Power BI Accelerator: Master Data Analysis and Visualization

Power BI splits into desktop, online service, and mobile apps; use desktop for data connections, modeling, analysis, and visualizations, while the online service distributes reports and automates refreshes for stakeholders.

assess a simple dataset for Power BI by identifying date, warehouses, products, categories, and numeric fields like revenue and demand. plan basic models and time series analyses for clear insights.

Connect to data sources using Power Query Editor, including Excel workbooks, clean and transform data into tables for Power BI analysis.

Master the Power BI query editor to transform and optimize data as a snapshot of the raw source, with every change recorded by the Power Query engine as applied steps.

Explore data preparation in Power BI's Power Query editor to transform, clean, and add calculated and helper columns, including add column from examples to accelerate analytics.

Organize your Power BI queries by grouping into a data model and separating supporting queries, then learn duplicates vs references and building lookup tables to optimize the model.

Create a comprehensive date table in Power BI using extended code from Enterprise DNA, then paste into a blank query, set the date range, and name it dates.

Master data modeling in Power BI by distinguishing fact and lookup tables, arranging facts in a bottom row and lookups above to ensure clean relationships.

Build a Power BI model by linking date and product lookup tables to the fact table with one-to-many relationships, enabling filters to flow downward like a waterfall and supporting auditing.

Explore DAX, the data analysis expressions language in Power BI, and learn to create efficient measures instead of calculated columns, using IntelliSense and clear naming to enhance analytics and visuals.

Master core DAX measures rapidly in Power BI, using count rows, distinct count, min, max, and average, and organize them in a dedicated measures table with folders.

Learn how iterating functions and measure branching, driven by context, enable per-row calculations in DAX, and why virtual measures can replace calculated columns for revenue analysis.

Explore measure branching in DAX to reference core measures, create cumulative totals and moving averages, and build dynamic visuals with date tables, filters, and calculated columns.

Follow a simple framework to create Power BI visualizations quickly with grid-based alignment and clear labeling, emphasizing key insights from the palette.

Save and organize Power BI models by grouping measures into folders, then apply simple, cohesive color themes to create clear, dynamic visual insights.

Publish your Power BI reports to the online service to distribute insights via organized workspaces and share across devices, teams, and the cloud.

Publish a Power BI report to see a report view and a data set, then explore data flows, dashboards, and apps to centralize data, model relationships, and democratize reporting.

Explore data architecture and the Power Query editor to evaluate, load, and transform data in Power BI, with centralized datasets, dataflows, and Data Marts for scalable analytics.

Accelerate Power BI development by using templates with a date table, measures, visuals, and theme; automate setup with one click, and build Power Query foundations for simpler DAX and reporting.

Learn to accelerate Power BI development by building reusable templates with themes, visuals, and a measures table, leverage the query editor, and prioritize architecture simplification over excessive DAX.

Discover how to design analytical solutions in Power BI by applying a framework, building reports and dashboards, and delivering immersive, cloud-based insights via Power BI Desktop and the online service.

Master Power BI modeling with two fact tables by planning in the Power Query stage and query editor, using DAX to sum revenue and costs across dimensions.

Use the power query editor to transform data before loading into Power BI, creating queries that capture raw data and building a date table via analyst hub code.

Centralize your Power BI date table by creating it in a dataflow and using a single enterprise common data sets source to power all reports, simplifying DAX and updates.

Explore optimizing Excel data in Power BI by importing as tables, enforcing intuitive names, and applying query editor transformations to clean, rename, and organize fields for analysis.

Master data transformation in Power BI by managing applied steps, renaming or deleting them, exploring transformations, and using staging queries to avoid circular references in data models.

Optimize Power BI performance by using staging queries, understanding reference versus duplicate, and simplifying the data model to reduce clutter and speed up visuals.

Master merging and appending in Power BI by building centralized lookup tables for dates, warehouses, customers, vendors, and products. Create a supporting query to unify products across sales and inventory.

Apply advanced transformations to optimize Power BI models by building lookup tables, defining product segments, and transforming product data to enhance filtering across sales and inventory.

Centralize core tables in data flows to build a scalable star schema model that reduces data silos and simplifies DAX and visualizations in Power BI.

Close and apply to load queries into the Power BI data model, review and shape tables, and prepare the model for analysis; tomorrow, build relationships to enable analysis and visualization.

Learn how to build clean Power BI data models with one-to-many lookups, filters flowing down the waterfall to the fact table, and avoiding multidirectional or many-to-many relations.

Simplify Power BI reports by improving data modeling, handling many-to-many, bridge tables, and slowly changing dimensions, and favor one-to-many relationships over bidirectional cross filters, merge fact tables when appropriate.

Master DAX and evaluation context in Power BI by simplifying models, splitting insights into focused reports, and delivering a cohesive app-based experience for end users.

Master Power BI data modeling and DAX basics using simple aggregation functions like sum and average, and grasp the evaluation context and initial context driven by filters.

Create quick measures in Power BI—total revenue, total costs, count rows, and distinct customers—from fact tables, then use calculated columns sparingly.

Explore smoothing with moving averages, set a dynamic date range using a parameterized date table, and compare aggregating functions (sum, average, count) with iterating functions (averageX, sumX) in Power BI.

Master simple measures and DAX formatting to quickly create inventory analytics, using stock quantity and total value, then visualize trends with branching measures.

Learn to use iterating functions such as sumx to calculate high value sales (over $5,000) by evaluating row-level logic across the sales data table, with date and relationship context.

Master measure branching in Power BI to derive the percentage of high value sales by referencing measures within other measures, enabling quick, reusable insights across time contexts.

Scale Power BI formulas by applying measure branching to break down results into components and reuse core measures, cards, and patterns from a formula catalog.

Master a core set of DAX functions in Power BI, including measures and calculated columns, and use lookup tables to streamline data models and inventory insights.

Explore best practices for Power BI data models and DAX measures, including using atomic function challenges to build daily DAX and M skills, plus practical inspiration from enterprise showcases.

Master when to use dax, calculated columns, and power query, and optimize Power BI by making measures dynamic within a session, pushing logic upstream, and keeping fact tables thin.

Create a dedicated measures table with folders to organize core and time-related measures. Hide the revenue table and rely on measures and lookup tables for clean modeling.

Master the evaluation context in Power BI by building core measures with aggregating and iterating functions, and recognizing how filters and visuals shape context before calculation.

Extend Power BI analytics with measure branching to create advanced patterns that reference other measures, including percent, averaging, and cumulative patterns, while keeping DAX clear and fast.

Explore DAX patterns, including calculate and table functions, and learn to modify the evaluation context to compute warehouse-specific revenue and compare to total revenue.

Explore advanced DAX patterns with calculate and branch to modify calculation context, build measures like top warehouse revenue, and use filter functions to filter on dimension tables.

Explore advanced DAX filtering to calculate total transactions across sales and inventory using filter and calculate, applying and/or operators to model low inventory and reorder conditions.

Explore DAX table functions and virtual tables to craft calculations, leveraging filter and all variations, distinct and time intelligence functions for advanced data analysis.

Discover how DAX table functions create virtual tables in Power BI by creating a new table in the modeling area, using filter, values, and all to visualize results and context.

Explore DAX tables: physical, calculated, and virtual, and learn their differences, how they relate to the data model, and the SQL view versus CTE analogies.