Clase 2 | ¿Qué es DAX? Y todo el poder dentro de Power BI 🚀

Introduction to Participants and Their Experiences

Carla Ríos: Overcoming Initial Doubts

• Carla Ríos, an accounts payable analyst at Micheline in Querétaro, shares her initial fears about using dashboards, thinking they were overly complicated.

• She expresses gratitude for the course's step-by-step guidance, which helped her create a dashboard she initially thought was beyond her capabilities.

Norman Ramírez: Professional Growth through Learning

• Norman Ramírez, a logistics manager, discusses how the course has significantly advanced his professional skills by modeling information on a national level.

• He praises the program for its comprehensibility and ease of learning, highlighting the excellent support provided throughout.

Feedback on Course Structure and Support

• The design and sequence of the program are commended as excellent; every video covers all necessary aspects without leaving gaps.

• The chat support is described as wonderful, providing encouragement and solutions when participants face challenges.

Personal Stories of Transformation

Pedro Jacinto Espinoza: Enhancing Presentation Skills

• Pedro Jacinto Espinoza, a public accountant aged 55, notes that directors are no longer accustomed to traditional financial statements.

• He appreciates Power BI for enabling more engaging presentations and acknowledges the instructor's professionalism despite their youth.

Lucio Eduardo Valderrama: A Comprehensive Learning Experience

• Lucio Eduardo Valderrama from Colombia reflects on his extensive experience with various programs but finds this course akin to attending university due to its structured methodology.

• He emphasizes that unlike simple tricks found online, this course offers deep pedagogical insights that enhance understanding.

Engagement in Live Classes

Class Interaction and Content Overview

• The host welcomes participants to the second class of "Power BI Imparable," encouraging interaction through chat confirmations regarding audio and visual clarity.

• Participants from various countries introduce themselves in the chat while expressing excitement about learning DAX (Data Analysis Expressions).

Class Objectives and Resources

• The session promises valuable insights into maximizing analysis capabilities using DAX functions.

• Attendees are reminded about downloadable materials available in comments to ensure they do not miss any important content during the class.

Introduction to the Class

Overview of Class Structure

• The instructor reassures students that it's okay to join the second class without having attended the first, emphasizing accessibility.

• A correction is made regarding event dates from a previous session, ensuring clarity for attendees.

• Key dates for upcoming classes are highlighted: February 10, 11, 12, and 15.

• Students are encouraged to set reminders and alarms for class times and join a WhatsApp group for updates.

Social Media Presence

• The instructor mentions their social media presence under "Hazlo con Excel," noting over 100,000 followers but acknowledges disorganization in content delivery.

• Emphasizes that this week focuses on Power BI with approximately eight hours of premium content spread across four classes.

Course Details and Offerings

Course Structure

• Classes will not be recorded indefinitely; access will only be available until February 15.

• Introduction of the comprehensive course "Triunfa en Power BI," which includes over 60 hours of content and various learning materials.

Tools and Resources

• Mention of tools like Rocket BI and Power Gama designed to enhance Power BI usage; these will be discussed later in detail.

Market Positioning of Power BI

Importance in Business Intelligence

• Discussion on Gartner's Magic Quadrant positioning highlights Microsoft’s strong market presence with Power BI as a leading analytics platform.

• Emphasis on how Microsoft's integration with Microsoft 365 enhances Power BI's capabilities compared to competitors.

Execution Capability

• The instructor explains that understanding market trends is crucial but execution capability is equally important for success in business intelligence.

Learning Objectives

Maximizing Learning Experience

• Students are urged to focus fully during classes to maximize their learning potential with Power BI.

Foundation in AI Integration

• A critical rule regarding artificial intelligence (AI): solid foundational knowledge in Power BI is essential before leveraging AI effectively.

Introduction to Power BI and AI Integration

Building a Strong Foundation in Power BI

• The goal is to create a positive dependency on artificial intelligence while enhancing results through tools like Rocket B, Chat GPT, Gemini, or Cloud.

Class Preparation Tips

• To maximize learning, significantly reduce distractions during the class for better focus and engagement.

• Participants are encouraged to download materials shared during the session for effective learning.

Streaming Quality Recommendations

• Ensure optimal viewing quality on YouTube by adjusting settings to the highest resolution available for a clearer experience.

Class Structure and Learning Objectives

Today's Focus: DAX and Advanced Calculations

• The current session will cover DAX (Data Analysis Expressions), focusing on advanced calculations essential for creating impactful reports.

Upcoming Classes Overview

• Tomorrow's class will involve creating a financial report with advanced design techniques and new data segmentation features.

• The final class will introduce data models that enhance analytical capabilities in Power BI.

Engagement and Certification Process

Participation Certification Requirements

• Attendees must collect four key phrases mentioned throughout the classes to qualify for participation certification.

Recommended Practices During Classes

• It is advised to have the guide open during sessions but wait until recorded repetitions to practice hands-on tasks effectively.

