R for Data Analysis and Research Training Course

Goal:

Achieve proficiency in working independently with the SPSS program for advanced applications, including dialog boxes and command language syntax, to apply selected analytical techniques.

Target Audience:

Analysts, researchers, scientists, students, and anyone wishing to develop the capability to utilize the SPSS package at an advanced level and learn specific statistical models. The training addresses universal analytical challenges and can be tailored to a specific industry.

Target Audience:

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

Learning Objectives:

Gain in-depth knowledge of Hadoop cluster administration.

This instructor-led, live training in Norway (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.

This instructor-led live training in Norway (online or onsite) is designed for scientists who wish to learn and apply 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:

• 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 Norway (online or onsite) targets intermediate-level engineers and designers who wish to apply DFSS to ensure quality is embedded in the product or process from the initial 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 optimize processes.
• Develop skills to manage and lead DFSS projects effectively.

This instructor-led, live training in Norway (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.

Audience

Financial or market analysts, managers, accountants

Course Objectives

Facilitate and automate all kinds of financial analysis with Microsoft Excel

Target Audience

This course is designed for analysts, researchers, scientists, graduates, and students, as well as any professionals interested in mastering statistical analysis techniques within Microsoft Excel.

Course Objectives

By the end of this course, you will have a stronger grasp of Excel's capabilities and statistical principles, thereby enhancing the effectiveness and efficiency of your work or research.

The curriculum covers the use of the Analysis ToolPak in Excel, various statistical functions, and fundamental statistical procedures. Additionally, it addresses common limitations in Excel and provides strategies to overcome them.

This instructor-led live training in Norway (online or onsite) is intended for intermediate-level data analysts and business professionals seeking to perform time series forecasting and automate data analysis workflows using R.

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of forecasting techniques in R.
• Apply exponential smoothing and ARIMA models for time series analysis.
• Leverage the 'forecast' package to create accurate forecasting models.
• Automate forecasting workflows for business and research applications.

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 Customization Options

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

R is an open-source, free programming language designed for statistical computing, data analysis, and graphical visualization. It is increasingly utilized by managers and data analysts within both corporate and academic sectors. R offers a vast array of packages for data mining.

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

Audience

This course is designed for analysts and forecasters who wish to initiate or enhance their forecasting capabilities. It covers areas such as sales forecasting, economic forecasting, technology forecasting, supply chain management, and demand or supply forecasting.

Description

Participants will be guided through a series of methodologies, frameworks, and algorithms that are essential for predicting future trends based on historical data.

The course utilizes standard tools such as Microsoft Excel or various Open Source programs, particularly the R project.

The principles taught in this course can be applied across any software platform, including SAS, SPSS, Statistica, MINITAB, and others.

This instructor-led, live training in Norway (online or onsite) is designed for beginner to intermediate-level professionals who aim to perform statistical analysis using SPSS to accurately interpret data, execute complex statistical tests, and derive 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.

Target Audience

Course Format

Upon completing this training, participants will be capable of:

In this instructor-led, live training session, attendees will acquire the skills to manipulate and visualize data using the tools provided within the Tidyverse.

The Tidyverse comprises a suite of flexible R packages designed for data cleaning, processing, modeling, and visualization. Key packages include: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Individuals new to the R programming language
• Those new to data analysis and visualization techniques

• A blend of lectures, discussions, exercises, and extensive practical, hands-on practice

• Execute data analysis and produce compelling visualizations
• Derive meaningful insights from various sample datasets
• Filter, sort, and summarize data to address exploratory questions
• Convert processed data into informative line charts, bar charts, histograms, and other plots
• Import and filter data from diverse sources, such as Excel, CSV, and SPSS files