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Course Outline
1. Building Power BI Desktop reports
Purpose: You will prepare a series of Power BI visualizations based on raw data.
- Setting up the Power BI environment
- Build a Power BI report based on data from a single spreadsheet.
- Visualization components: Chart, card, table, matrix, indicator and map.
- Working with visualizations - general rules for linking to data and formatting objects.
- Filtering a report: filtering by selection, using the filter panel and the fragmenter.
- Data model: A report created based on a data model - a set of relationally linked tables, taken from a single spreadsheet. Import, transform and clean the data in Power Query. Automatic and manual creation of relationships between tables. Principles of creating and types of relationships.
- Data integration: Report created based on distributed data sources. Working with Power Query. The most popular data sources from which you can retrieve data for Power BI. What is the Power Query M language and what capabilities it has.
- Data sources for Power BI: CSV files, Excel, JSON, XML, PDF. Tables published on the Internet. OData streaming data. Importing files from a folder. SharePoint file libraries. SQL databases.
- Working with queries: Joining and merging tables. Tracking query dependencies. Handling duplicate values. PIVOT-type transformations. Grouping, counting and aggregation of data.
- Dynamic queries: variables (parameters) in query optimization. Creation, management and handling of parameters from the Power BI report level.
2. Power BI reports with DAX elements
Purpose: Building a Power BI report with an introduction to the DAX language
- DAX language and model in Power BI: what it is and its uses.
- Components of the DAX data model: Column, calculated column, table, filtered table, calendar table, measure and relationship. Data types and data format in the model.
- Good data organization practices.
- Computed columns: What they are, their creation and modification; Operators and their priorities in the DAX language; Hiding columns in the user view.
- DAX language functions: Text, Number, Time, Logical, Conditional, Convert, Array and Filter functions.
- Relationships in the data model: Active and inactive relations and their use. Filtering directions. Joining tables in the absence of relationships.
- Computing tables: Filtering a table and its context in a query. FILTER/ALL/ALLEXCEPT functions.
- Measures in DAX: What measures are and for what purpose they are created; Measure vs. computed column in the context of query execution. How the CALCULATE function works. Aggregate, count and statistical functions in measures.
- Context in the DAX language: Context at the level of the row, the query and the filter used.
- Hierarchies in analysis: Hierarchies generated automatically and defined manually
- Time Intelligence in practice: Calendar tables in DAX; Time Intelligence functions in DAX - operations on time
3. Report based on data stored on SQL server
Purpose: Introduction to working with SQL database server. Saves significant time associated with generating, processing and importing file data.
- SQL Server modes of operation: Data Import vs. Direct Query. Capabilities and limitations.
- Importing SQL Server objects that can be used in model building: Tables, Views, Procedures that return data (overview), Table functions.
- Working with queries in SQL. Classic data retrieval - SELECT: syntax of the command and order of execution of SQL statements. Fast low-code query development - almost without writing SQL (Query Designer). SQL standard in the Power BI data model;
- Optimizing the use of SQL: how to retrieve only the data we need. SQL language functions in queries; Operations on joined tables - SQL joins; Combining query results. Aggregation of data on the SQL server side.
- Parameterization and SQL: Query modification with M language parameter; M language parameter of SQL; Controlling parameter values from Power BI; Integration of Power BI fragmenter with M language parameter.
- Dynamic SQL creation and submission to the server.
4. Power BI Online
Purpose: Create visualizations to share online.
- Online Report: Publish existing reports from Power BI Desktop. Create new reports from published datasets. Exporting a report to PDF, Excel, PowerPoint and embedding it in Power Point. Sharing reports with colleagues and publishing in public mode. Refreshing data by re-publishing.
- Organization and user areas: Workspace and dashboard and their key elements.
- Managing access to the workspace. Creating and managing a dashboard. The components of a dashboard its capabilities and limitations.
- Data sets and data repositories: An existing data set in a new report. Downloading a data source as a PBIX file. Power BI Online's query and object dependency view.
- Dataverse and Power Query Online: Dataverse as a component of Power Platform to help master data. How and where to use it. Creating and managing a data source. Data source synchronization scheduling and data source access control.
