This course offers this training program on Data Preparation and Analysis, designed to develop participants’ skills in collecting, organizing, and analyzing data scientif…
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.
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.
Definition of data science and its importance in organizations
Differences between data, information, and knowledge
Areas of application for data analysis
Quantitative and qualitative data
Data sources: primary and secondary
Challenges in collecting data from multiple sources
Designing data collection tools (surveys, interviews)
Ethical considerations in data collection
Electronic data collection methods
Creating and structuring spreadsheets
Coding values and transforming variables
Handling missing values and duplicates
Data cleaning and error correction
Formatting data types and converting structures
Merging data from different sources
Introduction to Excel as a primary analysis tool
Overview of SPSS, Power BI, and Python
Criteria for selecting the appropriate analytical tool
Mean, median, and standard deviation
Frequency distributions and graphical measures
Using tables and charts to interpret results
Extracting patterns from data
Identifying temporal changes and trends
Linking indicators with actual outcomes
Correlation and regression analysis
Interpreting cause-and-effect relationships
Applying analysis in practical scenarios
Handling open-text responses
Categorizing and coding qualitative answers
Extracting key themes and concepts
Choosing the appropriate chart type
Using colors and design principles in visualization
Transforming numbers into meaningful stories
Components of a professional analytical report
Writing with analytical clarity and precision
Providing recommendations based on results
Linking analysis to decision-making stages
Evaluating alternatives based on evidence
Measuring the impact of data-informed decisions
Definition and types of KPIs
Analyzing gaps between targets and actual results
Creating performance dashboards
Using analysis to enhance processes
Monitoring performance over time
Forecasting and improving problem response
Artificial intelligence in predictive analytics
Big Data and its significance
Integrating analysis with emerging technologies
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Arab Training Institute
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Arab Training Institute
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