This course offers this training program on Data Preparation and Analysis, designed to develop participants’ skills in collecting, organizing, and analyzing data scientifically to support decision-making across various work environments.
Data analysis has become one of the most essential modern tools for understanding trends, uncovering relationships, and providing practical solutions to administrative and operational challenges—especially with the increasing volume and diversity of information sources. This program introduces key concepts and methods used in data management, from preparation and processing techniques to applying quantitative and qualitative analytical tools. It also equips participants with the ability to interpret results and present them professionally, enhancing the value of analytical outputs and strengthening the link between analysis and decision.
Course Objectives
By the end of the course, participants will be able to:
Who should attend?
Employees working in planning and development departments in public and private institutions.
Data analysts and professionals in management information systems.
Research and studies officers, and teams in data and statistical centers.
Anyone wishing to develop data analysis skills for professional purposes.
Knowledge and Benefits:
After completing the program, participants will be able to master the following:
Understand the basic principles of data preparation and analysis.
Acquire skills in organizing, cleaning, and preparing data for analysis.
Use statistical analysis tools to understand patterns and indicators.
Produce precise and professional analytical reports.
Support decision-making through data-driven analysis.
Course Outline
Introduction to Data Science
Definition of data science and its importance in organizations
Differences between data, information, and knowledge
Areas of application for data analysis
Types of Data and Their Sources
Quantitative and qualitative data
Data sources: primary and secondary
Challenges in collecting data from multiple sources
Data Collection Methods
Designing data collection tools (surveys, interviews)
Ethical considerations in data collection
Electronic data collection methods
Data Organization and Coding
Creating and structuring spreadsheets
Coding values and transforming variables
Handling missing values and duplicates
Preparing Data for Analysis
Data cleaning and error correction
Formatting data types and converting structures
Merging data from different sources
Software Tools for Data Analysis
Introduction to Excel as a primary analysis tool
Overview of SPSS, Power BI, and Python
Criteria for selecting the appropriate analytical tool
Descriptive Statistical Analysis
Mean, median, and standard deviation
Frequency distributions and graphical measures
Using tables and charts to interpret results
Trend and Indicator Analysis
Extracting patterns from data
Identifying temporal changes and trends
Linking indicators with actual outcomes
Relationships Between Variables
Correlation and regression analysis
Interpreting cause-and-effect relationships
Applying analysis in practical scenarios
Qualitative Data Analysis
Handling open-text responses
Categorizing and coding qualitative answers
Extracting key themes and concepts
Data Visualization and Presentation
Choosing the appropriate chart type
Using colors and design principles in visualization