Community Engagement Encouragement

Support Through Interaction

• Viewers are encouraged to like the stream and subscribe to the channel, which helps spread awareness of Power BI methodologies.

Commitment Check from Participants

Engaging with Learners' Commitment

• A call-to-action was made for participants to express their commitment by commenting with "1," indicating their dedication to attending all classes.

Introduction to Power BI Class

Overview of the Class Structure

• The class begins with enthusiasm and a commitment to learning, emphasizing the importance of engagement in mastering Power BI.

• Introduction to Guide Number Two for the "Unstoppable Power BI" week, highlighting that prior attendance is not necessary for participation.

• Explanation of the guide's structure, which is an Excel file containing clickable links to various topics covered in the class.

Navigation and Resources

• Each topic within the guide has clickable links that direct participants to specific sections for detailed discussions.

• A reminder about joining a WhatsApp group for event updates and community interaction, with links provided in both the guide and video description.

Typical Workflow in Power BI

Importance of Repetition

• The instructor emphasizes moving quickly through familiar topics while acknowledging that repetition enhances understanding and retention.

Steps in Data Analysis

• The workflow starts with defining an analysis objective; clarity on what needs improvement or optimization is crucial.

• Examples of objectives include sales analysis, marketing performance, HR management, customer satisfaction, and inventory management.

Data Handling Process

Data Sources

• Identifying data sources is essential; these can range from databases (ERP/CRM systems), cloud storage, SQL databases, or even Excel files.

Importing Data into Power BI

• Data must be imported into Power BI using Power Query regardless of whether transformation is needed. This tool acts as a bridge between data sources and Power BI.

Data Transformation and Analysis

Exploratory Data Analysis (EDA)

• EDA involves reviewing data structures to ensure they meet analytical requirements before proceeding with analysis.

Cleaning and Structuring Data

• If data requires transformation or cleaning, it will be processed through Power Query before being loaded into Power BI. If no changes are needed, it can be directly loaded.

Modeling Data Efficiently

Assessing Efficiency

• After loading data into Power BI, assessing whether the data structure is efficient determines if further modeling is required. Inefficient structures may necessitate normalization processes explained in future classes.

Understanding Data Models and DAX in Power BI

Importance of Data Models

• The speaker emphasizes the need for a data model to efficiently scale analysis, especially when dealing with millions of rows of information.

• Once the data model is established, DAX (Data Analysis Expressions) will be utilized to create formulas and perform analyses.

Creating Measures and Dashboards

• The session will cover creating measures to achieve specific objectives and constructing a dashboard with selected visual objects.

• Repetition is highlighted as a key factor in understanding; the speaker encourages feedback on clarity throughout the session.

Transitioning to Dashboard Construction

• Before moving on to dashboard construction, the speaker stresses discussing critical concepts that are foundational for effective reporting.

• Participants are encouraged to provide feedback on their understanding as they progress through the material.

Implicit vs. Explicit Measures in Power BI

Understanding Implicit Measures

• The difference between implicit and explicit measures is introduced, with an emphasis on their significance in Power BI.

• An implicit measure is automatically created by Power BI when fields like billing are dragged into visuals, resulting in operations such as summation without user input.

Advantages and Disadvantages of Implicit Measures

• Advantages include ease of use since no DAX knowledge is required; they allow quick calculations like totals or averages.

• However, implicit measures lack flexibility for complex customizations beyond basic operations.

Exploring Explicit Measures

• Explicit measures require users to write DAX formulas themselves, allowing for more complex calculations tailored to specific analytical needs.

• They offer greater flexibility and control over calculation logic but necessitate a solid understanding of DAX, which can be challenging for beginners.

Practical Application: Building a Productivity Dashboard

Objective of the Dashboard

• The goal is to create a productivity dashboard that evaluates how effectively resources like time and operator effort are used to produce quality products.

Visual Elements and Organization

• A table structure will be used within the dashboard, indicating current status indicators (e.g., yellow marking pending tasks), facilitating clear tracking of progress.

Power BI Setup and Data Import Process

Initial Configuration in Power BI

• The session begins with a focus on changing the status to "completed" indicated by a green color, highlighting the importance of visual indicators in Power BI.

• A reminder is given for those who missed the previous class about enabling interaction within objects, which enhances Power BI's functionality.

• Instructions are provided on how to activate object interaction through the file menu, emphasizing its role in improving user experience and professional output.

Opening a New Document

• Participants are instructed to open a completely new blank document in Power BI as they prepare to import data.

• Various methods for importing data into Power BI are discussed, including direct imports from Excel or using the "Get Data" option.

Importing Data from Excel

• The speaker indicates that an Excel workbook containing productivity data will be used for analysis, guiding participants on how to access it.

• Emphasis is placed on selecting the correct workbook named "productividad" for importing relevant data into Power BI.

