Conclusions: Data Science for Business
Fundamental Concepts of Data Science
Best described as combination of analytical engineering and exploration
Data science in organizations and competitive landscapes
attract, structure and future data science teams
data science as a competitive advantage
tactical principles for successful implementation
Thinking data-analytically
data mining process
collection of data science tasks
Extracting knowledge from data
identifying attributes
testing similarity and importance
modeling data
controlling complexity and balancing error and accuracy
Mining Model Device Data
Incorporating location into data mining techniques
Comparing mobile data habits to traditional online access data
Applying data-analytical problems to shifting market trends
Changing Solutions Business Problems
What is the problem?
Changes with what information can be extracted from data
All stakeholders need to be aligned with vision of problem
Hits/Misses of model
Even misses on targeted advertising can be successful secondhand since inputs include the interests of individuals affiliations
What Data Cant Do
Humans pick up on social subtleties and trends that are crucial in identifying problems and inputs in the data mining process
Computes can sort and quantify mass data and quantify the statistical significant variables
Be creative in human manor, use common sense
lack of professional intuition and decision making
not always sufficient data pertaining to the decision at hand
Privacy, Ethics, and Mining
Personal data proves to be effective for targeting advertising
Personal data allows for business to make more effective decisions on multiple fronts
People feel violated that their information is sold between parties