R for Data Science Solutions Online Course

About the Course

R is a data analysis software as well as a programming language. Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data are cleared with R’s excellent data visualization feature.

The first section in this course deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the ‘dplyr’ and ‘data.table’ packages to efficiently process larger data structures. We also focus on ‘ggplot2’ and show you how to create advanced figures for data exploration.

In addition, you will learn how to build an interactive report using the “ggvis” package. Later sections offer insight into time series analysis, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.

By the end of this course, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.

Course Curriculum

Functions in R

• R Functions and Arguments
• Understanding Environments
• Working with Lexical Scoping
• Understanding Closure
• Performing Lazy Evaluation
• Creating Infix Operators
• Using the Replacement Function
• Handling Errors in a Function
• The Debugging Function

Data Extracting, Transforming, and Loading

• Downloading Open Data
• Reading and Writing CSV Files
• Scanning Text Files
• Working with Excel Files
• Reading Data from Databases
• Scraping Web Data

Data Pre-Processing and Preparation

• Renaming the Data Variable
• Converting Data Types
• Working with Date Format
• Adding New Records
• Filtering Data
• Dropping Data
• Merging and Sorting Data
• Reshaping Data
• Detecting Missing Data
• Imputing Missing Data

Data Manipulation

• Enhancing a data.frame with a data.table
• Managing Data with data.table
• Performing Fast Aggregation with data.table
• Merging Large Datasets with a data.table
• Subsetting and Slicing Data with dplyr
• Sampling Data with dplyr
• Selecting Columns with dplyr
• Chaining Operations in dplyr
• Arranging Rows with dplyr
• Eliminating Duplicated Rows with dplyr
• Adding New Columns with dplyr
• Summarizing Data with dplyr
• Merging Data with dplyr

Visualizing Data with ggplot2

• Creating Basic Plots with ggplot2
• Changing Aesthetics Mapping
• Introducing Geometric Objects
• Performing Transformations
• Adjusting Scales
• Faceting
• Adjusting Themes
• Combining Plots
• Creating Maps

Making Interactive Reports

• Creating R Markdown Reports
• Learning the Markdown Syntax
• Embedding R Code Chunks
• Creating Interactive Graphics with ggvis
• Understanding Basic Syntax and Grammar
• Controlling Axes and Legends and Using Scales
• Adding Interactivity to a ggvis Plot
• Creating an R Shiny Document
• Publishing an R Shiny Report

Simulation from Probability Distributions

• Generating Random Samples
• Understanding Uniform Distributions
• Generating Binomial Random Variates
• Generating Poisson Random Variates
• Sampling from a Normal Distribution
• Sampling from a Chi-Squared Distribution
• Understanding Student's t- Distribution
• Sampling from a Dataset
• Simulating the Stochastic Process

Statistical Inference in R

• Getting Confidence Intervals
• Performing Z-tests
• Performing Student's t-Tests
• Conducting Exact Binomial Tests
• Performing Kolmogorov-Smirnov Tests
• Working with the Pearson's Chi-Squared Tests
• Understanding the Wilcoxon Rank Sum and Signed Rank Tests
• Conducting One-way ANOVA
• Performing Two-way ANOVA

Rule and Pattern Mining with R

• Transforming Data into Transactions
• Displaying Transactions and Associations
• Mining Associations with the Apriori Rule
• Pruning Redundant Rules
• Visualizing Association Rules
• Mining Frequent Itemsets with Eclat
• Creating Transactions with Temporal Information
• Mining Frequent Sequential Patterns with cSPADE

Time Series Mining with R

• Creating Time Series Data
• Plotting a Time Series Object
• Decomposing Time Series
• Smoothing Time Series
• Forecasting Time Series
• Selecting an ARIMA Model
• Creating an ARIMA Model
• Forecasting with an ARIMA Model
• Predicting Stock Prices with an ARIMA Model

Supervised Machine Learning

• Fitting a Linear Regression Model with lm
• Summarizing Linear Model Fits
• Using Linear Regression to Predict Unknown Values
• Measuring the Performance of the Regression Model
• Performing a Multiple Regression Analysis
• Selecting the Best-Fitted Regression Model with Stepwise Regression
• Applying the Gaussian Model for Generalized Linear Regression
• Performing a Logistic Regression Analysis
• Building a Classification Model with Recursive Partitioning Trees
• Visualizing Recursive Partitioning Tree
• Measuring Model Performance with a Confusion Matrix
• Measuring Prediction Performance Using ROCR

Unsupervised Machine Learning

• Clustering Data with Hierarchical Clustering
• Cutting Tree into Clusters
• Clustering Data with the k-means Method
• Clustering Data with the Density-Based Method
• Extracting Silhouette Information from Clustering
• Comparing Clustering Methods
• Recognizing Digits Using the Density-Based Clustering Method
• Grouping Similar Text Documents with k-means Clustering Method
• Performing Dimension Reduction with Principal Component Analysis (PCA)
• Determining the Number of Principal Components Using a Scree Plot
• Determining the Number of Principal Components Using the Kaiser Method
• Visualizing Multivariate Data Using a biplot