An Interactive 5-Day Training Course
Data Analysis Techniques for Engineers and Technologists
Course Overview
Corporate ethos which demands continual improvement in work place efficiencies and reduced operating, maintenance, support service and administration costs means that managers, analysts and their advisors are faced with ever-challenging analytical problems and performance targets. To make decisions which result in improved business performance it is vital to base decision making on appropriate analysis and interpretation of numerical data.
Training Objectives
This seminar aims to provide those involved in analysing numerical data with the understanding and practical capabilities needed to convert data into information via appropriate analysis, and then to represent these results in ways that can be readily communicated to others in the organisation.
Objectives include:
- To provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and representation methods for numerical data
- To give delegates the ability to recognize which types of analysis are best suited to particular types of problems
- To give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions
- To provide delegates with a working vocabulary of analytical terms to enable them to converse with people who are experts in the areas of data analysis, statistics and probability, and to be able to read and comprehend common textbooks and journal articles in this field
- To introduce some basic statistical methods and concepts
- To explore the use of Excel 2010 or 2013 for data analysis and the capabilities of the Data Analysis Tool Pack
It should be noted that the course does not cover the subjects of data acquisition, databases, data management, data warehousing or the analysis of text-based information.
Who should Attend?
The course has been designed for professionals whose jobs involve the manipulation, representation, interpretation and/or analysis of data. Familiarity with a PC and in particular with Microsoft Excel (2003, 2007, 2010 or 2013) is assumed.
The course involves extensive computer-based data analysis using Excel 2010 and therefore delegates will be expected to be numerate and to enjoy working with numerical data on a computer.
About Saudi Glomacs
At Saudi GLOMACS, we specialize in delivering world-class training courses in Saudi Arabia and across various international locations. Our training courses are tailored to meet the unique demands of Saudi Vision 2030 and the Human Capability Development Program, focusing on empowering Saudi citizens and enhancing workforce skills. We offer diverse courses spanning leadership, management, engineering, and technical disciplines to cultivate expertise and drive professional growth. Our flexible learning options—whether in-person, online, or in-house—ensure accessibility and convenience for individuals and organizations alike.
With over 30+ years of experience through the GLOMACS global network, we are committed to delivering innovative, results-driven training solutions. Our expert instructors combine industry knowledge with dynamic teaching methods, fostering practical skill development and long-term career success. By choosing Saudi GLOMACS, you're investing in personal excellence and contributing to the Kingdom’s sustainable economic growth and vision-driven transformation.
Training Outline
DAY 1: Setting the Statistical Scene
- Statistics as an evidence-based management decision support tool
- The process of evidence-based statistical decision making
- Overview of the components of the discipline of Statistics
- Applications review of Statistics for Engineers and Technologists
- Data – Raw material of Statistics – Factors affecting data quality/integrity (sources of data, data gathering approaches (sampling), data types (categorical and numeric), data preparation issues) (all impact on the validity, accuracy, completeness and representativeness of data to address management issues
DAY 2: Exploratory Data Analysis – Tools to Profile and Describe Sample Data
DAY 3: Basic Probability Concepts – Measuring Uncertainty
- Basic Probability Types, Rules and Concepts
- Normal Probability Distribution
DAY 4: Inferential Analysis – Statistical Decision Making
- Confidence Intervals – To estimate likely population measures
- Application of Confidence Intervals to Statistical Quality Control (process control charts (R-charts, x(bar)-charts, S-charts), control charts for attribute data, process capability indexes (SPC XL for Excel)
- Hypothesis Testing – Tests for statistical relationships between measures
- From two samples (t-tests for independent and matched samples)
- From multiple samples (Analysis of Variance ANOVA)
- Test for independence of association (the chi-squared test)
DAY 5: Statistical Modelling – Building Models for Prediction Purposes
- The model building process
- Correlation analysis
- Multiple Linear Regression models (Stepwise Regression modelling)
- Modelling with categorical measures
- Curve fitting (polynomial and auto-regressive models)
DAY 6: Basics of Data Mining
- Overview of data mining
- Review of data mining approaches and techniques (Descriptive data mining and Predictive data mining modelling approaches)
Certificates
- On successful completion of this training course, GLOMACS Certificate will be awarded to the delegates
- Continuing Professional Education credits (CPE) : In accordance with the standards of the National Registry of CPE Sponsor, one CPE credit is granted per 50 minutes of attendance
Accreditation
GLOMACS is registered with NASBA as a sponsor of Continuing Professional Education (CPE) on the National Registry of CPE Sponsors. NASBA have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.learningmarket.org.
All Training Seminars delivered by GLOMACS by default are eligible for CPE Credit.
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