Section 3: Machine Learning Pathway Overview

• Let's discuss the general idea of a pathway of using Machine Learning and Data Science for a useful Real World Application.
• This overview is very broad and in reality is a lot of overlap between the various stages presented here.
• Note that we will also try to distinguish various roles in the process such as Data Engineer, Data Analytst, Data Scientist, Mchine Learning Researcher, etc. 
• Keep in mind there is also a lot of overlap in these roles and different organizations label role titles differently.
• Lastly keep in mind we cover all of these steps and topics in depth throughout the course, this is just a high level overview of general process and pathway that utilzies a machine leargning model.

ML Pathway 

 

Data Product: Mobile Apps, Services, Websites, etc. 

Problem to solve

How to fix or change X?

 

Data Analysis : Reports, Visualizations, Communications, etc. 

Question to Answer 

How deos a change in X affect Y?

 

Real World

 

Data Engineering ->

• Collect & Store Data 
  -  Raw Data : Physical Sensor, Surveys, Simulations, Experiments, Data Usage, etc. 
  -  Process & Store Data : SQL Database, CSV files, Excel, Cloud Storage

Data Analysis (Data Analyst / Data Scientist) -> 

• Clean & Organize Data
  - Reorganize Data, Dealing with Missing Data, Restructure Data, etc
• Exploratory Data Analysis 
  - Reorganize Data, Dealing with Missing Data, Restructure Data, etc

Machine Learning (Data Scientist/ Machine Learning Engineer) 

• Machine Learning Models -- Service, Dashboard, Application 
  - Supervised Learning: Predict an Outcome
  - Unsupervised Learning: Discover Patterns in Data 

Data Product: Predict Future Outcomes, Gain Insight on Data