Data Analytics With R programing
Description
Data fills all available space, and now that storage is cheap, the amount of data has exploded. However, all that information is useless without analysis and context. The R programming language is designed to make it easier to analyze and visualize massive amounts of data. For example, R provides the ability to multiply one block of variables by another—an assumption that provides inherent advantages over other languages. This course shows why R is ideal for high volumes of data, introduces more efficient ways to use the language, and explains how to avoid the problems and capitalize on the opportunities of big data. Learn how to determine if you have enough memory and processing power, produce visualizations of big data,
What will you learn
Requirements
- Basic knowledge of R
- Knowledge of the GGPlot2 package is recommended
- Knowledge of dataframes
- Knowledge of vectors and vectorized operations
Lessons
- 18 Lessons
- 26:13:53 Hours
- Introduction to Data Analytics with R Programming01:22:15
- Introduction to R and Data Structures of R01:23:19
- Apply and Import Function in R01:30:32
- Import Excel and Basic Plot in R01:28:07
- Bar Plot and Scatter Plot01:28:19
- Web Scraping01:28:53
- Visualizing Plots02:07:31
- More on Plots and Fancy Plots 01:33:31
- Simple Linear Regression02:18:02
- Linear Regression more Examples01:23:16
- Logistic Regression01:14:00
- K-mean Clustering 01:14:49
- Apriori Algorithm - Market Basket Analysis 01:39:39
- Decision Tree and Anova 01:31:13
- Multiple Linear Regression00:40:48
- Polynomial Regression01:21:03
- Data Mining and Sequence Pattern Matching 01:17:49
- R Shiny Application 01:10:47
About instructor
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