Data Analytics Course PDF with SQL Exercises, Messy Practice Datasets, Answer Keys and 4 Portfolio Projects, Beginner Analyst Training

Practise the everyday work of a data analyst on realistic, messy data: spreadsheets, SQL, cleaning, charts and clear write-ups. Then finish four portfolio projects you can publish as your own case studies.

The Data Analytics Career Lab is a self-study course in 8 modules. Each module teaches one part of the analyst workflow in plain language and works through a real example on the course data. Exercises follow, with answers at the back. The SQL module has 30 exercises and a small checker script that tells you which of your answers are right.

All the data is fictional and made for practice: a coffee shop's sales, a help desk's tickets, a city bike share with daily weather, an online store's database and a set of email tests. The raw files contain the kinds of problems real files have: mixed date formats, typos, stray spaces, prices stored as text, duplicates and refunds. Each messy file comes with a cleaned version and a cleaning log, so you can check your work.

The 8 modules

1. The analyst workflow: turn a vague request into clear questions, define metrics, and write a short analysis plan
2. Spreadsheets for analysis: SUMIFS, COUNTIFS, PivotTables and change over time
3. SQL basics with SQLite: from SELECT and WHERE to joins, CASE, CTEs and window functions, with 30 exercises in five levels
4. Data cleaning: a 10-step checklist, cleaning in a spreadsheet or with a script, and a cleaning log that adds up
5. Exploratory analysis: centre and spread, distributions and outliers, fair segment comparisons and correlation
6. Visualisation: pick the right chart, write titles that state the finding, and a before-and-after chart makeover
7. Storytelling, ethics and limits: answer-first summaries, a one-page memo, an A/B test readout, and privacy, fairness and uncertainty
8. Portfolio and job search: turn projects into case studies, write specific and truthful resume bullets, practise interview questions and plan a 30-day search routine

The 4 portfolio projects

Each brief gives you a stakeholder, the questions to answer, the deliverables, suggested steps, stretch goals and sanity checks. It ends with a six-row scoring rubric and a checklist to go through before you publish.

• Project 1: Did a price rise hurt a coffee shop's sales?
• Project 2: Why did help-desk satisfaction dip?
• Project 3: What drives bike share demand?
• Project 4: Should an online store switch its email subject lines?

What you get

2 files, available to download as soon as your purchase is confirmed:

1. Data-Analytics-Career-Lab-Course.pdf: 47 pages, US Letter
• 8 modules with worked examples, charts, exercises and space for notes
• A suggested 8-week plan
• 4 portfolio project briefs, each with a rubric and a self-assessment page
• Appendices: answers to the exercises, all 30 SQL exercises with solutions, a data dictionary and a one-page cheat sheet

2. Data-Analytics-Lab-Data-Kit.zip (about 0.6 MB), containing:
• raw/: 4 messy CSV files to clean yourself, plus a daily weather file that is already clean (about 10,000 rows in total)
• clean/: the cleaned version of each file
• sql/: an SQLite database of a fictional online store (4 tables: 480 customers, 30 products, 1,850 orders and 3,416 order lines), the 30 exercises, full solutions, the expected result for each exercise, a checker script, a query runner, and CSV copies of every table
• scripts/: 4 Python cleaning scripts that turn each messy file into its clean file
• answer-keys/: a Module 2 answer workbook (.xlsx) with live formulas, the cleaning logs, Module 5 answers, and reference numbers for the four projects
• templates/: an analysis plan, cleaning log, data dictionary, project README, stakeholder memo and job search tracker
• A start-here guide, a data dictionary and the licence

What you need

• A spreadsheet app, such as Excel, Google Sheets, LibreOffice Calc or Numbers.
• For the SQL checker and the cleaning scripts: Python 3.8 or later. They use only Python's standard library, so there is nothing else to install. You can also open the database in any SQLite tool that supports window functions (SQLite 3.25 or later) and check your results against the expected results by hand.
• A PDF reader and a program that can open .zip files.
• No previous SQL or coding experience is needed. You should already be comfortable typing into a spreadsheet.

How delivery works

This is a digital download, so nothing is posted to you. You can download the files as soon as your purchase is confirmed, and download them again from your Etsy account at any time.

Who it's for

It suits beginners who want structured, hands-on analytics practice, career changers who want finished projects to show, and anyone who works with spreadsheets and wants to add SQL and clearer reporting. The suggested pace is 8 weeks at 4 to 6 hours a week, but you can go at any speed.

Usage terms

This licence is for personal use by the buyer. You can use the course, data, database, scripts and templates for your own learning, and print the PDF for yourself.

You may publish your own analysis, charts, write-ups and code for the portfolio projects, for example on a personal website. Say that the data is synthetic practice data from this course, and show no more than 50 rows of any dataset.

You may not resell, share, upload or redistribute the course or the data kit (including the datasets and database), whether changed or unchanged. You also may not present the data as real, or use the materials in a course, template or product of your own. Teams and classrooms need one copy for each learner.

Please note

This is a self-study course. It is not a certification, and it does not promise a job, an interview, income or any other result. All businesses, people and numbers in the data are fictional. Excel is a trademark of the Microsoft group of companies. This product is independent and is not affiliated with or endorsed by Microsoft or any other software maker.

AI disclosure

This product was made by the seller with the help of AI tools. An AI assistant did the following:
• wrote the course text, exercises, project briefs and rubrics
• wrote the code that generated the fictional datasets and the database
• wrote the SQL solutions, the checker and the cleaning scripts
• laid out the pages

Every SQL solution was then run against the database and matched to its expected result. Every cleaning script was run and its output compared with the known correct rows. The key numbers in the course were cross-checked with a separate data analysis library. The seller reviews and approves every product before it is released. The listing images show the real files: course pages, real data rows and real checker output rendered from the product. None of them are photographs.