Very clear and information packed course, would definitely recommend it.

*Elsa Mitchell - Babcock International*

Practical Applied Statistics courses

Code | Name | Duration | Overview |
---|---|---|---|

datama | Data Mining and Analysis | 28 hours | Objective: Delegates be able to analyse big data sets, extract patterns, choose the right variable impacting the results so that a new model is forecasted with predictive results. Data preprocessing Data Cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Statistical inference Probability distributions, Random variables, Central limit theorem Sampling Confidence intervals Statistical Inference Hypothesis testing Multivariate linear regression Specification Subset selection Estimation Validation Prediction Classification methods Logistic regression Linear discriminant analysis K-nearest neighbours Naive Bayes Comparison of Classification methods Neural Networks Fitting neural networks Training neural networks issues Decision trees Regression trees Classification trees Trees Versus Linear Models Bagging, Random Forests, Boosting Bagging Random Forests Boosting Support Vector Machines and Flexible disct Maximal Margin classifier Support vector classifiers Support vector machines 2 and more classes SVM’s Relationship to logistic regression Principal Components Analysis Clustering K-means clustering K-medoids clustering Hierarchical clustering Density based clustering Model Assesment and Selection Bias, Variance and Model complexity In-sample prediction error The Bayesian approach Cross-validation Bootstrap methods |

rprogadv | 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 |

excelafd | Analysing Financial Data in Excel | 14 hours | Audience Financial or market analysts, managers, accountants Course Objectives Facilitate and automate all kinds of financial analysis with Microsoft Excel Advanced functions Logical functions Math and statistical functions Financial functions Lookups and data tables Using lookup functions Using MATCH and INDEX Advanced list management Validating cell entries Exploring database functions PivotTables and PivotCharts Creating Pivot Tables Calculated Item and Calculated Field Working with External Data Exporting and importing Exporting and importing XML data Querying external databases Linking to a database Linking to a XML data source Analysing online data (Web Queries) Analytical options Goal Seek Solver The Analysis ToolPack Scenarios Macros and custom functions Running and recording a macro Working with VBA code Creating functions Conditional formatting and SmartArt Conditional formatting with graphics SmartArt graphics |

rlang | 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 two-sample 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( k-means, hierarchical clustering, k-medoids) Survival analysis Survival objects in r Kaplan-Meier estimate Confidence bands Cox PH models, constant covariates Cox PH models, time-dependent covariates Graphical procedures High-level plotting commands The plot() function Displaying multivariate data Display graphics Arguments to high-level 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 |

statdm | Statistical Thinking for Decision Makers | 7 hours | This course has been created for decision makers whose primary goal is not to do the calculation and the analysis, but to understand them and be able to choose what kind of statistical methods are relevant in strategic planning of the organization. For example, a prospect participant needs to make decision how many samples needs to be collected before they can make the decision whether the product is going to be launched or not. If you need longer course which covers the very basics of statistical thinking have a look at 5 day "Statistics for Managers" training. What statistics can offer to Decision Makers Descriptive Statistics Basic statistics - which of the statistics (e.g. median, average, percentiles etc...) are more relevant to different distributions Graphs - significance of getting it right (e.g. how the way the graph is created reflects the decision) Variable types - what variables are easier to deal with Ceteris paribus, things are always in motion Third variable problem - how to find the real influencer Inferential Statistics Probability value - what is the meaning of P-value Repeated experiment - how to interpret repeated experiment results Data collection - you can minimize bias, but not get rid of it Understanding confidence level Statistical Thinking Decision making with limited information how to check how much information is enough prioritizing goals based on probability and potential return (benefit/cost ratio ration, decision trees) How errors add up Butterfly effect Black swans What is Schrödinger's cat and what is Newton's Apple in business Cassandra Problem - how to measure a forecast if the course of action has changed Google Flu trends - how it went wrong How decisions make forecast outdated Forecasting - methods and practicality ARIMA Why naive forecasts are usually more responsive How far a forecast should look into the past? Why more data can mean worse forecast? Statistical Methods useful for Decision Makers Describing Bivariate Data Univariate data and bivariate data Probability why things differ each time we measure them? Normal Distributions and normally distributed errors Estimation Independent sources of information and degrees of freedom Logic of Hypothesis Testing What can be proven, and why it is always the opposite what we want (Falsification) Interpreting the results of Hypothesis Testing Testing Means Power How to determine a good (and cheap) sample size False positive and false negative and why it is always a trade-off |

