Statistics Training Courses

Statistics Training

Statistics is the practice of collecting and analyzing large sets of data by sampling parts of the whole and drawing inferences from the sample.

NobleProg onsite live Statistics training courses demonstrate through interactive discussion and hands-on practice how to apply Statistic principles to the solving of real-world problems.

Statistics training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local Statistics training can be carried out live on customer premises or in NobleProg local training centers.

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Statistics Course Outlines

Code Name Duration Overview
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.
surveyrste Survey Research, Sampling Techniques & Estimation 14 hours
spssanal Statistical Analysis using SPSS 21 hours SPSS is software for editing and analyzing data.
BigData_ A practical introduction to Data Analysis and Big Data 35 hours Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress.
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.
datashrinkgov Data Shrinkage for Government 14 hours
intror Introduction to R with Time Series Analysis 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 a wide variety of packages for data mining.
DatSci7 Data Science Programme 245 hours The explosion of information and data in today’s world is un-paralleled, our ability to innovate and push the boundaries of the possible is growing faster than it ever has. The role of Data Scientist is one of the highest in-demand skills across industry today. We offer much more than learning through theory; we deliver practical, marketable skills that bridge the gap between the world of academia and the demands of industry. This 7 week curriculum  can be tailored to your specific Industry requirements, please contact us for further information or visit the Nobleprog Institute website www.inobleprog.co.uk Audience: This programme is aimed post level graduates as well as anyone with the required pre-requisite skills which will be determined by an assessment and interview.  Delivery: Delivery of the course will be a mixture of Instructor Led Classroom and Instructor Led Online; typically the 1st week will be 'classroom led', weeks 2 - 6 'virtual classroom' and week 7  back to 'classroom led'.     
rneuralnet Neural Network in R 14 hours This course is an introduction to applying neural networks in real world problems using R-project software.
StaEcoMod Statistical and Econometric Modelling 21 hours
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  
mlentre Machine Learning Concepts for Entrepreneurs and Managers 21 hours This training course is for people that would like to apply Machine Learning in practical applications for their team.  The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience Investors and AI entrepreneurs Managers and Engineers whose company is venturing into AI space Business Analysts & Investors
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
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.
descstats Descriptive Statistics 14 hours This course will cover averages (mean, median, mode, proportions, etc), dispersions (variance, standard deviation, etc), contingency tables (cross tabs, etc), graphs/charts  
advr Advanced R 7 hours This course covers advanced topics in R programming.
danagr Data and Analytics - from the ground up 42 hours Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions:  What has happened? processing and analyzing data producing informative data visualizations What will happen? forecasting future performance evaluating forecasts What should happen? turning data into evidence-based business decisions optimizing processes The course itself can be delivered either as a 6 day classroom course or remotely over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
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.
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.
datacolmtd Data Collection Methods 14 hours
datascience Data Science Training 21 hours Data Science Training Aim: Obtaining the required knowledge for application of Data Science methods and also getting consultancy for establishing a Data Science team in an insurance company
kylin Apache Kylin: From classic OLAP to real-time data warehouse 14 hours Apache Kylin is an extreme, distributed analytics engine for big data. In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse. By the end of this training, participants will be able to: Consume real-time streaming data using Kylin Utilize Apache Kylin's powerful features, including snowflake schema support, a rich SQL interface, spark cubing and subsecond query latency Note We use the latest version of Kylin (as of this writing, Apache Kylin v2.0) Audience Big data engineers Big Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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...
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
rlang 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.
tbladv Tableau Advanced 14 hours Tableau helps people see and understand data.
tidyverse Introduction to Data Visualization with Tidyverse and R 7 hours The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble. In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse. By the end of this training, participants will be able to: Perform data analysis and create appealing visualizations Draw useful conclusions from various datasets of sample data Filter, sort and summarize data to answer exploratory questions Turn processed data into informative line plots, bar plots, histograms Import and filter data from diverse data sources, including Excel, CSV, and SPSS files Audience Beginners to the R language Beginners to data analysis and data visualization Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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)
67795 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.
bspkrintro R Fundamentals 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.
mrkfct 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 ...)
mlbankingr Machine Learning for Banking (with R) 28 hours In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects. Audience Developers Data scientists Banking professionals with a technical background Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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.
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.
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.
tableau1 Data analysis with Tableau 14 hours Tableau helps people see and understand data.
rforfinance R Programming for Finance 28 hours R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems. By the end of this training, participants will be able to: Understand the fundamentals of the R programming language Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.) Build applications that solve problems related to asset allocation, risk analysis, investment performance and more Troubleshoot, integrate deploy and optimize an R application Audience Developers Analysts Quants Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
