1496646 
Data Analytics With R 
21 hours 
R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data.
Audience
Developers / data analytics
Duration
3 days
Format
Lectures and Handson
Day One: Language Basics
Course Introduction
About Data Science
Data Science Definition
Process of Doing Data Science.
Introducing R Language
Variables and Types
Control Structures (Loops / Conditionals)
R Scalars, Vectors, and Matrices
Defining R Vectors
Matricies
String and Text Manipulation
Character data type
File IO
Lists
Functions
Introducing Functions
Closures
lapply/sapply functions
DataFrames
Labs for all sections
Day Two: Intermediate R Programming
DataFrames and File I/O
Reading data from files
Data Preparation
Builtin Datasets
Visualization
Graphics Package
plot() / barplot() / hist() / boxplot() / scatter plot
Heat Map
ggplot2 package ( qplot(), ggplot())
Exploration With Dplyr
Labs for all sections
Day 3: Advanced Programming With R
Statistical Modeling With R
Statistical Functions
Dealing With NA
Distributions (Binomial, Poisson, Normal)
Regression
Introducing Linear Regressions
Recommendations
Text Processing (tm package / Wordclouds)
Clustering
Introduction to Clustering
KMeans
Classification
Introduction to Classification
Naive Bayes
Decision Trees
Training using caret package
Evaluating Algorithms
R and Big Data
Hadoop
Big Data Ecosystem
RHadoop
Labs for all sections

1572256 
R 
21 hours 
Day 1
Introduction and preliminaries
Making R more friendly, R and available GUIs
Rstudio
Related software and documentation
R and statistics
Using R interactively
An introductory session
Getting help with functions and features
R commands, case sensitivity, etc.
Recall and correction of previous commands
Executing commands from or diverting output to a file
Data permanency and removing objects
Simple manipulations; numbers and vectors
Vectors and assignment
Vector arithmetic
Generating regular sequences
Logical vectors
Missing values
Character vectors
Index vectors; selecting and modifying subsets of a data set
Other types of objects
Objects, their modes and attributes
Intrinsic attributes: mode and length
Changing the length of an object
Getting and setting attributes
The class of an object
Ordered and unordered factors
A specific example
The function tapply() and ragged arrays
Ordered factors
Arrays and matrices
Arrays
Array indexing. Subsections of an array
Index matrices
The array() function
Mixed vector and array arithmetic. The recycling rule
The outer product of two arrays
Generalized transpose of an array
Matrix facilities
Matrix multiplication
Linear equations and inversion
Eigenvalues and eigenvectors
Singular value decomposition and determinants
Least squares fitting and the QR decomposition
Forming partitioned matrices, cbind() and rbind()
The concatenation function, (), with arrays
Frequency tables from factors
Day 2
Lists and data frames
Lists
Constructing and modifying lists
Concatenating lists
Data frames
Making data frames
attach() and detach()
Working with data frames
Attaching arbitrary lists
Managing the search path
Data manipulation
Selecting, subsetting observations and variables
Filtering, grouping
Recoding, transformations
Aggregation, combining data sets
Character manipulation, stringr package
Reading data
Txt files
CSV files
XLS, XLSX files
SPSS, SAS, Stata,… and other formats data
Exporting data to txt, csv and other formats
Accessing data from databases using SQL language
Probability distributions
R as a set of statistical tables
Examining the distribution of a set of data
One and twosample tests
Grouping, loops and conditional execution
Grouped expressions
Control statements
Conditional execution: if statements
Repetitive execution: for loops, repeat and while
Day 3
Writing your own functions
Simple examples
Defining new binary operators
Named arguments and defaults
The '...' argument
Assignments within functions
More advanced examples
Efficiency factors in block designs
Dropping all names in a printed array
Recursive numerical integration
Scope
Customizing the environment
Classes, generic functions and object orientation
Statistical analysis in R
Linear regression models
Generic functions for extracting model information
Updating fitted models
Generalized linear models
Families
The glm() function
Classification
Logistic Regression
Linear Discriminant Analysis
Unsupervised learning
Principal Components Analysis
Clustering Methods( kmeans, hierarchical clustering, kmedoids)
Survival analysis
Survival objects in r
KaplanMeier estimate
Confidence bands
Cox PH models, constant covariates
Cox PH models, timedependent covariates
Graphical procedures
Highlevel plotting commands
The plot() function
Displaying multivariate data
Display graphics
Arguments to highlevel plotting functions
Basic visualisation graphs
Multivariate relations with lattice and ggplot package
Using graphics parameters
Graphics parameters list
Automated and interactive reporting
Combining output from R with text
Creating html, pdf documents 
2929318 
Predictive Modelling with R 
14 hours 
Problems facing forecasters
Customer demand planning
Investor uncertainty
Economic planning
Seasonal changes in demand/utilization
Roles of risk and uncertainty
Time series Forecasting
Seasonal adjustment
Moving average
Exponential smoothing
Extrapolation
Linear prediction
Trend estimation
Stationarity and ARIMA modelling
Econometric methods (casual methods)
Regression analysis
Multiple linear regression
Multiple nonlinear regression
Regression validation
Forecasting from regression
Judgemental methods
Surveys
Delphi method
Scenario building
Technology forecasting
Forecast by analogy
Simulation and other methods
Simulation
Prediction market
Probabilistic forecasting and Ensemble forecasting

