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Chapter 1: Data Analytic Thinking (Fundamental concepts (Extracting useful…
Chapter 1: Data Analytic Thinking
Data-Analytic Thinking
one should be able to assess whether and how data can improve performance.
seeing if the exploitation of data for a given situation makes sense
Data Science
A set of fundamental principles that guide the extraction of knowledge from data
The book lays out 4 fundamental concepts
Data-Driven Decision-Making (DDD)
The more data driven a firm is then statistically the company is more productive.
2 main types of decision:
1) Decisions for which "discoveries" need to be made within data
2) Decisions that repeat, especially at massive scale, and so decision-making can benefit from even small increases in accuracy
Data Mining
The Extraction of knowledge of data, via technologies that incorporate these principles.
Big Data
Often referred to as data sets that are too large for traditional data processing systems.
Big Data 1.0
Current Era of Data usage
Firms are building the capabilities to process large data
Big Data 2.0
An expected phase of what's to come
Questions that will be asked during the phase consist of: " what can we do now that we couldn't before? what can be improved on?
Fundamental concepts
From a large mass of data, information technology can be used to find informative descriptive attributes of entities of interest
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
Extracting useful knowledge from data to solve business problems can be treated systematically by following a process with reasonably well-defined stages
CRISP-DM (Cross Industry Standard Process for Data Mining)
Formulating data mining solutions and evaluating the results involves thinking carefully about the context in which they will be used.