1. Microsoft Power BI Bootcamp
LearniT

2. “Genius is one percent inspiration, ninety-nine percent perspiration” – Thomas Edison
Does this sound like you?
Grab data from one or more sources
Prep data (often using VLOOKUP)
Create Pivots over the prepared data
Create reports from Pivot data (sometimes the Pivots ARE the reports)
Share with colleagues via or network drive
Spend at least half of time re-creating these reports, updated with the latest data, on a recurring basis
The world is changing in our favor

3. Data Modelling (Power Pivot)
Power BI Excel
Power BI
Dashboards
Stunning Visuals
Mobile Consumption
Alerts & Live Data
Excel
Pivot Tables
Pivot Charts
OLAP Tools
VBA
DAX Functions
Data Modelling (Power Pivot)
ETL (Power Query)

4. Day 1
By the end of today…
By the end of the day, we will have transformed local data, web data, and a little curiosity into stunning visual analysis and business insights.
Student will actual make the reports on this slide by the end of the day.

5. Our GOALS today are…
Analyze our company’s Wages and Sales across the United States
Perform multiple Analysis to discover insights
Visualize Analysis to make it “consumable”
To achieve this we’ll need to
1. Fetch data from the MANY different places we keep it
2. Clean up the data so we can analyze it
3. Use Power BI to discover insights
4. Learn to use different visual types
5. Organize our insights into a convenient report

6. Today we are going to cover
Getting Started 
What is Power BI? 
Getting the Tools 
Power BI and Excel 
Discovering Insights with PBI (Data Analysis Objectives) 
PBI Eco-System 
Getting and Transforming Data with Power Query
Concepts and Objectives 
Importing from Various Sources 
Appending and Merging Queries together 
Data Normalizing Strategies 
Reusing Queries
Introduction to Power BI Desktop
Concepts (Power BI compared to Excel) 
Importing Data from Excel 
Issue Queries Directly from Power BI 
Building Reports 
Creating Simple Visualization 
Telling a Story with your PBI Reports
Creating Slicers
Using Charts as Slicers

7. Get & Transform (aka Power Query)
Extract Transform Load 1 2 3 4 5 6 7 8 9
Power Query is what is known as an ETL tool. ETL = Extract, Transform and Load
Extract: connect to data from various places
Transform: format and organize your data so it is ready for analysis. Power Query memorizes all of your normalization preferences so it can repeat them.
Load: Load the data to Excel and throw it in a Pivot Table. OR load it as a “Connection Only” for later use with other data. OR load it straight to your data model for multi-dimensional analysis!

8. Normalizing Concepts
Smallest Meaningful Value Break down the fields to their smallest meaningful value. Columns with last and first names need to be broken down into separate columns with first name, and last name.
Remove Un-Necessary Data Any columns or rows that are meaningless, or will not be used for your analysis should be removed.
Errors Different kinds of errors should be handled differently, but they should all be addressed before moving on
Compliant Field Names You don’t want field names with weird characters (gets in the way of formulas and functions). Additionally, it is best for fields to (columns) have names that explicitly refer to the kind of content contained there. Relatable fields should be named the same between data sets (or named something very similar) to avoid headaches down the road.
Standardize Data A lot of data comes in with interfering characters, like dollar signs, or spaces that will interfere with analysis.
Standardize Data Types Make sure each column has the appropriately applied data type. Numbers = numbers, dates = dates, currency, text, etc.
Create Key / ID Fields If you are later going to be making relationships between data sets, it is wise to add key columns with identifying numbers or ID’s that can later be used to establish relationships.
Using Power Query for Normalizing Power Query has a lot of normalizing strategies that require a lot less time and energy that performing the same tasks in Excel.

9. LAB – Building Reports Revenue – Actual vs. Projected
1hr
Revenue – Actual vs. Projected
Page ___ of the Student Guide
Wage vs. Revenue Comparison
Page ___ of the Student Guide

10. Day 2
By the end of today…
By the end of the day, we will take data from many sources and relate and model to have a comprehensive related set of data.