1366 
Market Forecasting 
14 hours 
Audience
This course has been created for analysts, forecasters wanting to introduce or improve forecasting which can be related to sale forecasting, economic forecasting, technology forecasting, supply chain management and demand or supply forecasting.
Description
This course guides delegates through series of methodologies, frameworks and algorithms which are useful when choosing how to predict the future based on historical data.
It uses standard tools like Microsoft Excel or some Open Source programs (notably R project).
The principles covered in this course can be implemented by any software (e.g. SAS, SPSS, Statistica, MINITAB ...)
Problems facing forecasters
Customer demand planning
Investor uncertainty
Economic planning
Seasonal changes in demand/utilization
Roles of risk and uncertainty
Time series methods
Moving average
Exponential smoothing
Extrapolation
Linear prediction
Trend estimation
Growth curve
Econometric methods (casual methods)
Regression analysis using linear regression or nonlinear regression
Autoregressive moving average (ARMA)
Autoregressive integrated moving average (ARIMA)
Econometrics
Judgemental methods
Surveys
Delphi method
Scenario building
Technology forecasting
Forecast by analogy
Simulation and other methods
Simulation
Prediction market
Probabilistic forecasting and Ensemble forecasting
Reference class forecasting

2929322 
Data Mining & Machine Learning with R 
14 hours 
Introduction to Data mining and Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
BiasVariance tradeoff
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Dicriminant analysis
Logistic regression
KNearest neighbors
Support Vector Machines
Neural networks
Decision trees
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans
Advanced topics
Ensemble models
Mixed models
Boosting
Examples
Multidimensional reduction
Factor Analysis
Principal Component Analysis
Examples

2929326 
R Programming for Data Analysis 
14 hours 
This course is part of the Data Scientist skill set (Domain: Data and Technology)
Introduction and preliminaries
Making R more friendly, R and available GUIs
Rstudio
Related software and documentation
R and statistics
Using R interactively
An introductory session
Getting help with functions and features
R commands, case sensitivity, etc.
Recall and correction of previous commands
Executing commands from or diverting output to a file
Data permanency and removing objects
Simple manipulations; numbers and vectors
Vectors and assignment
Vector arithmetic
Generating regular sequences
Logical vectors
Missing values
Character vectors
Index vectors; selecting and modifying subsets of a data set
Other types of objects
Objects, their modes and attributes
Intrinsic attributes: mode and length
Changing the length of an object
Getting and setting attributes
The class of an object
Arrays and matrices
Arrays
Array indexing. Subsections of an array
Index matrices
The array() function
The outer product of two arrays
Generalized transpose of an array
Matrix facilities
Matrix multiplication
Linear equations and inversion
Eigenvalues and eigenvectors
Singular value decomposition and determinants
Least squares fitting and the QR decomposition
Forming partitioned matrices, cbind() and rbind()
The concatenation function, (), with arrays
Frequency tables from factors
Lists and data frames
Lists
Constructing and modifying lists
Concatenating lists
Data frames
Making data frames
attach() and detach()
Working with data frames
Attaching arbitrary lists
Managing the search path
Data manipulation
Selecting, subsetting observations and variables
Filtering, grouping
Recoding, transformations
Aggregation, combining data sets
Character manipulation, stringr package
Reading data
Txt files
CSV files
XLS, XLSX files
SPSS, SAS, Stata,… and other formats data
Exporting data to txt, csv and other formats
Accessing data from databases using SQL language
Probability distributions
R as a set of statistical tables
Examining the distribution of a set of data
One and twosample tests
Grouping, loops and conditional execution
Grouped expressions
Control statements
Conditional execution: if statements
Repetitive execution: for loops, repeat and while
Writing your own functions
Simple examples
Defining new binary operators
Named arguments and defaults
The '...' argument
Assignments within functions
More advanced examples
Efficiency factors in block designs
Dropping all names in a printed array
Recursive numerical integration
Scope
Customizing the environment
Classes, generic functions and object orientation
Graphical procedures
Highlevel plotting commands
The plot() function
Displaying multivariate data
Display graphics
Arguments to highlevel plotting functions
Basic visualisation graphs
Multivariate relations with lattice and ggplot package
Using graphics parameters
Graphics parameters list
Automated and interactive reporting
Combining output from R with text