advr | Advanced R | 7 hours | Rstudio IDE Data manipulation with dplyr, tidyr, reshape2 Object oriented programming in R Performance profiling Exception handling Debugging R code Creating R packages Reproducible research with knitr and RMarkdown C/C++ coding in R Writing and compiling C/C++ code from R |

datacolmtd | Data Collection Methods | 14 hours | Method of data collection Survey design (including questionnaire and question design) Different types of surveys (cross-sectional/time series/panel) Measurement bias Framing bias Response bias Non-response analysis Methods used to help correct for bias (e.g. weighting) Data linkage (e.g. linking survey data with administrative data) Assessing data quality & validating data |

apacheh | Administrator Training for Apache Hadoop | 35 hours | Audience: The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment Goal: Deep knowledge on Hadoop cluster administration. 1: HDFS (17%) Describe the function of HDFS Daemons Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing. Identify current features of computing systems that motivate a system like Apache Hadoop. Classify major goals of HDFS Design Given a scenario, identify appropriate use case for HDFS Federation Identify components and daemon of an HDFS HA-Quorum cluster Analyze the role of HDFS security (Kerberos) Determine the best data serialization choice for a given scenario Describe file read and write paths Identify the commands to manipulate files in the Hadoop File System Shell 2: YARN and MapReduce version 2 (MRv2) (17%) Understand how upgrading a cluster from Hadoop 1 to Hadoop 2 affects cluster settings Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons Understand basic design strategy for MapReduce v2 (MRv2) Determine how YARN handles resource allocations Identify the workflow of MapReduce job running on YARN Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN. 3: Hadoop Cluster Planning (16%) Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster. Analyze the choices in selecting an OS Understand kernel tuning and disk swapping Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario 4: Hadoop Cluster Installation and Administration (25%) Given a scenario, identify how the cluster will handle disk and machine failures Analyze a logging configuration and logging configuration file format Understand the basics of Hadoop metrics and cluster health monitoring Identify the function and purpose of available tools for cluster monitoring Be able to install all the ecosystem components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Manager, Sqoop, Hive, and Pig Identify the function and purpose of available tools for managing the Apache Hadoop file system 5: Resource Management (10%) Understand the overall design goals of each of Hadoop schedulers Given a scenario, determine how the FIFO Scheduler allocates cluster resources Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN Given a scenario, determine how the Capacity Scheduler allocates cluster resources 6: Monitoring and Logging (15%) Understand the functions and features of Hadoop’s metric collection abilities Analyze the NameNode and JobTracker Web UIs Understand how to monitor cluster Daemons Identify and monitor CPU usage on master nodes Describe how to monitor swap and memory allocation on all nodes Identify how to view and manage Hadoop’s log files Interpret a log file |

mtstatda | Minitab for Statistical Data Analysis | 14 hours | The course is aimed at anyone interested in statistical analysis. It provides familiarity with Minitab and will increase the effectiveness and efficiency of your data analysis and improve your knowledge of statistics. Descriptive Statistics Normal Distribution Correlation Regression Trend analysis & forecasting Confidence intervals t-tests proportion tests variance tests Anova Chi Squared tests |

descstats | Descriptive Statistics | 7 hours | This course will cover averages (mean, median, mode, proportions, etc), dispersions (variance, standard deviation, etc), contingency tables (cross tabs, etc), graphs/charts Types of data Distributions Central tendency – mean, median, mode Measures of dispersion - variance, standard deviation Standard error Central Limit Theorem and Law of Large numbers Confidence intervals, p values Hypothesis testing, statistical significance Covariance and correlation Causal versus descriptive inference Stated versus revealed preference Choosing optimal sample size ex ante Output (tables and graphs) |

rneuralnet | Training Neural Network in R | 14 hours | This course is an introduction to applying neural networks in real world problems using R-project 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 |

