Data Analysis Techniques

Introduction :

In today's rapidly evolving corporate landscape, where change is imperative for survival and the pressure to enhance production efficiencies and lower operating/maintenance costs is constant, engineers and technologists are confronted with escalating plant and process performance objectives. Consequently, there is a growing dependence on precise and dependable data analysis, representation, and interpretation. This course is designed to equip engineers and technologists with the knowledge and practical skills required to transform data into actionable information and effectively present it for optimal utilization.

Target Audience :

• Business analysts, data analysts, and data scientists seeking to improve their analytical skills.
• Managers and decision-makers interested in understanding data analysis techniques.
• Students and professionals in fields such as marketing, finance, and operations who require data analysis for their work.
• Anyone who wants to learn how to interpret and analyze data effectively.

Course Objectives :

• 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 provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and data representation methods, which have direct relevance to a wide range of analytical problems.
• 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 an overview of the main data analysis applications within engineering systems.

Course Modules:

Introduction to Data Analysis

• Definition of Data Analysis and Its Importance
• Types of Data: Qualitative vs. Quantitative
• Overview of the Data Analysis Process

Data Collection Methods

• Primary vs. Secondary Data Collection Techniques
• Designing Effective Surveys and Questionnaires
• Data Sampling Techniques: Random, Stratified, and Convenience Sampling

Data Cleaning and Preparation

• Importance of Data Cleaning in the Analysis Process
• Techniques for Handling Missing Data and Outliers
• Tools and Practices for Data Preparation: Normalization and Transformation

Exploratory Data Analysis (EDA)

• Overview of EDA and Its Goals
• Descriptive Statistics: Measures of Central Tendency and Dispersion
• Visualization Techniques: Histograms, Box Plots, and Scatter Plots

Statistical Analysis Techniques

• Introduction to Inferential Statistics
• Hypothesis Testing: Concepts and Procedures
• Understanding p-values, Confidence Intervals, and Statistical Significance

Correlation and Regression Analysis

• Understanding Correlation: Pearson vs. Spearman Correlation
• Simple Linear Regression: Concepts and Applications
• Multiple Regression Analysis: Building Predictive Models

Advanced Data Analysis Techniques

• Overview of Time Series Analysis for Trend Forecasting
• Introduction to Cluster Analysis and Its Applications
• Using Principal Component Analysis (PCA) for Dimensionality Reduction

Data Visualization Techniques

• Importance of Data Visualization in Analysis Interpretation
• Creating Effective Charts and Graphs: Best Practices
• Using Visualization Tools: Tableau, Power BI, and Excel