# Data Analysis Basic Course Online Canonical URL: ## Overview Data Analysis Basic gives you a practical intro to the core concepts and workflows that turn raw data into information you can actually use to analyze situations and make smart decisions. The course walks through foundational topics like data structures and types, data quality, visualization, databases, governance, and reporting tools, along with guidance on how to communicate insights through data storytelling. You'll also dig into where data actually comes from, including internal and external sources, how analytical systems stack up against transactional systems, and why clear definitions, privacy standards, and compliance requirements really matter when you're working with real world data. Hands-on work in Excel and case studies built around real examples reinforce techniques like sorting, filtering, pivot tables, and the kinds of analytics patterns that show up 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 structured and unstructured data apart, and describe the common internal and external data sources you'll run into - Compare transactional systems and analytical systems, covering the purpose 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 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 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. ## Instructors ### Garfield Stinvil — Instructor Garfield is an experienced software trainer with over 16 years of real-world professional experience. He started as a data analyst with a Wall Street real estate investment company & continued working in the professional development department at New York Road Runners Organization. He enjoys bringing humor to whatever he teaches and loves conveying ideas in novel ways that help others learn more efficiently. Since starting his professional training career in 2016, he has worked with several corporate clients including Adobe, HBO, Amazon, Yelp, Mitsubishi, WeWork, Michael Kors, Christian Dior, and Hermès. Outside of work, his hobbies include rescuing & archiving at-risk artistic online media using his database management skills. ### Colin Jaffe — Instructor Colin Jaffe is a programmer, writer, and teacher with a passion for creative code, customizable computing environments, and simple puns. He loves teaching code, from the fundamentals of algorithmic thinking to the business logic and user flow of application building—he particularly enjoys teaching JavaScript, Python, API design, and front-end frameworks. Colin has taught code to a diverse group of students since learning to code himself, including young men of color at All-Star Code, elementary school kids at The Coding Space, and marginalized groups at Pursuit. Colin lives in Brooklyn with his wife, two kids, and many intricate board games. ### Brian McClain — Instructor Brian McClain is an experienced instructor, curriculum developer, and web developer. Brian served as Director for a coding bootcamp, where he is now a lead instructor and course developer for both JavaScript and Python. He teaches Web Development, JavaScript, Python for Data Science, Machine Learning, and AI. He taught Python Data Science and Machine Learning as an Adjunct Professor of Computer Science at Westchester County College. Brian is also an active industry professional in the field of generative AI app development. His website and iOS app, Artmink, provide appraisals of art and antiques from user-uploaded images. ## Pricing **Tuition:** $649