StaEcoMod | Statistical and Econometric Modelling | 21 hours | The Nature of Econometrics and Economic Data Econometrics and models Steps in econometric modelling Types of economic data, time series, cross-sectional, panel Causality in econometric analysis Specification and Data Issues Functional form Proxy variables Measurement error in variables Missing data, outliers, influential observations Regression Analysis Estimation Ordinary least squares (OLS) estimators Classical OLS assumptions, Gauss Markov-Theorem Best Linear Unbiased Estimators Inference Testing statistical significance of parameters t-test(single, group) Confidence intervals Testing multiple linear restrictions, F-test Goodness of fit Testing functional form Missing variables Binary variables Testing for violation of assumptions and their implications: Heteroscedasticity Autocorrelation Multicolinearity Endogeneity Other Estimation techniques Instrumental Variables Estimation Generalised Least Squares Maximum Likelihood Generalised Method of Moments Models for Binary Response Variables Linear Probability Model Probit Model Logit Model Estimation Interpretation of parameters, Marginal Effects Goodness of Fit Limited Dependent Variables Tobit Model Truncated Normal Distribution Interpretation of Tobit Model Specification and Estimation Issues Time Series Models Characteristics of Time Series Decomposition of Time Series Exponential Smoothing Stationarity ARIMA models Co-Integration ECM model Predictive Analysis Forecasting, Planning and Goals Steps in Forecasting Evaluating Forecast Accuracy Redisual Diagnostics Prediction Intervals |

mrkanar | 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: Instructor-led training over the course of five half-day sessions with in-class 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. Cross-selling and up-selling: 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.) |

datavisR1 | Introduction to Data Visualization with R | 28 hours | This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions Day 1: overview of R programming introduction to data visualization scatter plots and clusters the use of noise and jitters Day 2: other type of 2D and 3D plots histograms heat charts categorical data plotting Day 3: plotting KPIs with data R and X charts examples dashboards parallel axes mixing categorical data with numeric data Day 4: different hats of data visualization disguised and hidden trends case studies saving plots and loading Excel files |

datashrinkgov | Data Shrinkage for Government | 14 hours | Why shrink data Relational databases Introduction Aggregation and disaggregation Normalisation and denormalisation Null values and zeroes Joining data Complex joins Cluster analysis Applications Strengths and weaknesses Measuring distance Hierarchical clustering K-means and derivatives Applications in Government Factor analysis Concepts Exploratory factor analysis Confirmatory factor analysis Principal component analysis Correspondence analysis Software Applications in Government Predictive analytics Timelines and naming conventions Holdout samples Weights of evidence Information value Scorecard building demonstration using a spreadsheet Regression in predictive analytics Logistic regression in predictive analytics Decision Trees in predictive analytics Neural networks Measuring accuracy Applications in Government |

advspsspas | Advanced Statistics using SPSS Predictive Analytics Software | 28 hours | Goal: Mastering the skill work independently with the program SPSS for advanced use, dialog boxes, and command language syntax for the selected analytical techniques. The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and advanced level and learn the selected statistical models. Training takes universal analysis problems and it is dedicated to a specific industry Preparation of a database for analysis management of data collection operations on variables transforming the variables selected functions (logarithmic, exponential, etc.) Parametric and nonparametric statistics, or how to fit a model to the data measuring scale distribution type outliers and influential observations (outliers) sample size central limit theorem Study the differences between the characteristics of statistical tests based on the average and media Analysis of correlation and similarities correlations principal component analysis cluster analysis Prediction - single regression analysis and multivariate method of least squares Linear Model instrumental variable regression models (dummy, effect, orthogonal coding) Statistical Inference |

intror | Introduction to R with Time Series Analysis | 21 hours | 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 two-sample 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 High-level plotting commands The plot() function Displaying multivariate data Display graphics Arguments to high-level plotting functions Basic visualisation graphs Multivariate relations with lattice and ggplot package Using graphics parameters Graphics parameters list 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 non-linear regression Regression validation Forecasting from regression |

