For most analysts, the spreadsheet is where data work begins, and it never fully goes away. Before you can answer a business question you need to be able to open a dataset, understand what each column holds, and judge whether it can be trusted. Data literacy is that skill: reading data critically rather than taking every number at face value.
You'll start with the basics of data types and classifications, such as numbers vs text, and structured vs unstructured data. From there the module covers the Excel techniques analysts use every day: conditional formulas like SUMIFS and COUNTIFS, lookups with XLOOKUP, and summarising thousands of rows in seconds with pivot tables. The same ideas carry over to Google Sheets.
Once you're comfortable exploring data by hand, you'll be ready for the next module, SQL Fundamentals, which does the same kind of work directly against a database.
The basic data types (character, string, integer, real, date, Boolean) and how data is classified, so you know what you're looking at before you analyse it.
How to total, count, and average only the rows that meet your conditions, and why the "S" versions are better than SUMIF and COUNTIF.
A quick overview of XLOOKUP, the modern alternative to VLOOKUP and INDEX MATCH for pulling matching values from another table.
What pivot tables are, why they're so useful, and five simple steps to build your first one and summarise a large dataset.
A complete course on using Excel for data analytics, from essential functions to more advanced features. Watch it in sections as a deeper reference alongside the lessons above.