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Data Shinobi: 3 - The Shrines & The Deities

In this post, we will look at the various 'Shrines' and 'Giants' on whose shoulders most modern Data Scientists stand. I am often daunted by the Job Descriptions people come up with for Data Scientists these days. 

A recent JD I encountered contained :-

  1. A lot of Techniques under - Statistical Modeling, Machine Learning, NLP, Visualization, Big Data, Databases...
  2. And the tools list was never ending - R, SAS, SPSS, CoreNLP, Cassandra, MongoDB, Mahout, Matlab, Python, NLTK, Cloudera, MapR, Tableau, Spotfire, Qlikview, Excel Macros, SQL, HDFS & Hive... almost every tool you've heard of.

Well, having a person to do the entire analytics stack is akin to having one person who can build a complete car from scratch. Thankfully, we are seeing a lot of sub-areas diverging under Data Science and slowly people are starting to understand that all cannot be placed in one bucket anymore.

This blog is an attempt at listing down the various areas, pioneers and some resources to delve deeper into these areas. Each of these areas has a lot of depth and takes time to even digest, leave alone master. Most of the details below, in conformity to this series, are based on my perception and my discussions with other Data Scientists.

Statistical Learning

Machine Learning

Natural Language Processing

Optimization

Other Resources

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Comment by Howard Drew on November 21, 2015 at 10:41pm
Thanks for the dojo data. I am going to have to catch up on the previous lessons

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