surveyrste | Survey Research, Sampling Techniques & Estimation | 14 hours | Survey research: Principle of sample survey design and implementation survey preliminaries sampling methods (probability & non-probability methods) population & sampling frames survey data collection methods Questionnaire design Design and writing of questionnaires Pre-tests & piloting Planning & organisation of surveys Minimising errors, bias & non-response at the design stage Survey data processing Commissioning surveys/research Sample Techniques & Estimation: Sampling techniques and their strengths/weaknesses (may overlap above sampling methods) Simple Random Sampling Unequal Probability Sampling Stratified Sampling (with proportional to size & disproportional selection) Systematic Sampling Cluster sampling Multi-stage Sampling Quota Sampling Estimation Methods of estimating sample sizes Estimating population parameters using sample estimates Variance and confidence intervals estimation Estimating bias/precision Methods of correcting bias Methods of handling missing data Non-response analysis |

sspsspas | Statistics with SPSS Predictive Analytics Software | 14 hours | Goal: Learning to work with SPSS at the level of independence The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques. Using the program The dialog boxes input / downloading data the concept of variable and measuring scales preparing a database Generate tables and graphs formatting of the report Command language syntax automated analysis storage and modification procedures create their own analytical procedures Data Analysis descriptive statistics Key terms: eg variable, hypothesis, statistical significance measures of central tendency measures of dispersion measures of central tendency standardization Introduction to research the relationships between variables correlational and experimental methods Summary: This case study and discussion |

spssanal | Statistical Analysis using SPSS | 21 hours | Getting started with SPSS Obtaining, Editing, and saving Statstical output Manipulating Data Descriptive Statistics Procedures Evaluating Score Distribution Assumptions t Tests Univariate Group Differences: Anova and Ancova Multivariate Group Dfferences: Manova Nonparametric procedures for ananlysing frequesncy data Correlations Regression with Quantitative Variables Regression with Categorical Variables Principal Components Analysys and Factor Analysis |

ModelFore4Gov | Modelling and Forecasting for Government | 14 hours | Modelling in government Hypothesis testing Why test a hypothesis? Type I and type II errors Estimating the tax gap Case studies using econometric & regression models in government Forecasting and time series models in government Shocks, trends and seasonality Forecasting tax receipts using regression and econometric modelling Sensitivity analysis and validation Prediction validation techniques. Hold out samples Prediction intervals Comparative analysis of forecasts Forecast Performance Measures |

samr | Statistic analysis in market research | 28 hours | Goal: Improving consumer behavior researcher workshop products and services Addressees The researchers, market analysts, managers and employees of marketing departments, sales departments primarily pharmaceutical and FMCG, students of socio-economic and everyone interested in market research Module 1 Quantitative research Pre-treatment results check the accuracy of the database control of missing data weighting observations Statistical models multiple regression conjoint analysis classification trees Automate procedures in tracking studies Analysis of data from a marketing experiment The report and draw conclusions Module 2 Qualitative Research The transformation of qualitative data into a quantitative Statistical models for qualitative data |

tableauvra | Visual Reporting and Analysis with Tableau | 7 hours | Connecting to Data Connecting to various databases – data connection types Multiple data sources & data blending Creating Basic Visualizations Sorting, Filtering, Organizing data Using Multiple Measures on the Same Axis Showing the Relationship between Numerical Values Mapping Data Geographically Tableau geocoding – advanced mapping + using Background Images Basic calculations and aggregations Parameters, references lines Overview of additional visualizations Dashboards: quick filters, actions, and parameters Advanced calculations Tips & tricks – parameters, calculations, sorting, filtering etc. Best practices when using Tableau |

