R Programming for Data Science

Vrije Universiteit Amsterdam - Artificial Intelligence - Experimental Design and Data Analysis:

• Designing experiments and analyze the results according to the design,
• Analyzing data using the common ANOVA designs,
• Analyzing data using linear regression or a generalized linear regression model,
• Performing basic nonparametric tests,
• Performing bootstrap and permutation tests.
• Summarizing data;
• Basics of probability theory;
• Estimating means and fractions;
• Hypothesis testing for one- and two-sample problems about means and proportions;
• Correlation and linear regression;
• Contingency tables.

Data Science and Machine Learning:

• Programming with R
• Advanced R Features
• Using R Data Frames to solve complex tasks
• Use R to handle Excel Files
• Web scraping with R
• Connect R to SQL
• Use ggplot2 for data visualizations
• Use plotly for interactive visualizations
• Machine Learning with R, including:
• Linear Regression
• K Nearest Neighbors
• K Means Clustering
• Decision Trees
• Random Forests
• Data Mining Twitter
• Neural Nets and Deep Learning
• Support Vectore Machines