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About

The “Data Analytics” part of the title means: “Data Science, Data Mining, Text Mining, Machine Learning, Statistical Learning, Statistics, Analytics Modeling, Business Analytics, Knowledge Discovery, Soft Computing, Natural Language Processing, Data Aggregation, Econometrics, Visualization & related Programming: Descriptive -, Predictive -, & Prescriptive Analytics“. This means: Everything needed to derive something out of your data, with whatever tool, algorithm, technique, method or programming language necessary or appropriate to achieve this.

“R”, because R is a good choice to start and to get informed about the latest publications and to have a first look at the related algorithms and run them on your own data to find out if the new algorithm fits to the answer your derive from your data. But also, because R is a good choice to do analytical computation looking at the amount of available tools and API´s to existing tools and applications and freely available IDE (RStudio) for the most relevant OS´s.

“R provides a common language through which we can visit foreign disciplines and see the same statistical models from a different perspective.” Joel Cadwell (November 14, 2014)

“It is important to understand data science even if you never intend to do it yourself, because data analysis is now so critical to business strategy. Businesses increasingly are driven by data analytics, so there is great professional advantage in being able to interact competently with and within such businesses. Understanding the fundamental concepts, and having frameworks for organizing data-analytic thinking not only will allow one to interact competently, but will help to envision opportunities for improving data-driven decision-making, or to see data-oriented competitive threads.” Foster Provost & Tom Fawcett (2013)

“Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” H.G. Wells (1895)

“We need to remain constantly open to new mathematical models of machine learning.” Mirko Krivanek (March 5, 2015)

“You do not know how to model: Learn it dude! There is no short-cut to learning. Your organization needs to learn it, even yourself. Leverage every single person of your organization that has any glimps of experiences in dealing with the data. Combine that quant dude with a domain expert, let them fight and muddle through the journey. The organization needs it. So does you to learn how it bring value exactly to different internal clients.” Jeffrey Ng (December 26, 2014)

“The process we are interested in is the deployment of useful data driven models into production.” John Mount (April 19, 2013)

See also this overview “How Do I become a Data Scientist?

Michael Laux

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