| 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
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| 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.
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| 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 ...)
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| 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...
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| 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)
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| 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.
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| 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.
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| 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.
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| 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
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| sixsigmayb | Six Sigma Yellow Belt | 21 hours | Yellow Belt covers the basics of the Six Sigma Define Measure Analyse Improve Control (DMAIC) approach enabling delegates to take part and lead team based waste and defect reduction projects and initiatives. In addition emphasis is placed on applying the problem solving tools into daily roles.
At the end of the course you will be equipped to look at your immediate team and role, determine what can be improved and create a business improvement project on a selected opportunity that is aligned to customer requirements.
You will be able to analyse the process using visualization tools and identify the waste (non-value adding) components and work to eliminate these from the process. You will apply root cause analysis techniques to identify the underlying causes of defects in the process.
The course uses simulations, case study exercises and work based projects to enable delegates to 'learn through doing'.
Notes: This course has a minimum class size of 4. And if requested this course can be delivered in 2 days with some reductions to the course content and level of detail in some areas, notably Customer needs; Graphical analysis and Process handover.
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| 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.
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| rneuralnet | Neural Network in R | 14 hours | This course is an introduction to applying neural networks in real world problems using R-project software.
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| 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.
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| 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
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| 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.
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| sixsigmagb | Six Sigma Green Belt | 70 hours | Green Belts participate in and lead Lean and Six Sigma projects from within their regular job function. They can tackle projects as part of a cross functional team or projects scoped within their normal job.
Each session of Green Belt training is separated by 3 or 4 weeks when the Green Belts apply their training to their improvement projects. We recommend supporting the Green Belts on their projects in between training sessions and holding stage gate reviews along with leadership and Lean Six Sigma Champions to ensure DMAIC methodology is being rigorously applied.
Week 1 Foundation: covers the fundamentals of the Lean Six Sigma Define Measure Analyse Improve Control (DMAIC) approach enabling participants to take part and lead waste and defect reduction projects and initiatives.
Week 2 Practitioner: provides additional data analysis and lean tools for participants to lead well scoped process improvement projects related to their regular job function.
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| sixsigmabb | Six Sigma Black Belt | 84 hours | Six Sigma is a data driven approach that tackles variation to improve the performance of products, services and processes, combining practical problem solving and the best scientific approaches found in experimentation and optimisation of systems. The approach has been widely and successfully applied in industry, notably by Motorola, AlliedSignal & General Electric.
Black Belt is a qualification for improvement managers in a Six Sigma organisation. You will learn the tools and techniques to take an improvement project through the Define, Measure, Analyse, Improve and Control phases (DMAIC). These techniques include Process Mapping, Measurement System Evaluation, Regression Analysis, Design of Experiments, Statistical Tolerancing, Monte Carlo Simulation and Lean Thinking.
The content of the course takes the participants through the DMAIC phases as well as introducing subjects such as Lean Thinking, Design for Six Sigma and discussing important leadership issues and experiences in deploying a Six Sigma programme.
Week 1 Foundation: covers the fundamentals of the Lean Six Sigma Define Measure Analyse Improve Control (DMAIC) approach enabling participants to take part and lead waste and defect reduction projects and initiatives.
Week 2 Practitioner: provides additional data analysis and lean tools for participants to lead well scoped process improvement projects related to their regular job function.
Week 3 Expert: provides regression, design of experiment and data analysis techniques to enable participants to tackle complex problem solving projects that require understanding of the relationships between multiple variables.
The trainer has 16 years experience with Six Sigma and as well as leading the deployment of Six Sigma at a number of businesses he has trained and coached over 300 Black Belts. Here are a few comments from previous participants:
- “Probably the most valuable course I will ever pass”
- “The content was very well delivered. The examples very relevant. Thank you”
- “The course was excellent and I am able to use part of it to coach my lean teams here” (Company supervisor who attended with KTP associate)
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| 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.
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| 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.
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| bigdatar | Programming with Big Data in R | 21 hours | Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.
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| xcelsius | Xcelsius | 14 hours | Description:
In this Xcelsius Training course, students will use Xcelsius Present to create interactive visualizations for presenting complex data in a simple way, and to conduct analysis to make critical decisions. Students will also create complete dashboards that present business, project, and human resources information, all consolidated and presented in a user-friendly manner. Finally, students will publish dashboards into various file formats such as Adobe Flash, Microsoft Office PowerPoint, Adobe PDF, and also to the web.
Objectives:
Upon successful completion of this course, students will be able to:
- Explore the Xcelsius workspace and an already created dashboard.
- Create simple visualizations.
- Conduct data analysis using Xcelsius components that give dynamic functionality to the specified data.
- Create a Project Management dashboard.
- Create a dashboard to consolidate and present the Human Resources information of an organization.
- Finalize dashboards and export them to different file formats.
Audience:
This course is designed for professionals who conduct data analysis and need to present robust and timely data in an interactive display.
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| 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.
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| 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
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| 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.
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| 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.
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| 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.
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| StaEcoMod | Statistical and Econometric Modelling | 21 hours | The objective of the course is to enable participants to gain a mastery of the fundamentals of statistical and econometric modelling.
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| datashrinkgov | Data Shrinkage for Government | 14 hours | The objective of the course is to enable participants to gain a mastery of the fundamentals of data shrinkage for government.
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| surveyrste | Survey Research, Sampling Techniques & Estimation | 14 hours | This 2-day course provides a comprehensive demonstration of the fundamenals of survey research, sampling techniques & estimation.
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| 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.
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