Training Program Overview Data analysis and reporting techniques:

The modern institutional work environment imposes continuous requirements for improvement and operational efficiency enhancement. With the increasing reliance on data across various sectors, data analysis techniques and infographic usage have become essential tools that organizations need, regardless of their nature of activity.

The availability of massive amounts of data poses a real challenge for managers and decision-makers, requiring advanced analytical competencies. Additionally, selecting the correct methods for analyzing, presenting data, and preparing reports is a pivotal element in supporting decisions that contribute to performance improvement and achieving institutional objectives.

Despite the importance of data analysis, this requires advanced skills in data summarization, visual representation, and professional report preparation.

The Infographics: Data Analysis Techniques and Report Preparation program aims to provide participants with essential skills for analyzing digital data, preparing reports, and using graphics and visuals to present data effectively and clearly.

This training program highlights:

1. Providing participants with theoretical understanding and practical experience in a range of the most common analysis techniques and digital data representation methods.
2. Enabling participants to identify the most appropriate type of analysis for each problem according to the nature and structure of the data.
3. Providing participants with sufficient theoretical background to help them judge situations where some applied techniques may lead to inaccurate or misleading conclusions.
4. Providing participants with analytical terminology and concepts that enable them to communicate professionally with data analysis, statistics, and probability experts, and understand and read specialized scientific books and references in this field.
5. Introducing basic statistical methods.
6. Exploring the use of Excel (2010 or 2013 versions) in data analysis and leveraging the capabilities of the Data Analysis Tool.

Training Program Objectives:

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

1. Apply analysis and graphical presentation techniques using infographics.
2. Distinguish between appropriate analysis types for different data structures and formats.
3. Possess theoretical and knowledge background that enables them to judge the accuracy and reliability of analysis results.
4. Communicate professionally with specialists in data analysis fields and read and understand analytical reports.
5. Build a strong foundation in statistical concepts and methods.

Training Methodology:

Participants spend most of the program time in practical application, exploring Excel’s capabilities in data analysis and visual representation, including using the Data Analysis Tool Pack, and working on real data analysis problems that simulate the actual work environment.

Training Program Topics and Content:

First: Introduction and Descriptive Statistics

1. Data analysis concept.
2. Review of statistics fundamentals and handling small sample sizes.
3. Quick application guide for using Excel and Power BI.
4. Describing datasets using statistical methods.
5. Graphical data representation.
6. How to create infographics using Excel and Power BI.
7. Comparing data presentation using infographics with traditional methods.
8. Normal distribution.

Second: Frequency Analysis and Time Series

1. Frequency of occurrence.
2. Histograms.
3. Pareto Analysis.
4. Pivot tables and pivot charts.
5. The difference between Excel dashboards and infographics.
6. Time series analysis.
7. Trending Data analysis.
8. Estimation theory.

Third: Scenario Analysis, Confidence Intervals, and Six Sigma Methodology

1. Scenario modeling.
2. Interactive tables using artificial intelligence tools.
3. Confidence Intervals.
4. Importance of statistical control charts for oil and gas companies.
5. Introduction to Six Sigma methodology.
6. Error Bars.
7. Mini case studies.

Fourth: Regression Analysis and System Modeling

1. Simple regression analysis and maximum likelihood estimation.
2. Curve Fitting.
3. Polynomial curve fitting.
4. Data characterization using mathematical equations.
5. Forecasting.
6. Single input, single output system modeling.
7. Multiple input, single output system modeling (multivariate analysis).
8. Regression analysis applications in oil and gas companies.
9. Data presentation using appropriate and professional reporting methods.

Fifth: Correlation Analysis and Analysis of Variance (ANOVA)

1. Differences between data groups and different data types.
2. Correlation analysis.
3. Autocorrelation functions.
4. Analysis of Variance (ANOVA).
5. Comprehensive review of acquired concepts and how to apply them practically in the work environment.

Data analysis and reporting techniques