Day 1

1. Quantitative Data

   • Discrete

     • Whole number
     • Got by counting the values
     • Eg: 8, 10, 12, 231

   • Continuous

     • Decimal number
     • Got by measuring with something.
     • Eg: 3.2, 5.8

2. Qualitative Data

   • Binary

     • Have 2 values
     • Eg: Yes/No, True/false, Male/Female

   • Nominal

     • More than 2 values
     • Eg: Marital status 

   • Ordinal

     • ordered category
     • Eg: rating 1-5 satisfaction 

1. Descriptive (What happened)

2. Diagnostic (Why it happened)

3. Predictive (What will happened)

4. Prescriptive (What action to be taken)

Artificial Intelligence (AI) - Machines behave like human

Machine Learning (ML) - How to make the machine behave like human

Deep Learning (DL) - Make machine learn, understand and respond.

1. Get Data

2. Data Processing/Data Cleansing

   • Remove missing values
   • Identify outliers
3. Visualize the data
   • Convert data into chart/graph
   • Descriptive & Dianostic

1. Descriptive Statistics

   • Describe basic statistical values
   • Eg: Mean, mode, median, range, variance, standard deviation

2. Inferential Statistics

   • Hypothesis Testings/ assumptions
     1. Null Hypothesis- No statistical difference between groups/not enough evidence
     2. Alternative Hypothesis- Statistical difference between group
   • Techniques
     1. Min-max Scalling
     2. Standardizaion