ImpEvalQuatAnal | Impact Evaluation – Quantitative Analysis | 14 hours | This course covers Impact evaluation and does not cover the broader design of evaluations. Why evaluate The evaluation lifecycle Process and Impact evaluation Counterfactuals and baselines Exploring your options Randomised control trial Difference in differences (with practical exercise) Regression discontinuity design Propensity score matching Interrupted time series Instrumental variables |

predmodr | 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 non-linear 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 |

statsqa | Statistical Quality Analysis | 7 hours | This course covers the fundamentals of statistical process control and how these quality tools can provide the necessary evidence to improve and control processes. Know when and where to use the various types of control charts available in Minitab for your own processes. And learn how to use capability analysis tools to evaluate your processes. Gage R&R, Destructive Testing, Gage Linearity and Bias, Attribute Agreement, Variables and Attribute Control Charts, Capability Analysis for Normal, Non-normal and Attribute data |

dmmlr | Data Mining & Machine Learning with R | 14 hours | Introduction to Data mining and Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Dicriminant analysis Logistic regression K-Nearest neighbors Support Vector Machines Neural networks Decision trees Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means Advanced topics Ensemble models Mixed models Boosting Examples Multidimensional reduction Factor Analysis Principal Component Analysis Examples |

octaveda | Octave for Data Analysis | 14 hours | Audience: This course is for data scientists and statisticians that have some familiarity statistical methods and would like to use the Octave programming language at work. The purpose of this course is to give a practical introduction in Octave programming to participants interested in using this programming language at work. environment data types: numeric string, arrays matrices variables expressions control flow functions exception handling debugging input/output linear algebra optimization statistical distributions regression plotting |

pgmt | The Practitioner’s Guide to Multivariate Techniques | 14 hours | The introduction of the digital computer, and now the widespread availability of computer packages, has opened up a hitherto difficult area of statistics; multivariate analysis. Previously the formidable computing effort associated with these procedures presented a real barrier. That barrier has now disappeared and the analyst can therefore concentrate on an appreciation and an interpretation of the findings. Multivariate Analysis of Variance (MANOVA) Whereas the Analysis of Variance technique (ANOVA) investigates possible systematic differences between prescribes groups of individuals on a single variable, the technique of Multivariate Analysis of Variance is simply an extension of that procedure to numerous variates viewed collectively. These variates could be distinct in nature; for example Height, Weight etc, or repeated measures of a single variate over time or over space. When the variates are repeated measures over time or space, the analyses may often be reduced to a succession of univariate analyses, with easier interpretation. This procedure is often referred to as Repeated Measure Analysis. Principal Component Analysis If only two variates are recorded for a number of individuals, the data may conveniently be represented on a two-dimensional plot. If there are ‘p’ variates then one could imagine a plot of the data in ‘p’ dimensional space. The technique of Principal Component Analysis corresponds to a rotation of the axes so that the maximum amounts of variation are progressively represented along the new axes. It has been described as …….‘peering into multidimensional space, from every conceivable angle, and selecting as the viewing angle that which contains the maximum amount of variation’ The aim therefore is a reduction of the dimensionality of multivariate data. If for example a very high percentage (say 90%) of the variability is contained in the first two principal components, a plot of these components would be a virtually complete pictorial representation of the variability. Discriminant Analysis Suppose that several variates are observed on individuals from two identified groups. The technique of discriminant analysis involves calculating that linear function of the variates that best separates out the groups. The linear function may therefore be used to identify group membership simply from the pattern of variates. Various methods are available to estimate the success in general of this identification procedure. Canonical Variate Analysis Canonical Variate Analysis is in essence an extension of Discriminant Analysis to accommodate the situation where there are more than two groups of individuals. Cluster Analysis Cluster Analysis as the name suggests involves identifying groupings (or clusters) of individuals in multidimensional space. Since here there is no ‘a priori’ grouping of individuals, the identification of so called clusters is a subjective process subject to various assumptions. Most computer packages offer several clustering procedures that may often give differing results. However the pictorial representation of the so called ‘clusters’, in diagrams called dendrograms, provides a very useful diagnostic. Factor Analysis If ‘p’ variates are observed on each of ‘n’ individuals, the technique of factor analysis attempts to identify say ‘r’ (< p) so called factors which determine to a large extent the variate values. The implicit assumption here therefore is that the entire array of ‘p’ variates is controlled by ‘r’ factors. For example the ‘p’ variates could represent the performance of students in numerous examination subjects, and we wish to determine whether a few attributes such as numerical ability, linguistic ability could account for much of the variability. The difficulties here stem from the fact that the so-called factors are not directly observable, and indeed may not really exist. Factor analysis has been viewed very suspiciously over the years, because of the measure of speculation involved in the identification of factors. One popular numerical procedure starts with the rotation of axes using principal components (described above) followed by a rotation of the factors identified. |

rprogda | 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 two-sample 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 High-level plotting commands The plot() function Displaying multivariate data Display graphics Arguments to high-level 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 |

