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하루에 하나씩
Section 3: Machine Learning Pathway Overview 본문
- 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
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