Data Analysis Intermediate Course (Self-Paced)
IDTECH8001SP

Module 1: Foundations of Data Analysis and Data Context

• Differentiate between raw data and meaningful information by examining how context and attribute labeling transform data into actionable insights.
• Identify common sources of organizational data including transactional systems, enterprise systems, and external data sources.
• Explain how transactional systems differ from analytical systems such as data warehouses and data marts.
• Recognize how data is combined, transformed, and integrated across systems to support analysis and decision making.

Module 2: Data Discovery and Data Quality Assessment

• Conduct initial data discovery to understand the structure, context, and potential analytical value of a dataset.
• Identify common data quality issues including duplicates, missing values, formatting inconsistencies, and data integrity errors.
• Apply techniques to detect data anomalies using sorting, filtering, and conditional formatting.
• Prepare datasets for analysis by applying data cleaning and standardization methods.

Module 3: Data Preparation and Data Transformation Techniques

• Use Excel tools to prepare datasets for analysis, including text-to-columns, formatting corrections, and standardization functions.
• Apply functions such as TRIM, CLEAN, CONCAT, and IFERROR to correct and standardize datasets.
• Understand how joining data from multiple systems can introduce data quality risks.
• Evaluate strategies for improving data integrity before performing analysis.

Module 4: Analytical Tools and Data Visualization

• Create pivot tables and pivot charts to summarize and visualize large datasets.
• Use analytical features within Excel to explore patterns, trends, and outliers.
• Apply visualization techniques to communicate insights derived from data.
• Understand how visual representation improves comprehension of analytical results.

Module 5: Descriptive Statistics and Analytical Techniques

• Apply descriptive statistical methods including mean, median, mode, and measures of dispersion.
• Interpret standard deviation, variance, and coefficient of variation to evaluate data distribution.
• Assess skewness and kurtosis when examining data patterns and anomalies.
• Use the Excel Data Analysis Toolpak to perform statistical analysis and generate analytical outputs.

Module 6: Applied Data Analysis and Case Studies

• Perform analytical exercises including duplicate transaction analysis, vendor analysis, and anomaly detection.
• Apply sampling methods to create representative subsets of data for deeper analysis.
• Use analytical techniques to identify potential fraud, policy violations, or operational risks.
• Interpret analytical results to support decision making and organizational insight.

Module 7: Data Governance, Compliance, and Responsible AI Use

• Recognize regulatory and governance frameworks that influence data analysis practices.
• Understand the importance of data reliability, audit standards, and data integrity requirements.
• Apply best practices when using artificial intelligence tools in data analysis.
• Identify privacy, ethical, and legal considerations when working with sensitive or regulated data.