Statistics Training in Cambridge

This instructor-led, live training in Cambridge (online or onsite) is aimed at intermediate-level engineers and designers who wish to use DFSS to ensure that quality is built into the product or process from the start of the design phase.

By the end of this training, participants will be able to:

• Understand the principles and concepts of Design Six Sigma (DFSS).
• Learn the DMAIC (Define, Measure, Analyze, Improve, Control) and DMADV (Define, Measure, Analyze, Design, Verify) methodologies.
• Apply DFSS tools and techniques to design and optimise processes.
• Develop skills to manage and lead DFSS projects effectively.

This instructor-led, live training in Cambridge (online or onsite) is aimed at advanced-level professionals who wish to gain an in-depth understanding of the Six Sigma DMAIC methodology at a Black Belt level.

By the end of this training, participants will be able to:

• Develop a comprehensive understanding of the Define, Measure, Analyze, Improve, and Control phases with a focus on complex projects.
• Gain expertise in advanced statistical analysis techniques for data-driven decision-making.
• Lead large-scale Six Sigma projects and manage project teams effectively.
• Implement significant process improvements that align with organizational goals and strategies.

R Markdown is an authoring framework that combines executable R code with narrative text to create dynamic and reproducible documents.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level R users who wish to generate automated, dynamic reports using R Markdown for internal documentation, research publications, or web publishing.

By the end of this training, participants will be able to:

• Create R Markdown documents with embedded R code chunks and formatted text.
• Generate outputs in multiple formats including HTML, PDF, and Word.
• Use parameterized reports and conditional logic to generate dynamic content.
• Customize document appearance with themes, templates, and LaTeX for professional presentation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

This instructor-led, live training in Cambridge (online or onsite) is aimed at intermediate-level professionals who wish to have a comprehensive understanding of the DMAIC methodology and prepare for the role of an effective Six Sigma Green Belts.

By the end of this training, participants will be able to:

• Gain a thorough knowledge of the Define, Measure, Analyze, Improve, and Control phases of Six Sigma.
• Apply a wide range of Six Sigma tools and techniques effectively within each phase of DMAIC.
• Perform and interpret statistical analyses for process improvement.
• Plan, execute, and manage a Six Sigma project.

This instructor-led, live training in Cambridge (online or onsite) is aimed at beginner-level professionals who wish to have a fundamental understanding of Six Sigma and Lean principles to integrate them and improve business processes.

By the end of this training, participants will be able to:

• Obtain a solid understanding of Lean Six Sigma principles.
• Use Six Sigma methodologies to improve process quality by removing the causes of defects and minimizing variability.
• Integrate Lean and Six Sigma methodologies to achieve more efficient and effective process improvements.
• Teach basic Lean Six Sigma tools and techniques that Yellow Belts can apply in process improvement projects, such as 5S, Kaizen, and process mapping.

This instructor-led, live training in Cambridge (online or onsite) is aimed at intermediate-level professionals who wish to understand both Six Sigma and Lean principles in order to lead significant process improvement projects in their organizations.

By the end of this training, participants will be able to:

• Develop a comprehensive understanding of how Lean and Six Sigma methodologies can be integrated for process improvement.
• Gain in-depth knowledge and application skills in the Define, Measure, Analyze, Improve, and Control phases.
• Apply advanced statistical tools for data-driven decision-making and process analysis.
• Lead and manage Lean Six Sigma projects effectively.

This instructor-led, live training in Cambridge (online or onsite) is aimed at beginner-level professionals who wish to clean and analyze data, make statistical projections, and create insightful visualizations using these tools.

By the end of this training, participants will be able to:

• Understand the basics of Python, R, Power Query, and Power BI for data analysis.
• Clean and organize datasets using Python and Power Query.
• Perform statistical analysis and projections with R.
• Create professional dashboards and reports with Power BI.
• Integrate and analyze data from multiple sources effectively.

This instructor-led, live training in Cambridge (online or onsite) is aimed at advanced-level professionals who wish to gain the skills to handle complex Six Sigma projects and take a leadership role in Six Sigma and Lean implementations.

By the end of this training, participants will be able to:

• Develop an in-depth understanding of advanced Six Sigma and Lean methodologies and tools.
• Lead complex process improvement projects that align with organizational strategies.
• Perform complex statistical analyses and make data-driven decisions.
• Effectively lead change initiatives and build a culture of continuous improvement.

This instructor-led, live training in Cambridge (online or onsite) is aimed at beginner-level professionals who wish to have a deeper understanding of the Six Sigma methodology and its principles and master the DMAIC process.

By the end of this training, participants will be able to:

• Gain knowledge beyond the basic principles covered in the White Belt course.
• Master the DMAIC process and learn advanced Six Sigma tools.
• Learn how to effectively contribute to a Six Sigma project team and collaborate with Green and Black Belts.
• Apply Six Sigma methodologies to real-world business processes for improvement.

This instructor-led, live training in Cambridge (online or onsite) is aimed at beginner-level professionals who wish to have a basic understanding of the Six Sigma methodology and its principles.

By the end of this training, participants will be able to:

• Understand the basics of Six Sigma and learn the DMAIC process.
• Understand the specific roles and responsibilities of a White Belt in the context of a Six Sigma project.
• Learn how to apply Six Sigma principles to identify improvement opportunities and support Six Sigma projects within an organization.

This instructor-led, live training in Cambridge (online or onsite) is aimed at scientists who wish to learn and use the Design of Experiments (DoE) to understand the cause-and-effect relationship between multiple factors.

