R for Data Analysis and Research Training Course

Goal:

To master the skills required to work independently with SPSS for advanced applications, including the effective use of dialog boxes and command language syntax for selected analytical techniques.

Intended Audience:

This course is designed for analysts, researchers, scientists, students, and anyone seeking to gain proficiency in using the SPSS package at an advanced level and to learn selected statistical models. The training addresses universal analytical challenges and is tailored to specific industry contexts.

Audience:

This course is designed for IT professionals seeking a solution to store and process large datasets within a distributed system environment.

Goal:

To develop in-depth expertise in Hadoop cluster administration.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level data scientists and statisticians who wish to prepare data, build models, and effectively apply machine learning techniques within 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 visualise results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

This instructor-led, live training in New Zealand (online or on-site) is designed for scientists who wish to learn and apply the Design of Experiments (DoE) to understand the cause-and-effect relationships between multiple factors.

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

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

This instructor-led, live training in New Zealand (available online or on-site) is intended for intermediate-level engineers and designers who wish to apply DFSS principles to ensure quality is embedded into the product or process from the outset of the design phase.

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

• Understand the core principles and concepts of Design for 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 the skills needed to effectively manage and lead DFSS projects.

This instructor-led, live training in New Zealand (online or on-site) is designed for anyone wishing to learn and master the fundamentals of econometric analysis and modelling.

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

• Learn and understand the fundamentals of econometrics.
• Use Eviews and Risk Simulator.

Audience

Financial or market analysts, managers, accountants

Course Objectives

Facilitate and automate all kinds of financial analysis with Microsoft Excel

Audience

Analysts, researchers, scientists, graduates, students, and anyone keen to learn how to streamline statistical analysis using Microsoft Excel.

Course Objectives

This course will enhance your proficiency in both Excel and statistics, thereby boosting the effectiveness and efficiency of your work or research.

The course covers how to utilise the Analysis ToolPak in Microsoft Excel, apply statistical functions, and carry out fundamental statistical procedures. It also explains Excel's limitations and provides strategies to overcome them.

This instructor-led, live training in New Zealand (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.
• Utilise the 'forecast' package to generate accurate forecasting models.
• Automate forecasting workflows for business and research applications.

This instructor-led, live training (available online or on-site) is designed for HR professionals who wish to leverage analytical methods to enhance organisational performance. The course explores both qualitative and quantitative approaches, including empirical and statistical techniques.

Course Format

• Interactive lectures and group discussions.
• Abundant exercises and hands-on practice.

Course Customisation Options

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

R is an open-source, free programming language designed for statistical computing, data analysis, and graphics. It is increasingly adopted by managers and data analysts across both corporate and academic sectors. R offers a wide array of packages tailored for data mining tasks.

This course is intended for individuals who already have a background in data science and statistics. The explanations provided are designed to serve either as a refresher for those already familiar with the concepts or as an introduction for those with a suitable background.

Audience

This course has been developed for analysts and forecasters seeking to introduce or enhance forecasting capabilities in areas such as sales forecasting, economic forecasting, technology forecasting, supply chain management, and demand or supply planning.

Description

This course guides participants through a range of methodologies, frameworks, and algorithms that are valuable when selecting approaches to predict future outcomes based on historical data.

It utilises standard tools such as Microsoft Excel or certain open-source programs (notably the R Project).

The principles covered in this course can be implemented using any software (for example, SAS, SPSS, Statistica, MINITAB, etc.).

This instructor-led, live training in New Zealand (online or on-site) 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.

Audience

Course Format

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

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

The Tidyverse is a collection of versatile R packages for cleaning, processing, modelling, and visualising 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 visualisation

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

• Perform data analysis and create appealing visualisations
• Draw useful conclusions from various datasets of sample data
• Filter, sort and summarise 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