Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics

 In  this article, I clarify the various roles of the data scientist, and  how data science compares and overlaps with related fields such as  machine learning, deep learning, AI, statistics, IoT, operations  research, and applied mathematics. As data science is a broad  discipline, I start by describing the different types of data scientists  that one may encounter in any business setting: you might even discover  that you are a data scientist yourself, without knowing it. As in any  scientific discipline, data scientists may borrow techniques from  related disciplines, though we have developed our own arsenal,  especially techniques and algorithms to handle very large unstructured  data sets in automated ways, even without human interactions, to perform  transactions in real-time or to make predictions.

1. Different Types of Data Scientists

To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article  where I compare data science with 16 analytic disciplines, also published in 2014.The following articles, published during the same time period, are still useful:

• Data Scientist versus Data Architect
• Data Scientist versus Data Engineer
• Data Scientist versus Statistician
• Data Scientist versus Business Analyst

More recently (August 2016)  Ajit Jaokar discussed Type A (Analytics) versus Type B (Builder) data scientist:

• The  Type A Data Scientist can code well enough to work with data but is not  necessarily an expert. The Type A data scientist may be an expert in  experimental design, forecasting, modelling, statistical inference, or  other things typically taught in statistics departments. Generally  speaking though, the work product of a data scientist is not "p-values  and confidence intervals" as academic statistics sometimes seems to  suggest (and as it sometimes is for traditional statisticians working in  the pharmaceutical industry, for example). At Google, Type A Data  Scientists are known variously as Statistician, Quantitative Analyst,  Decision Support Engineering Analyst, or Data Scientist, and probably a  few more.
• Type  B Data Scientist: The B is for Building. Type B Data Scientists share  some statistical background with Type A, but they are also very strong  coders and may be trained software engineers. The Type B Data Scientist  is mainly interested in using data "in production." They build models  which interact with users, often serving recommendations (products,  people you may know, ads, movies, search results). Source: click here.

I also wrote about the ABCD's of business processes optimization where  D stands for data science, C for computer science, B for business  science, and A for analytics science. Data science may or may not  involve coding or mathematical practice, as you can read in my article  on low-level versus high-level data science.  In a startup, data scientists generally wear several hats, such as  executive, data miner, data engineer or architect, researcher,  statistician, modeler (as in predictive modeling) or developer. 

Continue reading here: Difference between Machine Learning and Data Science