11. By the end of today… Day 2 Mobile Consumption
By the end of the day, we will take multiple reports and publish them to the web.
Student will make the dashboard on this slide by the end of the day.

12. Our GOALS today are… Analyze our Sales, Profits, and Returns over time
See these analysis segmented by Product, Product Subcategory and Product Category
Visualize the analysis
Share the analysis in a consumable Dashboard
To achieve this we’ll need to
1. Combine yesterday’s geography data with our company’s transaction history (new data)
2. Create some measures for our desired analysis
3. Put this data on a time scale (calendar table)
4. Visualize the analysis

13. Today we are going to cover
Data Modeling 
Concepts of Power Pivot and "Data Model" in Excel 
Importing Data from Various Sources 
Modelling & Relating  
Concepts of creating a Data Model (Decision Structures) 
Creating Relationships 
Creating Calculated Columns 
Designing Measures  
Building Hierarchies 
Data Analysis Strategies 
Power BI 
Importing a Data Model from Excel 
Data Modelling directly in Power BI 
Importing Additional Data 
Creating Calculated Columns and Measures in Power BI 
DAX expressions 
Power BI Cont.
Introduction to Time Intelligence
Calendar Tables 
Essential Time Intelligence DAX expressions 
Building Interactive Reports 
Concepts: Reports for Consumption 
Disconnecting Visuals  
Drilling Down with Hierarchies 
Power BI Online 
Dashboards vs. Reports 
Pinning Visuals 
Excel Objects Online 
Publish and Share Dashboards and Reports  
Using Q&A 
Designing for Better Q&A 

14. Our Data Model Our Data Model Looks like this Product Category
Geography
Product Subcategory
Stores
Product
FactSales
Channel
Promotion

15. Data “Model” Basic Structure
Lookup Table
Data Table
Fact Table
Transaction Table
Lookup Table
Lookup Table
Lookup Table
Flow of Information

16. Real-World models look more like

17. What is a Measure? This is a measure These are measures One Number
Many Numbers
This is a measure
These are measures
One Number
Measures are a mathematical aggregation. A measure takes many numbers and collapses them down to a single value. That’s how summary happens.

18. No “Naked” Measures Total Sales = Fact Sales [ Sales Amount]
Because measures are designed to take MANY numbers and condense them into a single value, they must always have an aggregation function wrapped around columns of data.
Total Sales = Fact Sales [ Sales Amount]
Total Sales = SUM ( Fact Sales [ Sales Amount] )

19. Profit Margin = Total Profit / Total Sales
Nesting Measures
Total Profit
Total Sales
“Ancestor Measures”
Profit Margin = Total Profit / Total Sales
“Derived Measures”

20. Let’s Keep a Measure Map
Total Profit
Total Sales
Total Transactions
Days Selling
Total Margin
Sales per Transaction
Sales per Day

21. % Transactions Refunded
Measure Map
Total Sales
Total Profit
Total Transactions
Days Selling
Refunds
Promo Sales
Regular Sales
Total Margin
Sales per Transaction
% Total Sales Refunded
Sales per Day
Net Sales
% Sales on Promo
% Sales No Promo
% Transactions Refunded

22. CALCULATE Will likely be your favorite function
Think of it like the SUMIF
= CALCULATE ( FUNCTION ( Table[Column] ) , Table[Column] = ThingtoFilterFor )
SUM IF

23. CALCULATE (using measures instead of functions)
If you have a measure already made, you can use it instead of a function
= CALCULATE ( [ MEASURE ] ) , Table[Column] = ThingtoFilterFor )
SUM IF

24. Measures to CALCULATE Total by “Promo”
Sales not on Promo
Table: FactSales Measure Name: RegularSales Formula: = CALCULATE ( [TotalSales] , FactSales[PromotionKey] = 1 ) Format: Currency
Sales NOT on Promo
Table: FactSales Measure Name: PromoSales Formula: = CALCULATE ( [TotalSales] , FactSales[PromotionKey] <> 1 ) Format: Currency
Let’s create a couple measures to determine Total Sales ON Promo, and Total Sales not on Promo

25. Measures to CALCULATE % Promo
% Sales Not on Promo
Table: Fact Sales Measure Name: % Sales NoPromo Formula: = [RegularSales] / ( [TotalSales] ) Format: Number, Percent
% Sales ON Promo
Table: Fact Sales Measure Name: % Sales OnPromo Formula: = [PromoSales] / ( [TotalSales] ) Format: Number, Percent
Students can try to make these themselves.

