Power BI Essential Training

Learn to import data in Power Query Desktop by bringing in four tables, apply transformations, set data types, and append sales tables into a single dataset for effective visualizations.

Append data in Power Query from multiple tables to create a single sales table, aligning columns, naming it sales, then close and apply to load into Power BI.

Use the merge query option in Power BI essential training to join multiple tables with a left outer join and bring brand names and product keys into the sales table.

Transform data in Power Query to consolidate four tables into one, removing unneeded columns and merging or expanding as needed, while enabling load to control loading into Power BI.

Explore Power Query tools and options in Power BI essential training, including changing data types, splitting columns, duplicating and removing duplicates, and extracting month names from dates.

Explore using the Power Query Editor in Power BI to connect to data, promote first-row headers, reshape tables, filter for specific years, and create new tables for year-based analysis.

Edit queries in Power BI to merge data from multiple sources, select tables and files, add new sources, and refresh to incorporate fresh data for up-to-date visualizations.

Explore advanced Power Query concepts by connecting to diverse data sources, including Excel and CSV files, and forming folder connections. Combine all files into one table to prepare for visualisations.

Connect and transform data in Power BI from multiple file types—Excel, text/csv, Access, and PDF—by selecting sources, choosing tables, and applying basic data transformations.

Learn to import data from the web into Power BI, connect via the web engine, select and transform tables, remove blanks and totals, and refresh data for current visuals.

Connect with multiple Excel sheets in a workbook to pull data, set the first row as headers, remove extra columns, and refresh automatically as new sheets are added.

Connect Power BI to a folder of Excel files, transform and combine them, and auto refresh dashboards as new data arrives.

Explore the Power BI Desktop canvas, connect data sources like Microsoft Access, build data models, and create visuals and dashboards with marketplace and AI visuals.

connect case study data in Power BI by importing an access database, selecting the six tables (dimensions and fact), configuring relationships, and exploring visuals to analyze sales and cost data.

Explore how to select and switch visuals in Power BI, from column charts and treemaps to cards and metrics, and compare table versus pivot-like visuals for drilldown.

Master selecting visuals in Power BI to shape dashboards and reports, using appropriate charts, filters, and data relationships to clearly compare sales, costs, profits, and by year and category.

Learn to edit visuals in Power BI by adjusting legends, data labels, colors, titles, backgrounds, borders, and formatting options, and place visuals on the canvas.

Learn to use card visuals to display total profit, total sales, and total cost in a Power BI dashboard report, and arrange and size them effectively.

Learn to use slicers in Power BI to filter visuals, connect multiple slicers to charts, switch to focus mode, view data in tables, and explore subcategory filtering.

Learn to create relationships across five tables in an Access file, link sales, date, and product, and build DAX measures like total sales and gross profit in Power BI training.

Create and manage relationships in Power BI data models to enable accurate totals and meaningful visuals. Learn key concepts like product key, product category key, cardinality, and active relationships.

Learn to distinguish a fact table from dimension tables in Power BI, build many-to-one relationships, and apply a star schema to filter sales data by product, category, month, and salesman.

Explore how Power BI relationships enable cross-filtering between fact and dimension tables, letting you filter sales, costs, and returns by product, brand, and date attributes.

Explore the star schema in Power BI by modeling a fact sales table with related dimension tables—product, region, salesman, category, and date—and contrast with the snowflake approach.

Test your Power BI model after building it to verify figures and relationships. Create connections between tables like product subcategory and category, and validate visuals to ensure correct results.

Optimize your Power BI data model by transforming data, removing unnecessary columns, and hiding keys to streamline relationships before visualization.

Understanding how correct relationships shape your Power BI data model, with single directional relationships between date, product, and dimension and fact tables; optimize with DAX once the model is solid.

In this lecture, learn why DAX is essential for Power BI by building and using measures to calculate total sales, total cost, gross profit, and margin across data tables.

Explore the difference between calculated field and measure in Power BI, and learn to create and use measures for total sales, total cost, and total profit in visuals.

Explore model optimization in Power BI by creating measures for year-over-year and actual vs budget comparisons, using dashboards to analyze sales, margins, and time intelligence for period comparisons.

Explore Power BI's time intelligence feature to calculate same period last year, compare totals across years and months, and build dynamic visual insights.

Create a date table with calendar auto in Power BI, use DAX to generate dates, and enable time intelligence by marking it as a date table with year and quarter.

Explore time functions to compare YTD, MTD, and QTD sales in Power BI and build measures for year-to-date totals across periods.

Learn to use quick measures in Power BI to apply time intelligence features, such as year-to-date and month-to-date calculations, based on total sales and dates.

Learn to solve Power BI case studies by building a complete dashboard with cards, slicers, and visuals, using data mapping, P&L style measures, and interactive year, month, or quarter views.

Case study 1 guides you to connect data, optimize the Power BI data model, trim unnecessary columns, and build a dashboard by linking product categories and subcategories.

Create DAX measures in Power BI from a data model, including total sales, total cost, and profit percentage, using arithmetic and aggregation for year-filtered visuals.

Learn to build a Power BI dashboard from visual inspection to interactive visuals, using slicers, charts, and trend lines to analyze sales, profits, and calendar year data.

Learn to customize visual colors in Power BI by selecting and editing data colors, applying a color theme, and instantly changing all visuals with the latest view options.

Consolidate three years of data and map traffic alarms in Power BI to build a time-aware dashboard, using Excel pivot data and mapping tables.

Demonstrates Power BI data preparation by importing two Excel tables, transforming and merging them, creating a mapping, and building relationships and a pivot to analyze revenue and expenses in USD.

Explore building a complete data model in Power BI by integrating 2017–2019 data from Excel and Word, creating mapping and calendar tables, defining relationships, and building interactive visuals with slicers.

Build a Power BI data model by merging a mapping table with main table and linking a date table to create month-to-date profit and loss visuals for main and subaccounts.