2929330 
Big Data & Database Systems Fundamentals 
14 hours 
The course is part of the Data Scientist skill set (Domain: Data and Technology).
Data Warehousing Concepts
What is Data Ware House?
Difference between OLTP and Data Ware Housing
Data Acquisition
Data Extraction
Data Transformation.
Data Loading
Data Marts
Dependent vs Independent data Mart
Data Base design
ETL Testing Concepts:
Introduction.
Software development life cycle.
Testing methodologies.
ETL Testing Work Flow Process.
ETL Testing Responsibilities in Data stage.
Big data Fundamentals
Big Data and its role in the corporate world
The phases of development of a Big Data strategy within a corporation
Explain the rationale underlying a holistic approach to Big Data
Components needed in a Big Data Platform
Big data storage solution
Limits of Traditional Technologies
Overview of database types
NoSQL Databases
Hadoop
Map Reduce
Apache Spark 
2623 
Marketing Analytics using R 
21 hours 
Audience:
Business owners (marketing managers, product managers, customer base managers) and their teams; customer insights professionals.
Overview:
The course follows the customer life cycle from acquiring new customers, managing the existing customers for profitability, retaining good customers, and finally understanding which customers are leaving us and why. We will be working with real (if anonymous) data from a variety of industries including telecommunications, insurance, media, and high tech.
Format:
Instructorled training over the course of five halfday sessions with inclass exercises as well as homework. It can be delivered as a classroom or distance (online) course.
Part 1: Inflow  acquiring new customers
Our focus is direct marketing so we will not look at advertising campaigns but instead focus on understanding marketing campaigns (e.g. direct mail). This is the foundation for almost everything else in the course.
We look at measuring and improving campaign effectiveness. including:
The importance of test and control groups. Universal control group.
Techniques: Lift curves, AUC
Return on investment. Optimizing marketing spend.
Part 2: Base Management: managing existing customers
Considering the cost of acquiring new customers for many businesses there are probably few assets more valuable than their existing customer base, though few think of it in this way. Topics include:
1. Crossselling and upselling: Offering the right product or service to the customer at the right time.
Techniques: RFM models. Multinomial regression.
b. Value of lifetime purchases.
2. Customer segmentation: Understanding the types of customers that you have.
Classification models using first simple decision trees, and then
random forests and other, newer techniques.
Part 3: Retention: Keeping your good customers
Understanding which customers are likely to leave and what you can do about it is key to profitability in many industries, especially where there are repeat purchases or subscriptions. We look at propensity to churn models, including
Logistic regression: glm (package stats) and newer techniques (especially gbm as a general tool)
Tuning models (caret) and introduction to ensemble models.
Part 4: Outflow: Understanding who are leaving and why
Customers will leave you – that is a fact of life. What is important is to understand who are leaving and why. Is it low value customers who are leaving or is it your best customers? Are they leaving to competitors or because they no longer need your products and services? Topics include:
Customer lifetime value models: Combining value of purchases with propensity to churn and the cost of servicing and retaining the customer.
Analysing survey data. (Generally useful, but we will do a brief introduction here in the context of exit surveys.)