MLFWR1 | 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 Bias-Variance trade-off Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means |

statsres | Statistics for Researchers | 35 hours | This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research in a range of scientific disciplines. It covers some probability and statistical methods, mainly through examples. This training contains around 30% of lectures, 70% of guided quizzes and labs. In the case of closed course we can tailor the examples and materials to a specific branch (like psychology tests, public sector, biology, genetics, etc...) In the case of public courses, mixed examples are used. Though various software is used during this course (Microsoft Excel to SPSS, Statgraphics, etc...) its main focus is on understanding principles and processes guiding research, reasoning and conclusion. This course can be delivered as a blended course i.e. with homework and assignments. Scientific Method, Probability & Statistics Very short history of statistics Why can be "confident" about the conclusions Probability and decision making Preparation for research (deciding "what" and "how") The big picture: research is a part of a process with inputs and outputs Gathering data Questioners and measurement What to measure Observational Studies Design of Experiments Analysis of Data and Graphical Methods Research Skills and Techniques Research Management Describing Bivariate Data Introduction to Bivariate Data Values of the Pearson Correlation Guessing Correlations Simulation Properties of Pearson's r Computing Pearson's r Restriction of Range Demo Variance Sum Law II Exercises Probability Introduction Basic Concepts Conditional Probability Demo Gamblers Fallacy Simulation Birthday Demonstration Binomial Distribution Binomial Demonstration Base Rates Bayes' Theorem Demonstration Monty Hall Problem Demonstration Exercises Normal Distributions Introduction History Areas of Normal Distributions Varieties of Normal Distribution Demo Standard Normal Normal Approximation to the Binomial Normal Approximation Demo Exercises Sampling Distributions Introduction Basic Demo Sample Size Demo Central Limit Theorem Demo Sampling Distribution of the Mean Sampling Distribution of Difference Between Means Sampling Distribution of Pearson's r Sampling Distribution of a Proportion Exercises Estimation Introduction Degrees of Freedom Characteristics of Estimators Bias and Variability Simulation Confidence Intervals Exercises Logic of Hypothesis Testing Introduction Significance Testing Type I and Type II Errors One- and Two-Tailed Tests Interpreting Significant Results Interpreting Non-Significant Results Steps in Hypothesis Testing Significance Testing and Confidence Intervals Misconceptions Exercises Testing Means Single Mean t Distribution Demo Difference between Two Means (Independent Groups) Robustness Simulation All Pairwise Comparisons Among Means Specific Comparisons Difference between Two Means (Correlated Pairs) Correlated t Simulation Specific Comparisons (Correlated Observations) Pairwise Comparisons (Correlated Observations) Exercises Power Introduction Example Calculations Factors Affecting Power Exercises Prediction Introduction to Simple Linear Regression Linear Fit Demo Partitioning Sums of Squares Standard Error of the Estimate Prediction Line Demo Inferential Statistics for b and r Exercises ANOVA Introduction ANOVA Designs One-Factor ANOVA (Between-Subjects) One-Way Demo Multi-Factor ANOVA (Between-Subjects) Unequal Sample Sizes Tests Supplementing ANOVA Within-Subjects ANOVA Power of Within-Subjects Designs Demo Exercises Chi Square Chi Square Distribution One-Way Tables Testing Distributions Demo Contingency Tables 2 x 2 Table Simulation Exercises Case Studies Analysis of selected case studies |

bigddbsysfun | 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 |

bigdatar | 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 Point-to-point 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 |