- RLS (Row Level Security). A table with permission control and connecting it to the model. Access rules and adding users to them.
- Data Gateway: (Data Gateway) installation and configuration. Adding new sources to the gateway. Connection management and data update schedule. Security and access control.
- Report subscriptions: Creating a subscription and managing its recipients. Scheduling the sending of notifications
- Power BI integration: Creating and publishing applications in Power BI Online. Downloading ready-made applications. Publishing applications to SharePoint and websites and Teams service. Dedicated report view for mobile devices. Permissions management.
5. Summary: A-Z project + R and Python language scripts.
Purpose: building an analytics system with publishing and sharing.
- Summary exercises
Purpose: (optional) Visualization and data processing of Python language.
- Python language applications: running directly in Power BI Desktop to import data directly into the model. Create and share reports in the Power BI service.
- Prerequisites: Python language environment in Power Query. Software libraries: Pandas and NumPy.
- Working with the Python language: enabling scripting support. Importing and refreshing data using scripting.
- Creating visualizations: Creating a dot plot - examining correlations. Line chart with multiple data series. Bar chart in data presentation.
- Limitations and data security in Python language.
Purpose: (Optional) R language visualization and data processing.
- Requirements and limitations of R language packages. Installing R language and function libraries.
- Applications of the R language: Preparing data models. Report creation. Data cleaning, advanced data shaping and data set analysis, including filling in missing data, forecasting, clustering.
- Running R language scripts: Preparing and running a script to import and refresh data models.
- Working with the R language: Using the R language in the Power Query editor. Ready-made visualizations usable in Power BI. Creating visualizations based on R language script data.
Requirements
The target audience for the training is people involved in processing, analyzing and presenting large amounts of data, analysts, accountants, as well as software developers and testers.
PREWORK- PREPARATION FOR TRAINING:
Purpose: Overview of software useful for training.
- Creating a data model: Power BI Desktop - required
- Microsoft SQL Server Management Studio - optional
- DAX Studio - optional to work with DAX
- Visual Studio Code - optional to work with Power Query M, Python, R
- Microsoft R Open environment, Python
Purpose: Working with Power BI - stages of report creation and life.
- Preparation of data for Power BI Desktop in Power Query.
- Optimization and parameterization of data, also using SQL.
- Working with the DAX data model: relationships, tables, calculated columns, calculated tables and measures.
- Building a report in the Power BI Desktop application.
- Publishing and sharing a report in the Power BI service.
- Managing access control to the data model.
- Reusing a published data model.
- Updating a report online
35 Hours
Testimonials (9)
dużo praktyki w trakcie szkolenia
Katarzyna Grześkowiak - mTab Sp. z o.o.
Course - Power BI - wizualizacja danych
dyskusja na temat legalnosci umieszczania danych w chmurze i dobre praktyki przy modelowaniu i raportowaniu.
Course - Microsoft Power BI Data Analyst (szkolenie autoryzowane PL-300)
Constructive exchanges
Cécile - Transdev Compagnie Axonaise
Course - Power BI
Machine Translated
The Trainer is professional. He can make me feel this program not too hard to use as I thought before training.
Benjaporn - Thai Asahi Kasei Spandex Co.,Ltd
Course - Power BI for Business Analysts
Dpt ilmu baru
Yani - PT. Becton Dickinson Indonesia
Course - Power BI for Developers
The pace, activities and screen mirroring. Power Bi is new to me and I am looking forward to working with it more.
Michelle - Childhood Development Initiative CLG
Course - Creating Dashboards Using Microsoft Power BI
the personalized part during which I was able to work on my project and directly apply the elements of the training
Caroline - Schreder
Course - Power BI Dashboards
Machine Translated
Great pace during the training. very knowledgeable and teaching style
James - Gilead Sciences Ireland
Course - Excel to Power BI
What was very interesting in this training was the direct handling of the power bi tool. What was presented was very informative and provided a real answer to my problems, while trying to learn on the job. The trainer gave us keys to be better organized and structured in our files. Which is interesting if we have to give up our work. It would have taken a few more hours to produce our own report, that's the only downside.
Linedie - Transdev Nord
Course - Advanced Power BI
Machine Translated
