Data Analytics

Workbooks, sheets, cells and ranges, keyboard shortcuts, and setting up a clean working file from day one.

Writing formulas, relative vs absolute references, and building calculations that stay correct as data changes.

The core functions used in almost every business spreadsheet, including nested and conditional logic.

Number formats, cell styles, and rule-based conditional formatting to make patterns visible at a glance.

Multi-level sorting, AutoFilter, and validation rules to keep incoming data clean and consistent.

Pulling matching data across sheets and tables, and why INDEX-MATCH beats VLOOKUP in real reports.

TRIM, LEFT/RIGHT/MID, TEXT-TO-COLUMNS and CONCAT for fixing messy, inconsistent spreadsheet data.

Bar, line, pie and combo charts — picking the right chart type and formatting it for a business audience.

Summarizing thousands of rows into a live, drag-and-drop report, plus building matching PivotCharts.

Multi-condition logic and aggregation formulas for real business rules, plus an introduction to array formulas.

Goal Seek, Scenario Manager and Data Tables for testing business assumptions before deciding.

Importing data from CSV/multiple files, applying repeatable cleaning steps, and refreshing on demand.

Loading multiple tables into Excel's data model, building relationships, and writing basic DAX measures.

Combining PivotTables, charts, slicers and KPI cards into a single, decision-ready Excel dashboard.

Recording macros and writing simple VBA to automate repetitive reporting tasks.

Building a full sales/expense analysis workbook from a raw dataset, following professional spreadsheet standards.

Relational database concepts, tables, rows and columns, and setting up a working SQL environment.

Retrieving and filtering exactly the rows you need with comparison, logical and pattern-matching operators.

ORDER BY, column/table aliases, and removing duplicates for cleaner query results.

SUM, AVG, COUNT, MIN/MAX and GROUP BY to summarize business data the way SQL is meant to be used.

Filtering on aggregated results, and the correct order of WHERE, GROUP BY and HAVING in a query.

Combining data across related tables correctly, and the practical difference between each join type.

Writing queries inside queries to answer multi-step business questions in a single statement.

ROW_NUMBER, RANK and running totals with OVER() — the modern way analysts solve ranking and trend questions.

Common Table Expressions for readable multi-step queries, and saving reusable queries as views.

Writing end-to-end queries that answer real sales, HR and finance questions from a business database.

The Power BI Desktop interface, and connecting to Excel, CSV and SQL data sources.

Shaping, cleaning and transforming data on import so your report starts from a reliable dataset.

Star schema basics, building relationships between tables, and structuring a model that scales.

Calculated columns vs measures, and core DAX functions for building your first custom calculations.

CALCULATE, filter context, and time-intelligence functions for YTD, MTD and period-over-period metrics.

Choosing and configuring the right visuals, cross-filtering, and designing a report page that reads clearly.

Bookmarks, drill-through, slicers and filter panes for a dashboard stakeholders can explore themselves.

Publishing to Power BI Service, setting up scheduled refresh, and sharing dashboards securely with a team.

What makes a report useful to a decision-maker — structure, audience and the right level of detail.

Choosing the right KPIs for a business question, and structuring findings into a clear data story.

Connecting Excel outputs into Power BI and setting up refreshable, low-maintenance recurring reports.

Analyzing a real sales/marketing dataset end to end — from raw data to a presented set of insights.

Applying the same Excel/SQL/Power BI workflow to HR attrition or finance/expense data.

Take a real business dataset from raw data through Excel/SQL analysis to a published Power BI dashboard and a presented report.