85063 
Training Neural Network in R 
14 hours 
This course is an introduction to applying neural networks in real world problems using Rproject software.
Introduction to Neural Networks
What are Neural Networks
What is current status in applying neural networks
Neural Networks vs regression models
Supervised and Unsupervised learning
Overview of packages available
nnet, neuralnet and others
differences between packages and itls limitations
Visualizing neural networks
Applying Neural Networks
Concept of neurons and neural networks
A simplified model of the brain
Opportunities neuron
XOR problem and the nature of the distribution of values
The polymorphic nature of the sigmoidal
Other functions activated
Construction of neural networks
Concept of neurons connect
Neural network as nodes
Building a network
Neurons
Layers
Scales
Input and output data
Range 0 to 1
Normalization
Learning Neural Networks
Backward Propagation
Steps propagation
Network training algorithms
range of application
Estimation
Problems with the possibility of approximation by
Examples
OCR and image pattern recognition
Other applications
Implementing a neural network modeling job predicting stock prices of listed

287766 
Programming with Big Data in R 
21 hours 
Introduction to Programming Big Data with R (bpdR)
Setting up your environment to use pbdR
Scope and tools available in pbdR
Packages commonly used with Big Data alongside pbdR
Message Passing Interface (MPI)
Using pbdR MPI 5
Parallel processing
Pointtopoint communication
Send Matrices
Summing Matrices
Collective communication
Summing Matrices with Reduce
Scatter / Gather
Other MPI communications
Distributed Matrices
Creating a distributed diagonal matrix
SVD of a distributed matrix
Building a distributed matrix in parallel
Statistics Applications
Monte Carlo Integration
Reading Datasets
Reading on all processes
Broadcasting from one process
Reading partitioned data
Distributed Regression
Distributed Bootstrap

287807 
Machine Learning Fundamentals with R 
14 hours 
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
BiasVariance tradeoff
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans

287823 
Data Mining with R 
14 hours 
Sources of methods
Artificial intelligence
Machine learning
Statistics
Sources of data
Pre processing of data
Data Import/Export
Data Exploration and Visualization
Dimensionality Reduction
Dealing with missing values
R Packages
Data mining main tasks
Automatic or semiautomatic analysis of large quantities of data
Extracting previously unknown interesting patterns
groups of data records (cluster analysis)
unusual records (anomaly detection)
dependencies (association rule mining)
Data mining
Anomaly detection (Outlier/change/deviation detection)
Association rule learning (Dependency modeling)
Clustering
Classification
Regression
Summarization
Frequent Pattern Mining
Text Mining
Decision Trees
Regression
Neural Networks
Sequence Mining
Frequent Pattern Mining
Data dredging, data fishing, data snooping 
287842 
Numerical Methods 
14 hours 
This course is for data scientists and statisticians that have some familiarity with numerical methods and have at least one programming language from R, Python, Octave, and some C++ options. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose of this course is to give a practical introduction in numerical methods to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
Topics Covered:
curve fitting
regression robust regression
linear algebra: matrix operations
eigenvalue/eigenvectormatrix decompositions
ordinary & partial differential equations
fourier analysis
interpolation & splines

287843 
Advanced R Programming 
7 hours 
This course is for data scientists and statisticians that already have basic R & C++ coding skills and R code and need advanced R coding skills.
The purpose is to give a practical advanced R programming course to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience
R's environment
Object oriented programming in R
S3
S4
Reference classes
Performance profiling
Exception handling
Debugging R code
Creating R packages
Unit testing
C/C++ coding in R
SEXPRs
Calling dynamically loaded libraries from R
Writing and compiling C/C++ code from R
Improving R's performance with C++ linear algebra library

1223204 
Building Web Applications in R with Shiny 
7 hours 
Description:
This is a course designed to teach R users how to create web apps without needing to learn crossbrowser HTML, Javascript, and CSS.
Objective:
Covers the basics of how Shiny apps work.
Covers all commonly used input/output/rendering/paneling functions from the Shiny library.
An overview of Shiny
Installation of Shiny for a local use
Basic Shiny concepts
Basic control accessories  Buttons, sliders, drop down menus
Program structure ui.r, server.r
Building first application
Running your application
Customizing interface
Html links in Shiny
JavaScript and Shiny
Advanced control accessories
Showing and Hiding elements of UI
Dynamic user interfaces
Advanced reactivity
Animation
Downloading uploading data
Sharing Shiny web applications
An overview of Shiny extensions

