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Course Outline
• M01: Get Started with Microsoft Data Analytics
• M02: Get Data in Power BI
• M03: Clean, Transform, and Load Data in Power BI
• M04: Design a Data Model in Power BI
• M05: Create Model Calculations using DAX in Power BI
• M06: Optimize Model Performance
• M07: Create Reports
• M08: Create Dashboards
• M09: Perform Analytics in Power BI
• M10: Implement Row Level Security
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Certification Areas (PL-300)
Microsoft Certified: Power BI Data Analyst
Associate
Exam PL-300: Microsoft Power BI Data Analyst
Skills measured
•Prepare the data (25-30%)
•Model the data (25-30%)
•Visualize and analyze the data (25-30%)
•Deploy and maintain assets (15-20%)
https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300
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Install Power BI Desktop
• Microsoft Store
• Microsoft Download Center:
https://www.microsoft.com/en-us/download/details.aspx?id=58494
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BI Tools:
Others:
o Tableau
o Qlik Sense
o SAP Crystal Reports
o Google Data Studio
Microsoft
o Power BI Desktop
o Power BI Service
o SQL Server Analysis Services
o Power BI Mobile
o Power BI Gateway
o Power BI Report Server
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Overview of Data Analysis:
Descriptive
Diagnostic
Predictive
Prescriptive
Cognitive
Components of data analysis:
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Descriptive Analytics:
Descriptive analytics help answer questions about what has happened based on
historical data. Descriptive analytics techniques summarize large datasets to
describe outcomes to stakeholders.
An example of descriptive analytics is generating reports to provide a view of an
organization's sales and financial data.
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Diagnostic analytics:
Diagnostic analytics help answer questions about why events happened. Diagnostic
analytics techniques supplement basic descriptive analytics, and they use the findings
from descriptive analytics to discover the cause of these events.
Includes these processes:
1. Identify anomalies in the data. These anomalies might be unexpected changes in a
metric or a particular market.
2. Collect data that's related to these anomalies.
3. Use statistical techniques to discover relationships and trends that explain these
anomalies.
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Predictive analytics:
Predictive analytics help answer questions about what will happen in the future.
Techniques include a variety of statistical and machine learning techniques such
as neural networks, decision trees, and regression.
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Prescriptive analytics:
Prescriptive analytics help answer questions about which actions should be taken
to achieve a goal or target. By using insights from prescriptive analytics,
organizations can make data-driven decisions.
Prescriptive analytics techniques rely on machine learning as one of the
strategies to find patterns in large datasets.
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Cognitive analytics:
Cognitive analytics attempt to draw inferences from existing data and patterns,
derive conclusions based on existing knowledge bases, and then add these
findings back into the knowledge base for future inferences, a self-learning
feedback loop. Cognitive analytics help you learn what might happen if
circumstances change and determine how you might handle these situations.
Effective cognitive analytics depend on machine learning algorithms, and will use
several natural language processing concepts to make sense of previously
untapped data sources, such as call center conversation logs and product
reviews.
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Roles in Data Analysis:
Business analyst
Data analyst
Data engineer
Data scientist
Database administrator
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Business analyst:
A business analyst is closer to the business and is a specialist in interpreting the
data that comes from the visualization. Often, the roles of data analyst and
business analyst could be the responsibility of a single person.
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Data analyst:
A data analyst enables businesses to maximize the value of their data assets
through visualization and reporting tools such as Microsoft Power BI.
Data analysts are responsible for profiling, cleaning, and transforming data.
Their responsibilities also include designing and building scalable and effective
data models, and enabling and implementing the advanced analytics capabilities
into reports for analysis.
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Data engineer:
Data engineers provision and set up data platform technologies that are on-
premises and in the cloud.
They manage and secure the flow of structured and unstructured data from
multiple sources.
Data engineers also ensure that data services securely and seamlessly integrate
across data platforms.
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Data scientist:
Data scientists perform advanced analytics to extract value from data. Their work
can vary from descriptive analytics to predictive analytics.
Descriptive analytics evaluate data through a process known as exploratory data
analysis (EDA).
Predictive analytics are used in machine learning to apply modeling techniques
that can detect anomalies or patterns. These analytics are important parts of
forecast models.
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Database administrator:
A database administrator implements and manages the operational aspects of
cloud-native and hybrid data platform solutions that are built on Microsoft Azure
data services and Microsoft SQL Server.
A database administrator is responsible for the overall availability and consistent
performance and optimizations of the database solutions.
They work with stakeholders to identify and implement the policies, tools, and
processes for data backup and recovery plans.
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5 Areas of Data Analysis:
Prepare
Model
Visualize
Analyze
Manage
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Prepare:
Data preparation is the process of taking raw data and turning it into information
that is trusted and understandable. This involves profiling, cleaning and
transformation to make data ready for model and visualization. Data preparation
can often be a lengthy process, which takes a data analyst through a series of
steps and methods to put the data in proper context and a state that eliminates
poor data quality and allows it to be turned into valuable insights.
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Model:
Once the data is in a proper state, it is ready to be modelled. Data modelling is
the process of determining how the tables are related to each other. This is done
by defining and creating relationships between the tables.
