Introduction to Data Science and Statistical Analytics
- Introduction to Data Science
- Use cases
- The need for Business Analytics
- Data Science Life Cycle
- Different tools available for Data Science
Introduction to R
- Installing R and R-Studio
- R packages and R Operators
- if statements and loops (for, while, repeat, break, next), switch case
Data Exploration, Data Wrangling, and R Data Structure
- Importing and Exporting data from an external source
- Data exploratory analysis
- R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List)
- Functions, Apply Functions
- Bar Graph (Simple, Grouped, Stacked)
- Pie Chart, Line Chart, Box (Whisker) Plot, Scatter Plot
Introduction to Statistics
- Terminologies of Statistics
- Measures of Centers, Measures of Spread
- Normal Distribution
- Binary Distribution
- Hypothesis Testing
- Chi-Square Test
Predictive Modeling – 1 ( Linear Regression)
- Supervised Learning – Linear Regression, Bivariate Regression, Multiple Regression Analysis, Correlation ( Positive, negative and neutral)
- Case Study
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
Predictive Modeling – 2 (Logistic Regression)
- What are Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- Random Forest
- What is Naive Bayes?
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- What is Canopy Clustering?
- What is Hierarchical Clustering?
Association Analysis and Recommendation engine
- Market Basket Analysis (MBA)
- Association Rules
- Apriori Algorithm for MBA
- Introduction of Recommendation Engine
- Types of Recommendation – User-Based and Item-Based
- Recommendation Use-case
- Introduction to Text Mining
- Introduction to Sentiment
- Setting up API Bridge, between R and Twitter Account
- Extracting Tweet from Twitter Acc
- Scoring the tweet
- What is Time Series data?
- Time Series variables
- Different components of Time Series data
- Visualize the data to identify Time Series Components
- Implement ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenario based on which different Exponential
- Smoothing model can be applied
- Implement respective ETS model for forecasting