1223205 
Introductory R for Biologists 
28 hours 
I. Introduction and preliminaries
1. Overview
Making R more friendly, R and available GUIs
Rstudio
Related software and documentation
R and statistics
Using R interactively
An introductory session
Getting help with functions and features
R commands, case sensitivity, etc.
Recall and correction of previous commands
Executing commands from or diverting output to a file
Data permanency and removing objects
Good programming practice: Selfcontained scripts, good readability e.g. structured scripts, documentation, markdown
installing packages; CRAN and Bioconductor
2. Reading data
Txt files (read.delim)
CSV files
3. Simple manipulations; numbers and vectors + arrays
Vectors and assignment
Vector arithmetic
Generating regular sequences
Logical vectors
Missing values
Character vectors
Index vectors; selecting and modifying subsets of a data set
Arrays
Array indexing. Subsections of an array
Index matrices
The array() function + simple operations on arrays e.g. multiplication, transposition
Other types of objects
4. Lists and data frames
Lists
Constructing and modifying lists
Concatenating lists
Data frames
Making data frames
Working with data frames
Attaching arbitrary lists
Managing the search path
5. Data manipulation
Selecting, subsetting observations and variables
Filtering, grouping
Recoding, transformations
Aggregation, combining data sets
Forming partitioned matrices, cbind() and rbind()
The concatenation function, (), with arrays
Character manipulation, stringr package
short intro into grep and regexpr
6. More on Reading data
XLS, XLSX files
readr and readxl packages
SPSS, SAS, Stata,… and other formats data
Exporting data to txt, csv and other formats
6. Grouping, loops and conditional execution
Grouped expressions
Control statements
Conditional execution: if statements
Repetitive execution: for loops, repeat and while
intro into apply, lapply, sapply, tapply
7. Functions
Creating functions
Optional arguments and default values
Variable number of arguments
Scope and its consequences
8. Simple graphics in R
Creating a Graph
Density Plots
Dot Plots
Bar Plots
Line Charts
Pie Charts
Boxplots
Scatter Plots
Combining Plots
II. Statistical analysis in R
1. Probability distributions
R as a set of statistical tables
Examining the distribution of a set of data
2. Testing of Hypotheses
Tests about a Population Mean
Likelihood Ratio Test
One and twosample tests
ChiSquare GoodnessofFit Test
KolmogorovSmirnov OneSample Statistic
Wilcoxon SignedRank Test
TwoSample Test
Wilcoxon Rank Sum Test
MannWhitney Test
KolmogorovSmirnov Test
3. Multiple Testing of Hypotheses
Type I Error and FDR
ROC curves and AUC
Multiple Testing Procedures (BH, Bonferroni etc.)
4. Linear regression models
Generic functions for extracting model information
Updating fitted models
Generalized linear models
Families
The glm() function
Classification
Logistic Regression
Linear Discriminant Analysis
Unsupervised learning
Principal Components Analysis
Clustering Methods(kmeans, hierarchical clustering, kmedoids)
5. Survival analysis (survival package)
Survival objects in r
KaplanMeier estimate, logrank test, parametric regression
Confidence bands
Censored (interval censored) data analysis
Cox PH models, constant covariates
Cox PH models, timedependent covariates
Simulation: Model comparison (Comparing regression models)
6. Analysis of Variance
OneWay ANOVA
TwoWay Classification of ANOVA
MANOVA
III. Worked problems in bioinformatics
Short introduction to limma package
Microarray data analysis workflow
Data download from GEO: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
Data processing (QC, normalisation, differential expression)
Volcano plot
Custering examples + heatmaps

