# Data Analysis Basic Course (Self-Paced)

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

## Overview

Data Analysis Basic gives you a practical intro to the core concepts and workflows that turn raw data into something meaningful you can actually use for analysis and smart decision-making. The course walks through foundational topics like data structures and types, data quality, visualization, databases, governance, and reporting tools, along with tips on getting your insights across through data storytelling.

You'll also dig into where data actually comes from, including both internal and external sources, how analytical systems work differently from transactional ones, and why clear definitions, privacy standards, and compliance requirements matter so much when you're working with real world data. Hands-on work in Excel and example-driven case studies reinforce techniques like sorting, filtering, pivot tables, and the common analytics patterns you'll see in audit, operations, and business settings.

## What you'll learn

- Explain why data analytics matters and how it backs decision-making in business and audit settings
- Tell the difference between structured and unstructured data, and describe the common internal and external data sources you'll run into
- Compare transactional systems with analytical systems, including the role of data marts, data warehouses, and ETL processes
- Spot common data quality issues and put basic normalization concepts to work to keep things consistent when you're joining datasets
- Describe the core ideas behind data governance, including data definitions and stewardship, and explain why they really matter
- Recognize the privacy and compliance considerations that come into play, including how to handle PII and PHI the right way
- Use basic data visualization principles to get your insights across clearly and pick out trends or outliers
- Put foundational Excel techniques to work 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