26. LAB Analysis Measures
Objective: build measures to accomplish the following
Aggregate Refunds (SUM RefundAmount)
Net Sales (Total Sales – Refunds)
Percent of Total Sales that was refunded
Percent of Total Transactions that were refunded

27. LAB – Analysis Measures
Refunds
Table: FactSales Measure Name: Refunds Formula: = SUM ( FactSales[ReturnAmount] ) Format: Currency
NetSales
Table: FactSales Measure Name: NetSales Formula: = [TotalSales] - [Refunds] Format: Currency
Percent of Sales Refunded
Table: FactSales Measure Name: % TotalSales Refunded Formula: = [Refunds] / [TotalSales] Format: Percent
Percent Transactions Refunded
Table: FactSales Measure Name: % Transactions Refunded Formula: = CALCULATE ( COUNT ( FactSales[ReturnAmount]) , FactSales[ReturnAmount] > 0 ) / COUNT ( FactSales[ReturnAmount]) Format: Percent
15min

28. LAB – Analysis Measures
Percent Transactions Refunded
Table: FactSales Measure Name: % Transactions Refunded Formula: = CALCULATE ( COUNT ( FactSales[ReturnAmount]) , FactSales[ReturnAmount] > 0 ) / COUNT ( FactSales[ReturnAmount]) Format: Percent
This measure has no ancestor dependencies.
If you preferred, how would you change it to have ancestor dependencies?

29. LAB – Analysis Measures
% UnitsSold that were Refunded
Transactions per Day Selling
Transactions per Day
Ask Garrett and Cara to suggest some!
IF YOU WOUD LIKE, these are additional measures that can be tried

30. Let’s do this in Power BI

31. % Transactions Refunded
Measure Map
Total Sales
Total Profit
Total Transactions
Days Selling
Refunds
Promo Sales
Regular Sales
Total Margin
Sales per Transaction
% Total Sales Refunded
Sales per Day
Net Sales
% Sales on Promo
% Sales No Promo
% Transactions Refunded

32. Calendar Tables should have the following
At least one column of “date” data type
Contain exactly one row per day
Completely consecutive gaps (don’t skip weekends, even if you’re closed)
Be related to your Data Tables (Sales)
Contain columns for all of your desired groupings (like MonthName)
Ideally only spans the dates of your business

33. Calendar & Calendar Auto
Calendar ( StartDate , EndDate )

34. Smart Calendar Function
CALENDAR ( StartDate , EndDate )
DATE(
YEAR( MIN( DateField ) ),
MONTH( MIN( DateField ) ),
DAY( MIN( DateField ) ) )
DATE(
YEAR( MAX( DateField ) ),
MONTH( MAX( DateField ) ),
DAY( MAX( DateField ) ) )

35. Add the Meta Data in columns
Year = Year ( Dates [ DateKey ] )
MonthNum = Month ( Dates [ DateKey ] )
MonthName = Format ( Dates [ DateKey ] ), “MMMM” )
MonthNameAbrv = Format ( Dates [ DateKey ] ), “MMM” )
DayofMonthNum = Day ( Dates [ DateKey ] )
DayName = Format ( Dates [ DateKey ] ), “DDDD” )
DayNameAbr = Format ( Dates [ DateKey ] ), “DDD” )
WeekdayNum = Weekday ( Dates [ DateKey ] )
QuarterNum = ROUNDUP( MONTH( Dates [ DateKey ] )/3 , 0 )
QuarterName = CONCATENATE( “Qtr ” , Dates[QuarterNum] )
Month & Year = CONCATENATE( Dates[MonthNameAbrv] &“ “, Dates[Year] )
Year & Month = Dates[Year] & Dates[MonthNum] 