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Visualize:
This is the task where you get to bring your data to life! The goal of this task is to
ultimately solve the business problem. A well-designed report should tell a
compelling and impactful story
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Analyze:
This task involves understanding and interpreting the information that is
displayed on the report. A data analyst should understand the analytical
capabilities of Power BI and use those to find insights, identify patterns and
trends, predict outcomes, and then communicate those insights in such a way
that everyone can understand.
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Manage:
There are many components in Power BI, including reports, dashboards,
workspaces, datasets and more. As a data analyst, you are responsible for the
management of these Power BI assets, overseeing the sharing and distribution
of items such as reports and dashboards, ensuring the security of Power BI
assets.
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Demo 1.1: Get Started with Power BI
• Power BI Interface
• Tables and Attributes
• Table Relationship and schema
• First Daxing
• First Visuals
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Import
By default, when you extract data from a data source:
1. Source is at its place
2. Another copy of the data source is created in Power BI Desktop as part of the pbix file.
This copy is called Dataset
3. After Extraction, the link to the source is broken
Later, if the source is updated, you would need to sync your source data with data set
manually. You do this by Refreshing the Dataset.
During Refresh, the link is established and data is updated.
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Direct Query
In direct connection:
• The connection is always live.
• There is no copy of the data given to the Power BI Desktop. The data set will be empty. Will
have structure, but no data.
• When you publish the data to service, there is no data uploaded to BI service.
Note: Changing the Storage mode of a table to Import is an irreversible operation. After this
property is set, it can't later be changed to either DirectQuery or Dual.
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Demo 1.3: Get data from SQL Server Using DirectQuery
SQL Server: Data-AI
Database: TailspinToys2020-US
Data Connectivity mode: DirectQuery
Choose these tables:
a. Product (Dim table)
b. Sales (Fact table)
Create visual to view Price per Product
Category
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4 Publish the report
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Issues with Data
Several columns contain errors
Some columns contain null values
ID columns may have duplicate values
• A single column may have combined several information, such as
address
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Advantages of Clean Data:
More accurate aggregations and calculations are produced
Tables are organized, where users can find the data in an intuitive
manner
Duplicates are removed, making data navigation simpler. It will also
produce columns that can be used in slicers and filters
A complicated column can be split into two simpler columns. Multiple
columns can be combined into one column for readability
Codes and Integers can be replaced with human readable values
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M04: Design a Data Model in Power BI
The databases you connect to either on-premises or in the cloud store data in tables. One
table might store data about products, while another stores data about orders, while
another stores data about salesperson commissions. Relationships define how data in
one table is related to data in another table.
A powerful component of self-service BI is the ability to compare data to find similarities,
differences, and trends.
Before you can do that, you must create a data model that defines the relationships
between different data components.
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M05: Create Model Calculations Using DAX in Power BI
Data Analysis Expressions (DAX) is a programming language that is used
throughout Microsoft Power BI for creating calculated columns, measures,
and custom tables.
Dax functions are available in Excel and Power BI both. However, not all
Power BI functions are available in Excel.
In this module, you will be able to:
• Create calculated columns
• Build measures
• Use Calculate function
• Implement time intelligence
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Student Practice: 4. Create DAX Calculations in Power BI
Desktop, Part 1
Student Practice: 5. Create DAX Calculations in Power BI
Desktop, Part 2
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M06: Optimize Model Performance
The performance optimization process involves minimizing the size of the data model, which
includes:
Ensuring that the correct data types are used
Deleting unnecessary columns and rows.
Avoiding repeated values.
Replacing numeric columns with measures.
Reducing cardinalities.
Analysing model metadata.
Summarizing data where possible.
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Some Visuals Explained
• Table and Matrix Visualizations
• Bar and Column Charts
• Line and Area Charts
• Pie Chart, Donut Chart, and
Treemaps
• Combo Charts
• Card Visualization
• Funnel Visualization
• Gauge Chart
• Waterfall Visualization
• Scatter Chart
• Maps
• Slicer Visualization
• Q&A Visualization
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• Table and Matrix Visualizations
Table is a 2 dimensional grid that
contains related data in a logical series
of rows and columns.
Matrix looks similar to table. However, it
allows you to select one or more elements
(rows, columns, values) in matrix.
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Bar and Column Charts
Bar and Column charts present specific data across different categories in a
stacked or clustered format.
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Line and Area Charts
Line and Area charts help you present trends over time. The basic area chart is
based on the line chart, with the area between axis and line filled in. The main
difference between these 2 chart types is that the area chart highlights the
magnitude of change over time.
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Pie and Donut Charts
Pie charts and Donut charts show you the relationship of parts to the whole by
dividing the data into segments. These charts are best suited for illustrating
percentages, such as the top 5 sales by product or country etc.
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Treemaps
Like Pie charts and Donut charts, Treemaps
show you the relationship of parts to the
whole by dividing the data into segments. A
treemap is ideal to visualize:
• Large amounts of hierarchical data
• Proportions between each part and the
whole
• The distribution pattern of the measure
across each level of categories in the
hierarchy.
