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R programming and its application to data analysis and statistical methods
data-analysis-in-r-programming's Introduction
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
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