Ideas for your next chapter.
Explore data, technology, and the way we learn.
Excel: build a spreadsheet you can trust
Keep one record per row and one field per column. Convert a range to a table, name it Sales, and check that dates and amounts have consistent types. Practice: build a five-row sales table with Date, Region and Revenue.
SQL joins: why your row count changed
SELECT chooses columns, FROM chooses a table, and WHERE filters rows. Practice: SELECT name, revenue FROM sales WHERE revenue > 100; Explain which rows remain.
Power Query: stop repeating the cleanup
Inspect nulls, duplicates and data types before making changes. Keep a copy of the source and define what one row represents.
Power BI: a dashboard needs a decision
Connect to a small CSV in Power BI Desktop. Profile missing values and set correct types before loading. Write down the business question the report will answer.
Python: small scripts, useful progress
Assign a variable with = and inspect its type using type(). Strings and numbers behave differently. Practice: calculate a total and print a clear message.
AI: verify before you publish
Generative models produce plausible outputs, not guaranteed facts. Choose low-risk tasks and define what a good result looks like before asking.
Machine learning: avoid data leakage
Features are inputs and labels are the outcome you want to predict. Start with a clear objective and a baseline. Identify features available at prediction time.
SEO: start with a useful answer
Answer a specific reader question with accurate original content. Use clear headings, descriptive titles and meaningful internal links.