Reading notes from Data Science for Business

(p.14) Fundamental concept: Extracting useful knowledge from data to solve business problems can be treated systematically by following a process with reasonably well-defined stages.
(p.15) Fundamental concept: From a large mass of data, information technology can be used to find informative descriptive attributes of entities of interest.
(p.15) Fundamental concept: If you look too hard at a set of data, you will find something — but it might not generalize beyond the data you’re looking at. This is referred to as overfitting a dataset. Data mining techniques can be very powerful, and the need to detect and avoid overfitting is one of the most important concepts to grasp when applying data mining to real problems. The concept of overfitting and its avoidance permeates data science processes, algorithms, and evaluation methods.
(p.15) Fundamental concept: Formulating data mining solutions and evaluating the results involves thinking carefully about the context in which they will be used.
(p.20) A critical skill in data science is the ability to decompose a data-analytics problem into pieces such that each piece matches a known task for which tools are available. Recognizing familiar problems and their solutions avoids wasting time and resources reinventing the wheel. It also allows people to focus attention on more interesting parts of the process that require human involvement — parts that have not been automated, so human creativity and intelligence must come into play.