• Attributes, by using size and colour
coding.
• Spot patterns, outliers, most-important
contributors, and exceptions,
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Combo Charts
A Combo chart is a combination of a
column chart and a line chart. It can
have 1 or 2 y-axes. With combo
charts, you can:
• Compare multiple measures with
different value ranges.
• Illustrate the correlation between
2 measures.
• Identify whether one measure
meets the target that is defined
by another measure.
• Conserve space on your report
page
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Card Visualization
A card displays a single value: a single
data point. This is ideal to visualize
important statistics that you want to
track in your report or dashboard, such
as Total value, YTD sales, or year-over-
year change.
A multirow card visual displays one or
more data points, with one data point
for each row.
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Funnel Visualization
A funnel visualization displays
a linear process that has
sequential connected stages.
For example, they are useful
for representing a workflow,
such as moving from a sales
lead to a prospect, through to
a proposal and sale.
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Gauge Chart
A Gauge chart has a
circular arc and
displays a single value
that measures progress
towards a goal or
target.
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Waterfall Visualization
A Waterfall visualization or Bridge chart shows running total as values are added or subtracted, which
is useful in displaying a series of positive and negative changes
It can be used to:
• Visualize change over time or
across different categories.
• Audit the major changes that
contribute to the total value.
• Plot an organization’s annual profit
by showing various sources of
revenue.
• Illustrate the beginning and ending
headcount for an organization in a
year.
• Visualize how much money you
earned and spend each month
and the running balance for your
account.
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Student Practice: 6. Design Reports in Power BI Desktop
Student Practice: 7. Enhance Reports in Power BI Desktop
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M08: Create Dashboards
A Power BI dashboard comprises of visuals that are taken from a single report or
multiple reports. The purpose of dashboards is to showcase the main, most
important highlights of the story.
Power BI dashboards is a feature only included in Power BI Service. You can also
view dashboards on mobile devices, though you can’t build them there.
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Dashboards vs. Reports
• Dashboards can be created from multiple datasets or reports
• Dashboards do not have the Filter, Visualization, and the Fields pane that are
there in Power BI Desktop. That means you can’t add new filters and slicers,
and you can’t make edits.
• Dashboards can only be a single page, whereas reports can be multiple pages.
• You can’t see the underlying dataset directly in a dashboard. However, you can
see the dataset in Power BI reports under Data tab.
• Both dashboards and reports can be refreshed to show the latest data.
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Mod 09: Perform Analytics in Power BI
This module outlines the advanced analytic capabilities of Power BI. Mostly Line Charts and Scatter
Charts are used.
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Mod 10: Row Level Security (RLS)
You can also share a single report but have users see different data
according to their job role.
For Instance, you want to make one report where employees in a specific
department can only see the sales for that department.
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Configuring RLS Using Static Method
1.Create a report in Microsoft Power BI Desktop.
1. Import the data.
2. Confirm the data model between both tables.
3. Create the report visuals.
2.Create RLS roles in Power BI Desktop by using DAX.
3.Test the roles in Power BI Desktop.
4.Deploy the report to Microsoft Power BI service.
5.Add members to the role in Power BI service.
6.Test the roles in Power BI service.
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Configuring RLS Using Dynamic Method
You can configure row-level security exactly the way you configured it previously, with only a single
change. Instead of creating multiple roles, you only need to create one role.
Instead of the fixed string, such
as Game or Clothing, this uses a DAX
function in the row-level security filter.
The userprincipalname() function will
compare the email address from the
Employees table with the email that the
user entered when signing in to Power BI
service.
Editor's Notes
If the source is moved or renamed, change the data source location: Home->Transform Data-> Data Source Settings
So, data is secure. But there is a challenge. In Power BI Service, the report will not show any data. So, this is totally useless.
So, you have to establish the connection from BI service to the SQL Server data source. This is possible through Gateway
Show video on PowerQuery
Incorrect data types will prevent you from creating certain calculations, deriving hierarchies, or creating proper relationships with other tables.
The Prerequisite: Same data structures
Combine two Orders datasets (Orders_SEA and Orders_Europe)
Prerequisite: The datasets can have different data structures but must have a common column
Objective: Merge Products and Stock tables so that we can have a report that shows product by product stock level and Stock Value
We have already completed data modelling in our previous module
Q&A: Types of keys, Types of relationships, Types of Tables, Types of schema, cross-filter direction
In the lab, students have created hierarchies and Field properties.
From a report user’s perspective, poor performance is characterized by report pages that take longer to load and visuals taking more time to update. This poor performance results in a negative user experience.
Poor performance is a direct result of a bad data model, bad Data Analysis Expressions (DAX), or a mix of both. The process of designing a data model for performance can be tedious, and it is often underestimated. However, if you address performance issues during development, you will have a robust Power BI Data Model that will return better reporting performance and a more positive user experience.
A smaller sized data model uses less resources (memory) and achieves faster data refresh, calculations, and rendering of visuals in reports.
Visuals are a fundamental part of your report because they help your report audience connect and interact with the information to make informed business decisions quickly.
Visuals allow you to share data insights more effectively and increase comprehension, retention and appeal.
