Data Analytics (Extended)

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.

Mean, median, mode, range, variance and standard deviation — summarizing a business dataset in a few honest numbers.

Probability fundamentals, discrete vs continuous distributions, and reading a distribution shape correctly.

The normal (bell) curve, z-scores, and why the Central Limit Theorem underpins most statistical methods.

Measuring relationships between business variables, correlation vs causation, and reading a correlation matrix correctly.

Population vs sample, random/stratified sampling, and why sampling method affects the conclusions you can draw.

What a confidence interval actually means, calculating one in Excel, and communicating uncertainty honestly.

Null and alternate hypotheses, t-tests and chi-square tests at a practical level, and reading test output correctly.

Building and interpreting a linear regression model with the Data Analysis ToolPak to predict a business outcome.

Moving averages, trendlines and Excel Forecast Sheets for projecting sales, demand and other business metrics.

Interpreting p-values correctly, common misinterpretations, and connecting significance back to a real business decision.

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.

Entities, relationships and Entity-Relationship diagrams for planning a database before you build it.

1NF through 3NF, reducing redundancy, and designing tables that stay consistent as data grows.

Writing reusable stored procedures and functions to standardize common business queries.

Automating actions on data changes with triggers, and keeping multi-step updates safe with transactions.

How indexes speed up queries, when to add one, and reading a query execution plan.

Rewriting slow queries, avoiding common performance traps, and optimizing joins on larger tables.

Batching, pagination and aggregation strategies for querying large, realistic business tables efficiently.

Designing a schema, loading real data, and writing an optimized query set to answer a full business brief.

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.

Ranking measures, comparison logic across periods, and What-If parameters for scenario dashboards.

Restricting what each viewer can see in a shared report, and basic data-governance practices for dashboards.

Adding custom visuals from AppSource and applying consistent themes and branding to a report.

Reducing model size, optimizing DAX for speed, and diagnosing a slow-loading report.

Building print-ready, page-based reports for formal business documents alongside interactive dashboards.

Building a single connected workflow where Excel, SQL and Power BI all feed the same live dashboard.

Analyzing a real retail sales dataset end to end — cleaning, SQL analysis and a published Power BI dashboard.

Investigating employee attrition patterns and presenting findings and recommendations to a business audience.

Budget vs actual analysis, expense trend tracking, and a finance-ready reporting dashboard.

Measuring campaign performance, conversion funnels and ROI from real marketing data.

Structuring findings for a non-technical audience, and presenting a dashboard the way you would to a client or manager.

A full business analytics solution — from raw data through statistics, SQL and Power BI — built, documented and presented to a live panel.