R for Data Science Solutions
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VIDEO TUTORIAL R for Data Science Solutions

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Over 100 hands-on tasks to help you effectively solve real-world data problems using the most popular R packages and techniques

About this R for Data Science Solutions Video course

  • Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages
  • Understand how to apply useful data analysis techniques in R for real-world applications
  • An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis

Data Science Solutions with R In Detail

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.

What will you learn in this course?

Course plan
Chapter 1
Functions in R
Chapter 2
Data Extracting, Transforming, and Loading
Chapter 3
Data Pre-Processing and Preparation
Chapter 4
Data Manipulation
Chapter 5
Visualizing Data with ggplot2
Chapter 6
Making Interactive Reports
Chapter 7
Simulation from Probability Distributions

Detailed course plan

Chapter 1 : Functions in R
Lesson 1R Functions and Arguments
Lesson 2Understanding Environments
Lesson 3Working with Lexical Scoping
Lesson 4Understanding Closure
Lesson 5Performing Lazy Evaluation
Lesson 6Creating Infix Operators
Lesson 7Using the Replacement Function
Lesson 8Handling Errors in a Function
Lesson 9The Debugging Function
Chapter 2 : Data Extracting, Transforming, and Loading
Lesson 1Downloading Open Data
Lesson 2Reading and Writing CSV Files
Lesson 3Scanning Text Files
Lesson 4Working with Excel Files
Lesson 5Reading Data from Databases
Lesson 6Scraping Web Data
Chapter 3 : Data Pre-Processing and Preparation
Lesson 1Renaming the Data Variable
Lesson 2Converting Data Types
Lesson 3Working with Date Format
Lesson 4Adding New Records
Lesson 5Filtering Data
Lesson 6Dropping Data
Lesson 7Merging and Sorting Data
Lesson 8Reshaping Data
Lesson 9Detecting Missing Data
Lesson 10Imputing Missing Data
Chapter 4 : Data Manipulation
Lesson 1Enhancing a data.frame with a data.table
Lesson 2Managing Data with data.table
Lesson 3Performing Fast Aggregation with data.table
Lesson 4Merging Large Datasets with a data.table
Lesson 5Subsetting and Slicing Data with dplyr
Lesson 6Sampling Data with dplyr
Lesson 7Selecting Columns with dplyr
Lesson 8Chaining Operations in dplyr
Lesson 9Arranging Rows with dplyr
Lesson 10Eliminating Duplicated Rows with dplyr
Lesson 11Adding New Columns with dplyr
Lesson 12Summarizing Data with dplyr
Lesson 13Merging Data with dplyr
Chapter 5 : Visualizing Data with ggplot2
Lesson 1Creating Basic Plots with ggplot2
Lesson 2Changing Aesthetics Mapping
Lesson 3Introducing Geometric Objects
Lesson 4Performing Transformations
Lesson 5Adjusting Scales
Lesson 6Faceting
Lesson 7Adjusting Themes
Lesson 8Combining Plots
Lesson 9Creating Maps
Chapter 6 : Making Interactive Reports
Lesson 1Creating R Markdown Reports
Lesson 2Learning the Markdown Syntax
Lesson 3Embedding R Code Chunks
Lesson 4Creating Interactive Graphics with ggvis
Lesson 5Understanding Basic Syntax and Grammar
Lesson 6Controlling Axes and Legends and Using Scales
Lesson 7Adding Interactivity to a ggvis Plot
Lesson 8Creating an R Shiny Document
Lesson 9Publishing an R Shiny Report
Chapter 7 : Simulation from Probability Distributions
Lesson 1Generating Random Samples
Lesson 2Understanding Uniform Distributions
Lesson 3Generating Binomial Random Variates
Lesson 4Generating Poisson Random Variates
Lesson 5Sampling from a Normal Distribution
Lesson 6Sampling from a Chi-Squared Distribution
Lesson 7Understanding Student's t- Distribution
Lesson 8Sampling from a Dataset
Lesson 9Simulating the Stochastic Process
Chapter 8 : Statistical Inference in R
Lesson 1Getting Confidence Intervals
Lesson 2Performing Z-tests
Lesson 3Performing Student's t-Tests
Lesson 4Conducting Exact Binomial Tests
Lesson 5Performing Kolmogorov-Smirnov Tests
Lesson 6Working with the Pearson's Chi-Squared Tests
Lesson 7Understanding the Wilcoxon Rank Sum and Signed Rank Tests
Lesson 8Conducting One-way ANOVA
Lesson 9Performing Two-way ANOVA
Chapter 9 : Rule and Pattern Mining with R
Lesson 1Transforming Data into Transactions
Lesson 2Displaying Transactions and Associations
Lesson 3Mining Associations with the Apriori Rule
Lesson 4Pruning Redundant Rules
Lesson 5Visualizing Association Rules
Lesson 6Mining Frequent Itemsets with Eclat
Lesson 7Creating Transactions with Temporal Information
Lesson 8Mining Frequent Sequential Patterns with cSPADE
Chapter 10 : Time Series Mining with R
Lesson 1Creating Time Series Data
Lesson 2Plotting a Time Series Object
Lesson 3Decomposing Time Series
Lesson 4Smoothing Time Series
Lesson 5Forecasting Time Series
Lesson 6Selecting an ARIMA Model
Lesson 7Creating an ARIMA Model
Lesson 8Forecasting with an ARIMA Model
Lesson 9Predicting Stock Prices with an ARIMA Model
Chapter 11 : Supervised Machine Learning
Lesson 1Fitting a Linear Regression Model with lm
Lesson 2Summarizing Linear Model Fits
Lesson 3Using Linear Regression to Predict Unknown Values
Lesson 4Measuring the Performance of the Regression Model
Lesson 5Performing a Multiple Regression Analysis
Lesson 6Selecting the Best-Fitted Regression Model with Stepwise Regression
Lesson 7Applying the Gaussian Model for Generalized Linear Regression
Lesson 8Performing a Logistic Regression Analysis
Lesson 9Building a Classification Model with Recursive Partitioning Trees
Lesson 10Visualizing Recursive Partitioning Tree
Lesson 11Measuring Model Performance with a Confusion Matrix
Lesson 12Measuring Prediction Performance Using ROCR
Chapter 12 : Unsupervised Machine Learning
Lesson 1Clustering Data with Hierarchical Clustering
Lesson 2Cutting Tree into Clusters
Lesson 3Clustering Data with the k-means Method
Lesson 4Clustering Data with the Density-Based Method
Lesson 5Extracting Silhouette Information from Clustering
Lesson 6Comparing Clustering Methods
Lesson 7Recognizing Digits Using the Density-Based Clustering Method
Lesson 8Grouping Similar Text Documents with k-means Clustering Method
Lesson 9Performing Dimension Reduction with Principal Component Analysis (PCA)
Lesson 10Determining the Number of Principal Components Using a Scree Plot
Lesson 11Determining the Number of Principal Components Using the Kaiser Method
Lesson 12Visualizing Multivariate Data Using a biplot

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