statsman | Statistics for Managers | 35 hours | This course has been created for decision makers whose primary goal is not to do the calculation and the analysis, but to understand them. The course uses a lot of pictures, diagrams, computer simulations, anecdotes and sense of humour to explain concepts and pitfalls of statistics. Introduction to Statistics What are Statistics? Importance of Statistics Descriptive Statistics Inferential Statistics Variables Percentiles Measurement Levels of Measurement Basics of Data Collection Distributions Summation Notation Linear Transformations Common Pitfalls Biased samples Average, mean or median? Misleading graphs Semi-attached figures Third variable problem Ceteris paribus Errors in reasoning Understanding confidence level Understanding Results Describing Bivariate Data Probability Normal Distributions Sampling Distributions Estimation Logic of Hypothesis Testing Testing Means Power Prediction ANOVA Chi Square Case Studies Discussion about case studies chosen by the delegates. |

dataar | 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 Hands-on 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 Built-in 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 Connecting R to databases Big Data Ecosystem Labs for all sections |

dataminr | 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 semi-automatic 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 |

stats2 | Statistics Level 2 | 28 hours | This training course covers advanced statistics. It explains most of the tools commonly used in research, analysis and forecasting. It provides short explanations of the theory behind the formulas. This course does not relate to any specific field of knowledge, but can be tailored if all the delegates have the same background and goals. Some basic computer tools are used during this course (notably Excel and OpenOffice) Describing Bivariate Data Introduction to Bivariate Data Values of the Pearson Correlation Guessing Correlations Simulation Properties of Pearson's r Computing Pearson's r Restriction of Range Demo Variance Sum Law II Exercises Probability Introduction Basic Concepts Conditional Probability Demo Gamblers Fallacy Simulation Birthday Demonstration Binomial Distribution Binomial Demonstration Base Rates Bayes' Theorem Demonstration Monty Hall Problem Demonstration Exercises Normal Distributions Introduction History Areas of Normal Distributions Varieties of Normal Distribution Demo Standard Normal Normal Approximation to the Binomial Normal Approximation Demo Exercises Sampling Distributions Introduction Basic Demo Sample Size Demo Central Limit Theorem Demo Sampling Distribution of the Mean Sampling Distribution of Difference Between Means Sampling Distribution of Pearson's r Sampling Distribution of a Proportion Exercises Estimation Introduction Degrees of Freedom Characteristics of Estimators Bias and Variability Simulation Confidence Intervals Exercises Logic of Hypothesis Testing Introduction Significance Testing Type I and Type II Errors One- and Two-Tailed Tests Interpreting Significant Results Interpreting Non-Significant Results Steps in Hypothesis Testing Significance Testing and Confidence Intervals Misconceptions Exercises Testing Means Single Mean t Distribution Demo Difference between Two Means (Independent Groups) Robustness Simulation All Pairwise Comparisons Among Means Specific Comparisons Difference between Two Means (Correlated Pairs) Correlated t Simulation Specific Comparisons (Correlated Observations) Pairwise Comparisons (Correlated Observations) Exercises Power Introduction Factors Affecting Power Why power matters Exercises Prediction Introduction to Simple Linear Regression Linear Fit Demo Partitioning Sums of Squares Standard Error of the Estimate Prediction Line Demo Inferential Statistics for b and r Exercises ANOVA Introduction ANOVA Designs One-Factor ANOVA (Between-Subjects) One-Way Demo Multi-Factor ANOVA (Between-Subjects) Unequal Sample Sizes Tests Supplementing ANOVA Within-Subjects ANOVA Power of Within-Subjects Designs Demo Exercises Chi Square Chi Square Distribution One-Way Tables Testing Distributions Demo Contingency Tables 2 x 2 Table Simulation Exercises |

rintrob | 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: Self-contained 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 two-sample tests Chi-Square Goodness-of-Fit Test Kolmogorov-Smirnov One-Sample Statistic Wilcoxon Signed-Rank Test Two-Sample Test Wilcoxon Rank Sum Test Mann-Whitney Test Kolmogorov-Smirnov 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(k-means, hierarchical clustering, k-medoids) 5. Survival analysis (survival package) Survival objects in r Kaplan-Meier estimate, log-rank test, parametric regression Confidence bands Censored (interval censored) data analysis Cox PH models, constant covariates Cox PH models, time-dependent covariates Simulation: Model comparison (Comparing regression models) 6. Analysis of Variance One-Way ANOVA Two-Way 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 |