1841 
Introduction to R 
21 hours 
R is an opensource free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or finetune financial models. R has a wide variety of packages for data mining.
This course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (http://www.rproject.org) both on a command line and in a graphical user interface (GUI).
Introduction and preliminaries
Making R more friendly, R and available GUIs
The R environment
Related software and documentation
R and statistics
Using R interactively
An introductory session
Getting help with functions and features
R commands, case sensitivity, etc.
Recall and correction of previous commands
Executing commands from or diverting output to a file
Data permanency and removing objects
Simple manipulations; numbers and vectors
Vectors and assignment
Vector arithmetic
Generating regular sequences
Logical vectors
Missing values
Character vectors
Index vectors; selecting and modifying subsets of a data set
Other types of objects
Objects, their modes and attributes
Intrinsic attributes: mode and length
Changing the length of an object
Getting and setting attributes
The class of an object
Ordered and unordered factors
A specific example
The function tapply() and ragged arrays
Ordered factors
Arrays and matrices
Arrays
Array indexing. Subsections of an array
Index matrices
The array() function
Mixed vector and array arithmetic. The recycling rule
The outer product of two arrays
Generalized transpose of an array
Matrix facilities
Matrix multiplication
Linear equations and inversion
Eigenvalues and eigenvectors
Singular value decomposition and determinants
Least squares fitting and the QR decomposition
Forming partitioned matrices, cbind() and rbind()
The concatenation function, (), with arrays
Frequency tables from factors
Lists and data frames
Lists
Constructing and modifying lists
Concatenating lists
Data frames
Making data frames
attach() and detach()
Working with data frames
Attaching arbitrary lists
Managing the search path
Reading data from files
The read.table()function
The scan() function
Accessing builtin datasets
Loading data from other R packages
Editing data
Probability distributions
R as a set of statistical tables
Examining the distribution of a set of data
One and twosample tests
Grouping, loops and conditional execution
Grouped expressions
Control statements
Conditional execution: if statements
Repetitive execution: for loops, repeat and while
Writing your own functions
Simple examples
Defining new binary operators
Named arguments and defaults
The '...' argument
Assignments within functions
More advanced examples
Efficiency factors in block designs
Dropping all names in a printed array
Recursive numerical integration
Scope
Customizing the environment
Classes, generic functions and object orientation
Statistical models in R
Defining statistical models; formulae
Contrasts
Linear models
Generic functions for extracting model information
Analysis of variance and model comparison
ANOVA tables
Updating fitted models
Generalized linear models
Families
The glm() function
Nonlinear least squares and maximum likelihood models
Least squares
Maximum likelihood
Some nonstandard models
Graphical procedures
Highlevel plotting commands
The plot() function
Displaying multivariate data
Display graphics
Arguments to highlevel plotting functions
Lowlevel plotting commands
Mathematical annotation
Hershey vector fonts
Interacting with graphics
Using graphics parameters
Permanent changes: The par() function
Temporary changes: Arguments to graphics functions
Graphics parameters list
Graphical elements
Axes and tick marks
Figure margins
Multiple figure environment
Device drivers
PostScript diagrams for typeset documents
Multiple graphics devices
Dynamic graphics
Packages
Standard packages
Contributed packages and CRAN
Namespaces

2202 
R for Data Analysis and Research 
7 hours 
Audience
managers
developers
scientists
students
Format of the course
online instruction and discussion OR facetoface workshops
The list below gives an idea of the topics that will be covered in the workshop.
The number of topics that will be covered depends on the duration of the workshop (i.e. one, two or three days). In a one or two day workshop it may not be possible to cover all topics, and so the workshop will be tailored to suit the specific needs of the learners.
A first R session
Syntax for analysing one dimensional data arrays
Syntax for analysing two dimensional data arrays
Reading and writing data files
Subsetting data, sorting, ranking and ordering data
Merging arrays
Set membership
The main statistical functions in R
The Normal Distribution (correlation, probabilities, tests for normality and confidence intervals)
Ordinary Least Squares Regression
Ttests, Analysis of Variance and Multivariable Analysis of Variance
Chisquare tests for categorical variables
Writing functions in R
Writing software (scripts) in R
Control structures (e.g. Loops)
Graphical methods (including scatterplots, bar charts, pie charts, histograms, box plots and dot charts)
Graphical User Interfaces for R

2642 
Forecasting with R 
14 hours 
This course allows delegate to fully automate the process of forecasting with R
Forecasting with R
Introduction to Forecasting
Exponential Smoothing
ARIMA models
The forecast package
Package 'forecast'
accuracy
Acf
arfima
Arima
arima.errors
auto.arima
bats
BoxCox
BoxCox.lambda
croston
CV
dm.test
dshw
ets
fitted.Arima
forecast
forecast.Arima
forecast.bats
forecast.ets
forecast.HoltWinters
forecast.lm
forecast.stl
forecast.StructTS
gas
gold
logLik.ets
ma
meanf
monthdays
msts
na.interp
naive
ndiffs
nnetar
plot.bats
plot.ets
plot.forecast
rwf
seasadj
seasonaldummy
seasonplot
ses
simulate.ets
sindexf
splinef
subset.ts
taylor
tbats
thetaf
tsdisplay
tslm
wineind
woolyrnq