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.
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.
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.
dsbda Data Science for Big Data Analytics 35 hours Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
intermediaterforfinance Intermediate R for Finance 21 hours R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn advanced programming concepts in R as they walk through coding in R using financial examples. By the end of this training, participants will be able to: Implement advanced R programming techniques Use R to manipulate their data to perform more advanced financial operations Audience Programmers Finance professionals IT Professionals Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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.
dataminr Data Mining with R 14 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 a wide variety of packages for data mining.
rintrob Introductory R for Biologists 28 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.
deepmclrg Machine Learning & Deep Learning with Python and R 14 hours
mlfinancer Machine Learning for Finance (with R) 28 hours Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: Understand the fundamental concepts in machine learning Learn the applications and uses of machine learning in finance Develop their own algorithmic trading strategy using machine learning with R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
bigdatar Programming with Big Data in R 21 hours
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
Piwik Getting started with Piwik 21 hours Audience Web analysist Data analysists Market researchers Marketing and sales professionals System administrators Format of course     Part lecture, part discussion, heavy hands-on practice
dlfinancewithr Deep Learning for Finance (with R) 28 hours Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use R to create deep learning models for finance Build their own deep learning stock price prediction model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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.
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.
bigddbsysfun Big Data & Database Systems Fundamentals 14 hours The course is part of the Data Scientist skill set (Domain: Data and Technology).
wolfdata Data Science: Analysis and Presentation 7 hours The Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.
dlforbankingwithpython Deep Learning for Banking (with Python) 28 hours Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use Python, Keras, and TensorFlow to create deep learning models for banking Build their own deep learning credit risk model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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.
rprogda R Programming for Data Analysis 14 hours This course is part of the Data Scientist skill set (Domain: Data and Technology)
datamodeling Pattern Recognition 35 hours This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience     Data analysts     PhD students, researchers and practitioners  
dlforbankingwithr Deep Learning for Banking (with R) 28 hours Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use R to create deep learning models for banking Build their own deep learning credit risk model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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.
dmmlr Data Mining & Machine Learning with R 14 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 a wide variety of packages for data mining.
kdd Knowledge Discover in Databases (KDD) 21 hours Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience     Data analysts or anyone interested in learning how to interpret data to solve problems Format of the course     After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
shinyrhtml Shiny, R and HTML: Merging Data Science and Web Development 7 hours Shiny is an open source R package that provides a web framework for building interactive web applications using R. In this instructor-led, live training, participants will learn how to combine data science and web development using Shiny, R, and HTML. By the end of this training, participants will be able to: Build interactive web applications with R using Shiny Audience Data scientists Web developers Statisticians Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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
ImpEvalQuatAnal Impact Evaluation – Quantitative Analysis 14 hours This course covers Impact evaluation and does not cover the broader design of evaluations.
predmodr Predictive Modelling with R 14 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 a wide variety of packages for data mining.
druid Druid: Build a fast, real-time data analysis system 21 hours Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo. In this course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment. Audience     Application developers     Software engineers     Technical consultants     DevOps professionals     Architecture engineers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
tableaupython Tableau with Python 14 hours Tableau is a business intelligence and data visualization tool. Python is a widely used programming language which provides support for a wide variety of statistical and machine learning techniques. Tableau's data visualization power and Python's machine learning capabilities, when combined, help developers rapidly build advanced data analytics applications for various business use cases. In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API. By the end of this training, participants will be able to: Integrate Tableau and Python using TabPy API Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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
ModelFore4Gov Modelling and Forecasting for Government 14 hours
tableauvra Visual Reporting and Analysis with Tableau 7 hours Tableau helps people see and understand data.
nlpwithr NLP: Natural Language Processing with R 21 hours It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data. This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements. By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance. Audience     Linguists and programmers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
rsttatan R for Statistical Analysis 14 hours Organization – Lab work and practical examples on real data
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).
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
frcr Forecasting with R 14 hours This course allows delegate to fully automate the process of forecasting with R

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Improve the quality of the items. Increase the number of items.;Improve the quality of the items Items that are either too easy so that almost everyone gets them correct or too difficult so that almost no one gets them correct are not good items: they provide very little information. # In most contexts, items which about half the people get correct are the best (other things being equal). Items that do not correlate with other items can usually be improved. Remove confusing and/or ambiguous items.