By the end of this training, participants will be able to:

• Understand the advantages of designed experiments over other approaches.
• Understand the cause-and-effect relationships and interactions between factors.
• Learn the best practices and guidelines for conducting successful experimentation.

This instructor-led, live training in Cambridge (online or onsite) is aimed at anyone who wishes to learn and master the fundamentals of econometric analysis and modeling.

By the end of this training, participants will be able to:

• Learn and understand the fundamentals of econometrics.
• Utilize Eviews and risk simulators.

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

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.

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

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 instructor-led, live 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.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Audience

Financial or market analysts, managers, accountants

Course Objectives

Facilitate and automate all kinds of financial analysis with Microsoft Excel

This instructor-led, live training in Cambridge (online or onsite) is aimed at data analysts who wish to program in R for Excel.

By the end of this training, participants will be able to:

• Toggle and move data between Excel and R.
• Use R Tidyverse and R features for data analytic solutions in Excel.
• Extend their data analytical skills by learning R.

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 optimise 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.

This instructor-led, live training (online or onsite) is aimed at HR professionals who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

This instructor-led, live training in Cambridge (online or onsite) is aimed at intermediate-level advanced R users who wish to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

By the end of this training, participants will be able to: create reusable functions, improve data workflows, debug and optimise code, and produce reproducible reports.

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

This instructor-led, live training in Cambridge (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.

By the end of this training, participants will be able to:

• Employ algorithms to buy and sell securities at specialised increments rapidly.
• Reduce costs associated with trade using algorithmic trading.
• Automatically monitor stock prices and place trades.

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.

This instructor-led, live training in Cambridge (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

By the end of this training, participants will be able to:

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

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.

This instructor-led, live training in Cambridge (online or onsite) is aimed at beginner-level data analysts who wish to use R programming to manipulate data, perform basic data analysis, and create compelling visualizations for insights.

By the end of this training, participants will be able to:

• Understand the basics of R Programming.
• Apply fundamental data science processes.
• Create visual representations of data.

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 instructor-led, live training in Cambridge (online or onsite) is aimed at data analysts who wish to program with R in SAS for cluster analysis.

By the end of this training, participants will be able to:

• Use cluster analysis for data mining
• Master R syntax for clustering solutions.
• Implement hierarchical and non-hierarchical clustering.
• Make data-driven decisions to help to improve business operations.

This instructor-led, live training in Cambridge (online or onsite) is aimed at beginner-level professionals who wish to gain a mastery of the fundamentals of R and how to work with data.

By the end of this training, participants will be able to:

• Understand the R programming environment and RStudio interface.
• Import, manipulate, and explore datasets using R commands and packages.
• Perform basic statistical analysis and data summarization.
• Generate visualizations using both base R and ggplot2.
• Manage workspaces, scripts, and packages effectively.

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.

This course is intended for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.

This instructor-led, live training in Cambridge (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME.

By the end of this training, participants will be able to:

• Plan, build, and deploy machine learning models in KNIME.
• Make data driven decisions for operations.
• Implement end to end data science projects.

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.

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.

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

By the end of this training, participants will be able to:

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quicly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

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.

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 instructor-led, live training in Cambridge (online or onsite) is aimed at professionals who wish to use R to perform statistical testing and data analysis.

By the end of this training, participants will be able to: install and configure R, perform statistical tests, interpret results, and apply analysis to real-world datasets.

This instructor-led, live training in Cambridge (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to perform statistical analysis using SPSS to interpret data accurately, run complex statistical tests, and generate meaningful insights.

By the end of this training, participants will be able to:

• Navigate the SPSS interface and manage datasets efficiently.
• Perform descriptive and inferential statistical analyses.
• Conduct t-tests, ANOVA, MANOVA, regression, and correlation analyses.
• Apply non-parametric tests, principal component analysis, and factor analysis for advanced data interpretation.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level analysts, researchers, and professionals who wish to use SPSS for data preparation, statistical analysis, and predictive modeling.

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.

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...

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)

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.

This instructor-led, live training in Cambridge (online or onsite) is aimed at advanced-level data professionals who wish to use Tableau to build complex visualizations, optimise dashboards, and perform advanced data analysis.

By the end of this training, participants will be able to: install and configure Tableau for advanced use, create advanced calculations, design interactive dashboards, and optimise performance.

This instructor-led, live training in Cambridge (online or onsite) is aimed at intermediate-level data professionals who wish to use Tableau to create advanced visualizations and dashboards for data-driven decision-making.

By the end of this training, participants will be able to: build advanced charts, use calculated fields and parameters, design interactive dashboards, and share insights effectively.

Audience

Format of the course

By the end of this training, participants will be able to:

In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.

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.

• Beginners to the R language
• Beginners to data analysis and data visualization

• Part lecture, part discussion, exercises and heavy hands-on practice

• 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

This instructor-led, live training in Cambridge (online or onsite) is aimed at intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.

By the end of this training, participants will be able to:

• Understand the fundamentals of forecasting techniques in R.
• Apply exponential smoothing and ARIMA models for time series analysis.
• Utilize the ‘forecast’ package to generate accurate forecasting models.
• Automate forecasting workflows for business and research applications.

Audience

• managers
• developers
• scientists
• students

Format of the course

on-line instruction and discussion OR face-to-face workshops

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 (https://www.r-project.org) both on a command line and in a graphical user interface (GUI).