36. LAB 1 Lab 2 – Make your Own Promotion / Sales Correlation Analysis
Profit
Which product categories have the highest profit margins? Which product generate the most dollars in profit? What correlations can be drawn here?
Compare Profit in each month, to the profit margin in each month. (Column and Line chart)
Refunds
What portion of total sales is refunded?
Which Product Category has the most refunds? The highest refund amount? The highest refund:sales ratio?
What time of year brings in the most refunds? Is there any correlation between promotions being run and refunds?
Sales & Transactions
Which days typically have the highest number of transactions? Which days have the highest sales typically?
Are the monthly sales trends consistent each year? Which months sell the best? How does this compare to the promotions being offered at the time?
Which Product Category is been declining in sales? Which has been steady?
Locations
Which locations have been selling the best?
Which locations have the highest refunds?
Which locations have the highest profit margin?
Which locations have the most transactions?
Transaction to profit ratio?
45min
Promotion / Sales Correlation Analysis
Page ___ of the Student Guide

37. Measure Map Total Discount Total Sales Total Profit Total Transactions
Days Selling
Refunds
Sales : Discount Ratio
Promo Sales
Regular Sales
Total Margin
Sales per Transaction
Sales per Day
Net Sales
% Total Sales Refunded
Promo Sales YTD
% Sales on Promo
% Sales No Promo

38. Day 3
By the end of today…
By the end of the day, we will have transformed local data, web data, and a little curiosity into stunning visual analysis and business insights.
Student will actual make the reports on this slide by the end of the day.

39. Our GOALS today are… To achieve this we’ll need to
1. Fetch data from the MANY different places we keep it
2. Clean up the data so we can analyze it
3. Use Power BI to discover insights
4. Learn to use different visual types
5. Organize our insights into a convenient report

40. Today we are going to cover…
Querying SharePoint Lists
UnPivot Data
Re-Using M Script
Grouping Data
Writing M Script
Calendar Tables with M
Custom Calendars (445)
Custom Functions in Power Query
Time Intelligence in Depth
DatesToDate Functions
YTD
QTD
MTD
DATEADD
FIRSTDATE and LASTDATE
ENDOFMONTH, STARTOFYEAR
CLOSING BALANCE MONTH. QUARTER, YEAR
DATESBETWEEN

41. Un-Pivoting Data – Examples of Bad Data
Sometimes the data is actually a Pivot Table
Sometimes a person just subtotaled manually

42. Un-Pivoting Data - Result
Example from Chandoo

43. The Principle of Un-Pivoting Data
Any Data that looks like this…
Should be turned in to this

44. Storyboard – draw lines for story connections
UnPivot & Copying Script
Data: Competitor Data
Tool: PQ
M Script
Data: Competitor Data
Tool: PQ

45. Measure Map First Date Last Date Total Discount Total Sales
SalesYTD
Sales Fiscal YTD
Sales MTD
Sales QTD
Total Sales Prev Month
First Date
Sales Growth by Month
Last Date
Total Discount
Total Sales
Total Profit
Total Transaction
Days Selling
Refunds
End of Period
Sales : Discount Ratio
Promo Sales
Regular Sales
Total Margin
Sales per Transaction
Sales per Day
Net Sales
% Total Sales Refunded
Promo Sales YTD
% Sales on Promo
% Sales No Promo

46. Day 4
By the end of today…
By the end of the day, we will have transformed local data, web data, and a little curiosity into stunning visual analysis and business insights.
Student will actual make the reports on this slide by the end of the day.

47. Our GOALS today are… To achieve this we’ll need to
1. Fetch data from the MANY different places we keep it
2. Clean up the data so we can analyze it
3. Use Power BI to discover insights
4. Learn to use different visual types
5. Organize our insights into a convenient report

48. Today we are going to cover…