
Explore moving from descriptive to predictive analytics using Power BI, augmented analytics, AI-enabled visuals, and AutoML, with hands-on practice and dashboard development.
Explore descriptive, diagnostic, prescriptive, and predictive analytics with Power BI to analyze sales by product and region across time, uncover why outcomes differ, and forecast future results.
Explore traditional BI, self-service BI, and augmented BI, and learn how Power BI supports self-service and augmented analytics while also serving traditional BI.
Power BI enables self-service data visualization, letting end users create their own reports and dashboards. Access desktop, service, and mobile options, share insights, and leverage augmented analytics and auto ML.
Discover AutoML and no-code tools that automate model building for non-coders, and grasp independent and dependent variables, algorithms, and model accuracy for forecasting and classification.
Assess your reporting landscape by listing all reports and classifying them as descriptive, diagnostic, prescriptive, or predictive to guide BI toward diagnostic, prescriptive, and predictive analytics with AutoML.
Download Power BI Desktop for 64-bit or 32-bit systems and publish dashboards to the free service; upgrade to Pro or Premium for advanced analytics, including logistic regression and classification models.
Explore the Power BI interface, create reports, import data, build table relationships, apply filters, and use a variety of visuals, including third-party options.
Import and load data from Excel into Power BI from multiple sources, preparing ABC company sales data with product types, segments, country, date, price, profit, and discounts for visuals.
Transform and clean data in Power BI by importing data, transforming datasets, removing columns and rows, renaming fields, adjusting data types, removing blanks and duplicates, and applying changes.
Demonstrates data transformation techniques in Power Query Editor, including handling nulls, splitting a hyphen-delimited column to extract AQI and numbers, merging columns, and using group by.
Explore data transformation in Power BI, including filling null values with fill down, and pivoting columns to group and summarize sales by brand, using Power Query Editor.
Learn how to build visuals in Power BI using visualization options, filter reports by product or country, and create and format a text box banner for dashboards.
Build and format a card visual in Power BI to display total profit, total sales, and total discounts, with borders, titles, alignment, font size, color, and data label settings.
Learn to build a stacked column chart, standardize decimal places, format data labels, and apply a date hierarchy to compare sales by product lines and deals across years.
Learn to create and format a stacked bar chart in Power BI, with product on the axis, sales as values, and legend settings, plus borders, font size, and background options.
Build a multi-row card to display the top three products by profit. Use top filters, customize the title and colors, and create a dashboard that auto-updates and can be shared.
Create a treemap to compare units sold by product. Format borders and data labels, adjust font sizes, and center the title to build a clear Power BI dashboard.
Create a pie chart on a new page in Power BI, set product as the legend and profit as values, and format legend, data labels, and title for clear visualization.
Create a dual axis chart in Power BI using a line for units sold and a stacked column for profit by segment, then format with borders, shadows, and axis options.
Create a ribbon chart in Power BI to display performance and ranking across countries and quarters, with customizable tooltips showing profit and optional trend lines.
Learn to visualize profit across products with a bar chart and filter it by country using a slicer, including setting multi-select and single-select options and proper visual placement.
Create a map visualization that displays profit, sales, and discounts by country, using circle size to reflect profit and tooltips to show detailed metrics.
Create a dedicated tooltip page, set its size to tooltip, and use it as a custom tooltip for a map chart; hover to view sales and profit.
Create a final Power BI chart to compare country performance by profit and output, grouping United States and India, and adjust color and font size for clarity.
Explore six dax function types: date and time, lookup value, logical, math, percentile, and text, and learn where to write them—measure, new column, quick measure, and conditional column—in Power BI.
Create a gauge chart in Power BI to visualize profit against a target and a maximum value by defining new measures for the target and max.
Create a conditional column in Power BI using DAX and Power Query Editor to classify profit or loss, with nested if and switch alternatives and basic debugging tips.
Create a max value measure with max and maxx across tables, then format date columns using format to display month and year.
Learn to use year-to-date in Power BI by building a revenue by customer matrix, adding a grand total percentage, and creating a YTD quick measure for a card visual.
Develop expertise using the calculate function to filter data and create a month-to-date measure within a matrix visual, displaying discount by country and financial segment.
The video demonstrates how to use the lookup function in Power BI to fetch regional data from a second table using a common country key, mirroring vlookup in Excel.
Learn to perform what-if analysis in Power BI by building scenario-based sales visuals, adding a constant target line, and creating sales potential measures to compare actual versus projected outcomes.
Learn to use the key influencers visual in Power BI to quickly identify drivers of profit and sales, including country, government segment, product, and discounts, for a clear high-level explanation.
Learn to establish a one to one relationship in Power BI between a product table and sales table, then create a calculated column for total sales using related unit price.
Explore how to establish one to many relationships across tables using common keys like customer ID and product ID, enabling computations for different visualizations in Power BI.
Learn how to merge two tables in power query using product and sales data, exploring left outer, right outer, full outer, and inner joins with a visual venn diagram.
Publish your Power BI desktop visuals to the web service, sign in, and publish to a workspace. Share, export, embed, manage permissions, and track who opens the report.
Implement row level security to restrict dashboards by country, using country filters and rules like Canada and Germany sales managers, publish securely, and test access with view as.
Explore Benford Law and its use in fraud detection by analyzing the leading digit distribution in Power BI, comparing actual versus Benford Law expectations to flag potential noncompliance.
Learn to build an aging analysis dashboard in Power BI by creating age and age-group columns, grouping delays into 30-day intervals, and visualizing revenue and aging counts with interactive slicers.
Recent Updates:
June 2023: Added a video lecture on Clustering and Segmentation
Nov 2022: Updated the course with using DAX functions like Calculate and Lookup
June 2022: Added a case study on using Python (programming language) in PowerBI environment
May 2022: Added a case study on Benford Law (Benford law is used to detect fraud)
April 2022: Added a case study on Ageing Analysis
Course Description:
In the last 50 years, the world of reporting, analytics and business intelligence (BI) has seen many of evolutions. The notable ones are the rise of self service BI and augmented analytics. Businesses are no longer content with descriptive or diagnostic analytics. The expectation for prescriptive and predictive analytics has become the new normal.
Machine learning and Artificial Intelligence technology has also evolved significantly in the last decade and the notable evolution is the rise of Auto ML - the no code machine learning approaches. Auto ML has significantly democratized predictive analytics. End users can predict the future outcomes of businesses in a few (mouse) clicks.
Power BI epitomizes the recent trends in business intelligence (BI) and augmented analytics for interactive, easy to use and self-serve dashboards & auto ML capabilities.
This is a comprehensive course on Power BI covering the following:
Data Transformation
DAX
Data Models
Simple & complex visuals
AI enabled visuals
Auto ML: Concepts and no code approaches to forecast future
There are no pre-requisites for this course, although knowledge of excel would be advantageous.
There are actually 2 courses (Power BI and AI - ML) in this course and both are covered in great detail. Whether your objective is to learn power bi or machine learning or both, this course will deliver the goods for you.