# Data Analysis Basic Course (Self-Paced)

Canonical URL: <https://www.nobledesktop.com/classes/data-analysis-basic-course-self-paced>

## Overview

Data Analysis Basic is a practical introduction to the core concepts and workflows used to transform raw data into meaningful information that supports analysis and informed decision-making. The course covers foundational topics such as data structures and data types, data quality, visualization, databases, governance, and reporting tools, along with guidance on communicating insights through data storytelling.

Learners also explore where data comes from, including internal and external sources, how analytical systems differ from transactional systems, and why clear definitions, privacy standards, and compliance requirements are essential when working with real-world data. Hands-on exercises in Excel and example-driven case studies reinforce techniques such as sorting, filtering, PivotTables, and common analytics patterns used in audit, operations, and business environments.

## What you'll learn

- Explain why data analytics is valuable and how it supports decision making in business and audit contexts.
- Distinguish between structured and unstructured data, and describe common internal and external data sources.
- Compare transactional systems and analytical systems, including the purpose of data marts, data warehouses, and ETL processes.
- Identify common data quality issues and apply basic normalization concepts to improve consistency when joining datasets.
- Describe core data governance concepts, including data definitions and stewardship, and explain why they matter.
- Recognize privacy and compliance considerations, including how to handle PII and PHI responsibly.
- Use basic data visualization principles to communicate insights clearly and identify trends or outliers.
- Apply foundational Excel techniques for analysis, including sorting, filtering, common functions, and pivot tables and charts.

## Curriculum

#### Module 1: Introduction to Data Analytics

- Understand the role of data analytics in modern organizations.
- Differentiate between data and information and recognize the importance of context.
- Identify how questions and data availability shape analytical approaches.

#### Module 2: Data Structures & Types

- Distinguish between structured and unstructured data.
- Understand tables, databases, rows, and columns.
- Recognize challenges in analyzing emails, images, PDFs, and other unstructured formats.

#### Module 3: Internal & External Data Sources

- Identify common internal systems (ERP, HR, POS, financial systems).
- Explore external data sources including government and partner data.
- Evaluate privacy, quality, and legal considerations when using external data.

#### Module 4: Transactional vs. Analytical Systems

- Compare transactional systems with data warehouses and data marts.
- Understand ETL (Extract-Transform-Load) concepts.
- Recognize how combining systems supports strategic decision-making.

#### Module 5: Data Quality & Governance

- Identify common data mismatches and transformation challenges.
- Understand data definitions, stewardship, and governance principles.
- Learn how poor governance can lead to operational failures.

#### Module 6: Data Privacy & Compliance

- Differentiate PII and PHI data types.
- Review major privacy regulations such as GDPR and CCPA.
- Apply best practices for handling sensitive data responsibly.

#### Module 7: Data Visualization

- Understand why visualization enhances learning and insight discovery.
- Differentiate between static and dynamic visualizations.
- Use visualization techniques to identify trends and outliers.

#### Module 8: Reporting & Analytics Tools

- Survey common tools such as Excel, Access, Tableau, and Power BI.
- Understand when to use visualization, statistical, or audit-specific tools.
- Recognize strengths and limitations of different analytics platforms.

#### Module 9: Excel for Data Analysis

- Apply sorting, filtering, and common math functions.
- Create pivot tables and pivot charts.
- Use Excel to answer basic business and audit questions.

#### Module 10: Analytical Techniques & Case Studies

- Perform stratification, duplicate detection, and normalization.
- Analyze vendor, employee, and transaction data.
- Apply techniques such as Benford’s Law, sampling, and date comparisons.

#### Module 11: AI & Emerging Trends in Data

- Understand the growth of big data and AI-driven analytics.
- Compare search engines and AI chat systems.
- Apply AI best practices and prompt fundamentals responsibly.

## Pricing

**Tuition:** $649