Artificial Intelligence and Data Science

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

Data Visualization

• Bar Graph (Simple, Grouped, Stacked)
• Histogram
• Pie Chart, Line Chart, Box (Whisker) Plot, Scatter Plot
• Correlogram

Introduction to Statistics

• Terminologies of Statistics
• Measures of Centers, Measures of Spread
• Probability
• Normal Distribution
• Binary Distribution
• Hypothesis Testing
• Chi-Square Test
• ANOVA

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)

• Logistic Regression

Decision Trees

• 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

• Random Forest
• What is Naive Bayes?

Unsupervised learning

• 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

Sentiment Analysis

• Introduction to Text Mining
• Introduction to Sentiment
• Setting up API Bridge, between R and Twitter Account
• Extracting Tweet from Twitter Acc
• Scoring the tweet

Time Series

• 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