Data Science is a term that the media has chosen to
minimize, obfuscate, and sometimes misuse. It involves a
lot more than just data and the science of working with
data. Today, the world uses Data Science in all sorts of
ways that you might not know about, which is why you
need Data Science Programming All-in-One For
Dummies
Computer science as an academic discipline began in the 1960’s. Emphasis was on
programming languages, compilers, operating systems, and the mathematical theory that
supported these areas. Courses in theoretical computer science covered finite automata,
regular expressions, context-free languages, and computability. In the 1970’s, the study
of algorithms was added as an important component of theory. The emphasis was on
making computers useful. Today, a fundamental change is taking place and the focus is
more on a wealth of applications. There are many reasons for this change. The merging
of computing and communications has played an important role. The enhanced ability
to observe, collect, and store data in the natural sciences, in commerce, and in other
fields calls for a change in our understanding of data and how to handle it in the modern
setting. The emergence of the web and social networks as central aspects of daily life
presents both opportunities and challenges for theory.
In writing this text my intention was to collect together in a single place
practical predictive modeling techniques, ideas and strategies that have been
proven to work but which are rarely taught in business schools, Data Science
courses or contained in any other single text.
This data set contains WWW-pages collected from computer science departments of various universities in January 1997 by the World Wide Knowledge Base (Web->Kb) project of the CMU text learning group. The 8,282 pages were manually classified into the following categories:
student (1641)
faculty (1124)
staff (137)
department (182)
course (930)
project (504)
other (3764)
As science advances, novel experiments are becoming more and more complex, requiring a zoo of control devices and electronics executing complicated sequences of steps. Device availability and monetary constrains usually lead to a highly heterogeneous setup with components from several different manufacturers using many different protocols and interfacing mechanisms. This often results in control software being puzzled together to use and provide a multitude of interfacing and control functionality, each using their own calling conventions, data structures, etc. To make matters worse, usually a group of relatively independent programmers is trying to write and maintain the code base. Often this causes extensive duplication of effort as program segments are hard to reuse, since unpredictable changes to the segments by the original authors might compromise other code using these segments.
