Data Analytics Profiling

 Day 1 - Understand the type of data

1. Primary Data & Secondary Data

2. Google Sheet Data (Quanlitative & Quantitative) 

   • Quanlitative: Binary/Nominal/Ordinal

   • Quantitative: Discrete/Continuous

3. Data Understanding

   • Labelled Data: Input data + Target

   • Unlabelled Data: Input data only

4. Category of data based on Structure

• Struture: Table Format

• Unstructured: Image/Audio/Emoji

• Semi Structure: Mix of structure & unstructured

5. Understand data analytics

• Descriptive: "what happened"

• Predictive: "What will happen in future?" (Forecasting)

• Diagnosis: "Why happen?"

• Prescriptive: "What action to be taken"

 Day 2 - Machine Learning/ Deep Learning/ AI

1. Machine Learning: all mechanical (basic / coding)

2. Deep Learning: Ask and respond

3. AI: Use machine learning to configure in the system

4. How we handle data?

   • Handle missing value: Less number? (remove it) / More number? (replace it)

   • Handle Duplicate: Drop/ Delete

   • Handle Outliers: > 10, replace it/  < 10, remove it (Boxplot can see the outliers)

5. Google Query (only for google sheet)

6. Google Visualization (Chart)

• Category VS Numerical = barplot/ line chart

• 2 Numerical = Scatter Plot

• Compare time series data = line chart

Day 3: Basic of R programming

1. Understand what is variable? Example: A10 (A is the variable)

2. Data Type in R

   • Trivial = Number: Integer/ Double/ Complex 

   • String = tolower(y)/ toupper(y)/ paste("Hi","Hello") = "Hi Hello"...

   • Vector = More than 1 value & All value should be all same type (Nurmeric/ Character/ Logical)

Day 4: R mini project

1. Understand all the basic coding such as: head/ tail/ $/ str/ summary/ nrow/ colnames/ ncol/ is.na/ colsums

Day 5: Recall and present our data cleaning project (University Ranking)

Day 6: Graphing & Seaborn 

Day 7: EDA & Introduction of Scikit Learn & Supervised Data 

Day 8: Unsupervised Data + Practice ML using titanic dataset + Project Development: Github/ Streamlit