Introduction to data analysis
Statistical theory. Statistical tools.
Hands-On:
- Basics Data Types, Creating Vectors. Coercion for Vectors
- Data Frame, Matrices, Creating Lists, Basic Graphics, Customizing Plots.
- Data wrangling Tidyverse, Tidyr (gather, spread)
- Data visualization (ggplot2 (providing the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use), Plotly, Leaflet
- Reporting (RMarkdown, Shiny)
- Overview of predictive analytics
- Data preparation
- Clustering,
- Modeling (linear models, k-means clustering)
- Support vector machine
- Random forest
Hands-On: Feature selection and model tuning
Hands-On: Time-series forecasting
- Association rules
- Dimensionality reduction
- Measuring performance
Total number of Hours : 40
