=100%. Does that look familiar? Annual data-doubling corresponds to 210 times more data after every 10 years: from zettabytes now to geopbytes in a few decades (similar to investing $45 to get $35 trillion)!  The Big Data explosion is truly enormous growth on steroids! This is why Big Data is not simply “more data”, but it is something completely different, mind-boggling, and off-the-charts impossible to grasp. Nearly every government entity, corporate decision-maker, business strategist, marketing specialist, statistician, domain scientist, news service, digital publisher, and social media guru is talking “Big Data”. However, most of us involved in those conversations cannot begin to assimilate how the current growth in Big Data and a simple math formula will be responsible for radically transforming modern civilization all over again.

Therefore, don’t believe people when they say “We have always had Big Data!” That statement completely misses the point of today’s data revolution and trivializes the massive disruptive forces that are now transforming our digital world. Today’s big data is not yesterday’s big data!

Follow Kirk Borne on Twitter @KirkDBorne

Variety is the Spice of Life for Data Scientists

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“Variety is the spice of life,” they say.  And variety is the spice of data also: adding rich texture and flavor to otherwise dull numbers. Variety ranks among the most exciting, interesting, and challenging aspects of big data.  Variety is one of the original “3 V’s of Big Data” and is frequently mentioned in Big Data discussions, which focus too much attention on Volume.

A short conversation with many “old school technologists” these days too often involves them making the declaration: “We’ve always done big data.”  That statement really irks me… for lots of reasons.  I summarize in the following article some of those reasons:  “Today’s Big Data is Not Yesterday’s Big Data.” In a nutshell, those statements focus almost entirely on Volume, which is really missing the whole point of big data (in my humble opinion)… here comes the Internet of Things… hold onto your bits!

The greatest challenges and the most interesting aspects of big data appear in high-Velocity Big Data (requiring fast real-time analytics) and high-Variety Big Data (enabling the discovery of interesting patterns, trends, correlations, and features in high-dimensional spaces). Maybe because of my training as an astrophysicist, or maybe because scientific curiosity is a natural human characteristic, I love exploring features in multi-dimensional parameter spaces for interesting discoveries, and so should you!

Dimension reduction is a critical component of any solution dealing with high-variety (high-dimensional) data. Being able to sift through a mountain of data efficiently in order to find the key predictive, descriptive, and indicative features of the collection is a fundamental required data science capability for coping with Big Data.

Identifying the most interesting dimensions of the data is especially valuable when visualizing high-dimensional data. There is a “good news, bad news” perspective here. First, the bad news: the human capacity for seeing multiple dimensions is very limited: 3 or 4 dimensions are manageable; and 5 or 6 dimensions are possible; but more dimensions are difficult-to-impossible to assimilate. Now for the good news: the human cognitive ability to detect patterns, anomalies, changes, or other “features” in a large complex “scene” surpasses most computer algorithms for speed and effectiveness. In this case, a “scene” refers to any small-n projection of a larger-N parameter space of variables.

In data visualization, a systematic ordered parameter sweep through an ensemble of small-n projections (scenes) is often referred to as a “grand tour”, which allows a human viewer of the visualization sequence to see quickly any patterns or trends or anomalies in the large-N parameter space. Even such “grand tours” can miss salient (explanatory) features of the data, especially when the ratio N/n is large. Consequently, a data analytics approach that combines the best of both worlds (machine vision algorithms and human perception) will enable efficient and effective exploration of large high-dimensional data.

One such approach is to use statistical and machine learning techniques to develop “interestingness metrics” for high-variety data sets.  As such algorithms are applied to the data (in parameter sweeps or grand tours), they can discover and then present to the data end-user the most interesting and informative features (or combinations of features) in high-dimensional data: “Numbers are powerful, especially in interesting combinations.”

The outcomes of such exploratory data analyses are even more enhanced when the analytics tool ranks the output models (e.g., the data’s “most interesting parameters”) in order of significance and explanatory power (i.e., their ability to “explain” the complex high-dimensional patterns in the data).  Soft10’s “automatic statistician” Dr. Mo is a fast predictive analytics software package for exploring complex high-dimensional (high-variety) data.  Dr. Mo’s proprietary modeling and analytics techniques have been applied across many application domains, including medicine and health, finance, customer analytics, target marketing, nonprofits, membership services, and more. Check out Dr. Mo at http://soft10ware.com/ and read more here: http://soft10ware.com/big-data-complexity-requires-fast-modeling-technology/

Kirk Borne is a member of the Soft10, Inc. Board of Advisors.

Follow Kirk Borne on Twitter @KirkDBorne

Standards in the Big Data Analytics Profession

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A sign of maturity for most technologies and professions is the appearance of standards. Standards are used to enable, to promote, to measure, and perhaps to govern the use of that technology or the practice of that profession across a wide spectrum of communities. Standardization increases independent applications and comparative evaluations of the tools and practices of a profession.

Standards often apply to processes and codes of conduct, but standards also apply to digital content, including: (a) interoperable data exchange (such as GIS, CDF, or XML-based data standards); (b) data formats (such as ASCII or IEEE 754); (c) image formats (such as GIF or JPEG); (d) metadata coding standards (such as ICD-10 for the medical profession, or the Dublin Core for cultural, research, and information artifacts); and (e) standards for the sharing of models (such as PMML, the predictive model markup language, for data mining models).

Standards are ubiquitous.  This abundance causes some folks to quip: “The nice thing about standards is that there are so many of them.”  So, it should not be surprising to note that standards are now beginning to appear also in the worlds of big data and data science, providing evidence of the growing maturity of those professions…

(continue reading here … https://www.mapr.com/blog/raising-standard-big-data-analytics-profession)

Follow Kirk Borne on Twitter @KirkDBorne