Artificial Intelligence and Data Science

Course Content

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

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