# Course attended - Data Analytics Begins with me NUS [[18-02-2022]] Got introduced to https://orangedatamining.com/ Orange Very cool app! #data #knowledge The most important thing i think away: ==Types of Learning 1. Supervised (data with a target) 1. Regression (To predict continuous variables) 1. Linear Regression 2. Classification (to classify data into groups) 1. Logistic models 2. Decision Tree 3. Random Forest 2. Unsupervised 1. Cluster analysis: to find natural groupings 1. K-means clustering --- ==Questions i have from the course:== 1. What data do i come across at work? How to analyse them to make them useful? 2. What if we can do "market segmentation" of our patients population? What kind of patients correlate to treatment outcome? 1. Decision tree algorithm to make pattern? to classify things? e.g The machine decided that AGE is most important. This is valuable for us to classify patients. 3. Outliers can be useful information such as detecting anomalies. 4. Regarding "Classification" models, use case of detecting Cancers. For Cancer, there is a period the cancer have not become cancer yet right? at what point is it consider Cancer? Then at what point is it consider a Relapse? [[Questions related to applying data analytics to work]] ![[Data Analytics Begins with Me - NUS Course (1).pdf]] ![[DABWM_v1.2.pdf]]