appliedml | Applied Machine Learning | 14 hours | This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Bayesian Graphical Models Factor Analysis (FA) Principal Component Analysis (PCA) Independent Component Analysis (ICA) Support Vector Machines (SVM) for regression and classification Boosting Ensemble models Neural networks Hidden Markov Models (HMM) Space State Models Clustering |

stats1 | Statistics Level 1 | 14 hours | This course has been created for people who require general statistics skills. This course can be tailored to a specific area of expertise like market research, biology, manufacturing, public sector research, etc... Introduction Descriptive Statistics Inferential Statistics Sampling Demonstration Variables Percentiles Measurement Levels of Measurement Measurement Demonstration Basics of Data Collection Distributions Summation Notation Linear Transformations Exercises Graphing Distributions Qualitative Variables Quantitative Variables Stem and Leaf Displays Histograms Frequency Polygons Box Plots Box Plot Demonstration Bar Charts Line Graphs Exercises Summarizing Distributions Central Tendency What is Central Tendency Measures of Central Tendency Balance Scale Simulation Absolute Difference Simulation Squared Differences Simulation Median and Mean Mean and Median Simulation Additional Measures Comparing measures Variability Measures of Variability Estimating Variance Simulation Shape Comparing Distributions Demo Effects of Transformations Variance Sum Law I Exercises Normal Distributions History Areas of Normal Distributions Varieties of Normal Distribution Demo Standard Normal Normal Approximation to the Binomial Normal Approximation Demo Exercises |

webappsr | 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 cross-browser 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 |

mlintro | Introduction to Machine Learning | 7 hours | This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Ridge regression Clustering |

excelstatsda | Excel For Statistical Data Analysis | 14 hours | Audience Analysts, researchers, scientists, graduates and students and anyone who is interested in learning how to facilitate statistical analysis in Microsoft Excel. Course Objectives This course will help improve your familiarity with Excel and statistics and as a result increase the effectiveness and efficiency of your work or research. This course describes how to use the Analysis ToolPack in Microsoft Excel, statistical functions and how to perform basic statistical procedures. It will explain what Excel limitation are and how to overcome them. Aggregating Data in Excel Statistical Functions Outlines Subtotals Pivot Tables Data Relation Analysis Normal Distribution Descriptive Statistics Linear Correlation Regression Analysis Covariance Analysing Data in Time Trends/Regression line Linear, Logarithmic, Polynomial, Power, Exponential, Moving Average Smoothing Seasonal fluctuations analysis Comparing Populations Confidence Interval for the Mean Test of Hypothesis Concerning the Population Mean Difference Between Mean of Two Populations ANOVA: Analysis of Variances Goodness-of-Fit Test for Discrete Random Variables Test of Independence: Contingency Tables Test Hypothesis Concerning the Variance of Two Populations Forecasting Extrapolation |

rintro | Introduction to R | 21 hours | R is an open-source 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 fine-tune 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.r-project.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 two-sample 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 non-standard models Graphical procedures High-level plotting commands The plot() function Displaying multivariate data Display graphics Arguments to high-level plotting functions Low-level 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 |

rdataana | R for Data Analysis and Research | 7 hours | Audience managers developers scientists students Format of the course on-line instruction and discussion OR face-to-face 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 Sub-setting 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 T-tests, Analysis of Variance and Multivariable Analysis of Variance Chi-square 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 |

frcr | 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 |

Course | Course Date | Course Price [Remote / Classroom] |
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Introduction to Machine Learning - Belfast City Centre | Wed, 2016-12-21 09:30 | £900 / £1150 |

The Practitioner’s Guide to Multivariate Techniques - Plymouth Drake Circus | Wed, 2016-12-21 09:30 | £1750 / £2050 |

Analysing Financial Data in Excel - London, Barbican | Thu, 2016-12-22 09:30 | £1750 / £2350 |

Administrator Training for Apache Hadoop - Birmingham | Mon, 2017-01-02 09:30 | £4450 / £5900 |

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Course | Venue | Course Date | Course Price [Remote / Classroom] |
---|---|---|---|

Applied Machine Learning | London, Barbican | Thu, 2017-03-30 09:30 | £1782 / £2382 |