Data Mining, Machine Learning and Quantitative Programming: R and Python
Implementation of the following Data Mining/ Machine Learning methods:
- Linear Regression
- Generalized Linear Models
- Logistic Regression
- Decision Trees
- Random Forrest
- Gradient Boosted Machines
- Support Vector Machines
- Neural Networks
Implementation of the following Quantitative methods: R and Python
- Linear Programming
- Integer Programming
- Goal Programming
- Simulated Annealing
- Network Models
- Genetic Algorithms/ Programming
Data Preparation General Purpose Programming: R and Python
- Calculating Various Statistics and Math Calculations
- Calculating Probability Values
- Data Input/ Export
- Data Cleansing
- Data Wrangling and Data Subsetting
- Feature Engineering
- Applying summarization and Aggregate functions
Database: SQL
- Principals of Database Design
- Using SQL to Create, Update and Delete Tables
- Using SQL to Select a subset of Data
- Using SQL to Join Tables
- Using SQL to perform various Aggregate Functions
Visualization: R/Tableau/Microsoft Power BI
- Using R "ggplot" for explanatory analysis and communicating the insights
- Using R "Shiny" for interactive visualization and dash boarding
- Using Tableau for explanatory analysis and communicating the insights
Software Repository and Development Platforms: Github/Git
- Creating a new repository
- Fork and Push changes to a repository
- Clone a public project
- Send a pull request/ Merge changes from a pull request
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