# Data Analysis Intermediate Course (Self-Paced)

Canonical URL: <https://www.graduateschool.edu/courses/data-analysis-intermediate-course-self-paced>

## Overview

This course develops practical intermediate-level data analysis skills used to transform raw data into meaningful insights that support informed decision making. Participants examine how data is structured, sourced, and analyzed across organizational systems, including transactional databases and analytical environments such as data warehouses and data marts. Through applied exercises, learners explore common data quality challenges such as duplicates, missing values, errors, and inconsistent formatting, and learn techniques for preparing data for analysis using tools available in Excel.

The course also introduces key analytical concepts including descriptive statistics, correlation, sampling, and visualization methods used to identify patterns, trends, and potential anomalies within datasets. Participants practice using pivot tables, charts, and analytical functions to interpret data while learning how analytical systems differ from operational systems in supporting decision making. The course also addresses regulatory and governance considerations relevant to data analysis, including audit standards, data reliability expectations, and responsible use of artificial intelligence tools in analytical workflows. By the end of the course, participants will be able to evaluate data integrity, perform structured analysis, and communicate insights derived from organizational data sources.

## What you'll learn

- Evaluate the structure and sources of organizational data in order to determine how datasets support analytical decision making.
- Assess data quality by identifying inconsistencies, duplicates, missing values, and integrity issues within datasets.
- Prepare and transform datasets using Excel tools and functions to ensure data is suitable for analysis.
- Apply analytical techniques including pivot tables, descriptive statistics, and sampling to interpret data patterns.
- Analyze datasets to identify trends, anomalies, and operational insights relevant to organizational decision making.
- Interpret statistical outputs to support evidence-based conclusions and communicate analytical findings.
- Apply data governance, regulatory, and ethical considerations when conducting data analysis.

## Curriculum

#### 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.

## Pricing

**Tuition:** $649