From http://blogs.msdn.com/b/brunoterkaly/archive/2014/07/24/fundamentals-of-machine-learning.aspx :
Excerpt:
How to think about the analytics spectrum
One great way to think about machine learning is to break down analytics into 3 questions:
- What happened?
- Historical
- What will happen?
- Predictive
- What should I do next?
- Prescriptive
How to think of the personas doing analytics
- The information worker
- Typically using a self-service approach using Power BI.
- Power BI for Office 365 is a self-service business intelligence (BI) solution delivered through Excel and Office 365 that provides information workers with data analysis and visualization capabilities to identify deeper business insights about their data
- IT professionals
- Involved in data transformation, data warehousing, creating data merchant cubes for analytics, and data modeling
- Work for GM's are directors
- Data scientists
- Deeply technical and skilled not just with code, but with mathematics, statistics, and probability
- Can use a variety of techniques to apply probability to predictions (ie, there is a 42% chance that prices will go up in the next 18 hours)
- Like Monte Carlo simulations, parameterizing the model
- What to look for in a data scientist
- Domain Knowledge
- Clear Understanding Of The Scientific Method
- Objectivity, Hypothesis, Validation, Transparency
- Strong in Math and Statistics
- Intellectual Curiosity and Critical Thinking
- Visualization and Communication
- Advanced Computing And Data Management
Academic backgrounds
If you were to go to school, went to study to be a data scientist, what courses would you take?
- Applied Mathematics
- Computer Science
- Econometrics
- Statistics
- Engineering
Industries that really benefit from that of science
- Financial Services
- Telecommunications
- Information Technology
- Manufacturing
- Utilities
- Healthcare
- Marketing