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