Learning Data Analytics: 1 Foundations
Build practical data-cleaning, SQL, and Power BI skills
Learning Data Analytics: 1 Foundations is an applied introduction that walks beginners through the practical mindset and toolset of working data analysts, taught by Robin Hunt.
The course emphasizes hands-on use of Excel, Power Query, basic SQL, and Power BI to find, clean, model, and visualize data for business questions. It targets learners seeking measurable, job-ready competencies—career changers and professionals who need repeatable ETL and reporting workflows—and pairs short lessons with exercises and downloadable files to practice importing flat files, validating joins, removing duplicates, and building simple visualizations.
At a Glance
Learning Data Analytics: 1 Foundations is an introductory course taught by Robin Hunt that orients learners to the mindset, processes, and practical tools used by working data analysts. It covers how to find, clean, model, and interpret data while introducing practical workflows in Excel, Power Query, SQL, and Power BI to prepare learners for entry-level analyst tasks.
| Level | Beginner |
| Rating | 4.7 out of 5 |
| Duration | 3+ hours |
| Languages | English |
| Certificate | Certificate of Completion |
| Access | Access for as long as your LinkedIn Learning subscription is active (subscription-based access) |
| Course includes |
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| Price | Included with subscription (LinkedIn Learning); free trial available |
What This Course Teaches
The course frames outcomes as concrete, job-relevant competencies a beginner analyst can demonstrate after completing the modules.
Learners will be able to explain core analyst concepts, write and interpret basic SQL, clean and model data with Excel and Power Query, import and prepare flat files, implement basic Power BI visualizations, and apply governance and validation practices when working with stakeholders.
How the Course Is Structured
The course is organized into nine sections (including Introduction and Conclusion) and is presented as a sequence of short, focused lessons that build from orientation to hands-on workflows.
It runs about 3+ hours in total and contains roughly 50+ short video lessons spread across those nine topical modules.
Curriculum overview
01 Introduction ▾
Course orientation, what to expect, and how to use the provided exercise files.
02 1. Getting Started with Data Analysis ▾
Defines data analysis and the analyst role, and outlines typical organizational responsibilities and skills.
03 2. Fundamentals of Data Understanding ▾
Covers identifying data and field types, basic syntax, and an introduction to SQL for reading queries.
- Challenge: Reading SQL
- Solution: Reading SQL
04 3. Key Elements to Understand when Starting Data Analysis ▾
Addresses how to find and interpret existing data, cleaning basics, workflows, and how joins and validation work in practice.
- Challenge: Products are not categorized
- Solution: Products are not categorized
05 4. Getting Started with a Data Project ▾
Practical guidance on initiating projects, common beginner mistakes, and working with Excel- and database-based datasets while preserving originals.
06 5. Data Importing, Exporting, and Connections ▾
Explains data governance, source data considerations, working with flat files and connections, and creating datasets intended for reuse.
07 6. Getting Started with Data Cleaning and Modeling ▾
Introduces ETL concepts, cleaning techniques using Excel macros and Power Query, and basic approaches to modeling data with queries.
- Challenge: Rename headers in Power Query
- Solution: Rename headers in Power Query
08 7. Applying Common Techniques for All Data Analysts ▾
Demonstrates common tasks such as conversions in Power Query, duplicate removal, text transformations, merging columns, logical functions, and aggregation.
- Challenge: Count and amounts of products
- Solution: Count and amounts of products
09 Conclusion ▾
Wraps up the course with final reflections and links to additional resources for continuing the learner’s data analytics journey.
Audience & Requirements
The course targets beginner learners who need practical, workplace-ready data skills rather than theoretical depth.
It is suitable for career changers, Excel-savvy professionals, and early-career analysts who want to move from spreadsheets to repeatable data-prep and basic visualization workflows.
- Career changers preparing for entry-level data analyst roles.
- Business professionals who need to analyze and report on operational data.
- Excel users seeking to learn Power Query and basic Power BI workflows.
- Students or early-career professionals exploring analytics fundamentals.
- No special prerequisites or prior programming experience.
- Excel (desktop) with Power Query recommended to follow along with exercises.
- Optional: Power BI Desktop or Microsoft Access to reproduce related demonstrations.
Final Verdict
Given its strong learner reception and practical, tool-focused syllabus, Learning Data Analytics: 1 Foundations is a worthwhile, low-risk way for beginners to gain job-relevant data-preparation and basic visualization skills. The course’s hands-on exercises, downloadable files, and a shareable completion certificate make it effective for professionals seeking demonstrable, resume-ready competencies.
It is best suited to career changers, Excel-savvy professionals, and early-career analysts who want a compact, applied introduction; learners needing deep statistical theory or intensive programming should pursue a longer, specialized program. The subscription-based access model and included certificate favor ongoing learners who will use the platform beyond this single course, while occasional learners should factor the access model into their decision.
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