Navigating and Selecting Data

• Once the Excel workbook is opened, users can navigate through its sheets; currently, only one sheet named "producción" contains information.

• To load data into Power BI, users must check the box next to the desired sheet. This action enables options for loading or transforming data.

Loading Data into Power BI

• The speaker decides not to clean or transform any data at this stage since it's assumed that prior analysis has ensured proper structure.

• Users are guided to click on the green load button to import their selected data directly into Power BI without modifications.

Reviewing Imported Data

• After loading, confirmation of successful import is noted by checking if the production table appears in the data panel.

• Users can switch views between report and table formats within Power BI to visually inspect their imported datasets.

Transforming Data (Optional)

• If needed, users can access Power Query at any time via the home tab for further transformations or adjustments of their datasets.

Creating Indicators in Power BI

Introduction to the Obstacles Column

• The discussion begins with an introduction to the "obstacles" column, indicating a focus on creating indicators within Power BI.

Creating the First Indicator

• The speaker outlines plans to create the first indicator, specifically focusing on "good pieces produced," which will be stored in a card format. This involves summing up the relevant data from a specific column.

Summing Produced and Rejected Pieces

• In table view, there are two columns: one for produced pieces and another for rejected ones. The goal is to sum only the good pieces produced.

Measures in Power BI

• The speaker explains how to sum columns using either implicit or explicit measures (DAX). They plan to demonstrate both methods during the session.

Using Visual Objects in Reports

• Instructions are provided on how to insert visual objects like cards into reports, highlighting potential issues with visibility of certain objects due to software updates. A workaround is suggested by restoring default visual objects.

Displaying Results with Cards

Selecting Card Types

• The speaker discusses selecting between different types of cards for displaying results, emphasizing that both standard and lightning symbol cards can be effective for showing sums of produced pieces.

Filling Data into Cards

• Demonstrates how to fill data into a card by checking off relevant fields from the data panel, resulting in an automatic display of summed values as an implicit measure created by Power BI itself.

Formatting and Customizing Measures

Adjusting Display Settings

• Instructions are given on adjusting display settings for numbers shown in cards, such as rounding options and ensuring complete unit visibility instead of automatic formatting choices made by Power BI.

Importance of DAX Measures

• Emphasizes that while implicit measures are useful, they lack flexibility needed for advanced calculations; thus, creating explicit DAX measures is essential for more complex analysis tasks ahead.

Creating Explicit DAX Measures

Methods for Writing DAX

• Various methods are discussed for initiating DAX measure creation within Power BI: through calculation sections or directly from table headers via right-click options—each method leading to similar outcomes but offering user preference flexibility.

How to Create DAX Measures in Power BI

Introduction to DAX Measures

• The process begins by right-clicking on the "Producción" table and selecting "Nueva medida," which activates the formula bar for writing DAX.

• A DAX measure starts with a name, followed by an equal sign, after which functions are written to return results.

Creating Your First Measure

• The first measure is named "Total piezas buenas producidas," chosen for clarity despite its length.

• After naming, the SUM function is introduced; it's important to note that all DAX language elements are in English.

Using the SUM Function

• To use the SUM function, parentheses are opened, prompting for a column name as an argument.

• The user can navigate through available columns in the "Producción" table; column names must be numeric for summation.

Writing Column References

• In DAX syntax, table names are enclosed in single quotes and column names in brackets. However, users can simply type "piezas producidas" and select it directly.

• Closing parentheses completes the SUM function. Pressing enter stores this measure within the production table indicated by a calculator symbol.

Understanding Flexibility of Measures

• Users confirm understanding via chat; comparisons to Excel highlight similarities in function usage.

• This measure is termed a calculated field or measure and offers flexibility not found in implicit measures created automatically by Power BI.

Building Reports with Measures

• A new card visual is added to display data from the created measure. Adjustments are made via formatting options for better visibility of numbers.

• While Power BI can perform automatic sums, manually creating measures allows for more complex calculations and reusability across reports.

Finalizing Report Setup

• Implicit measures are removed as users prepare to build their report layout effectively.

• Users begin adding background images relevant to their report sections using downloadable materials provided earlier.

Understanding Canvas and Formatting Options in Power BI

Introduction to the Canvas

• The current canvas is emphasized as a blank slate, crucial for formatting options. The focus should be on the canvas rather than any visual objects like cards.

• When selecting the blank canvas, different contextual options become available compared to when selecting other elements like cards.

Contextual Panels

• The format panel is contextual; it changes based on what is selected. Selecting the canvas provides options specific to configuring it.

• Users can add images or colors as backgrounds for the canvas, enhancing its visual appeal.

Image Transparency Settings

• Initially, images may appear invisible due to default transparency settings at 100%. This requires adjustment for visibility.

• Reducing transparency from 100% to 0% allows users to see their background image clearly.

Feedback and Engagement

• The presenter checks in with participants for understanding and engagement, emphasizing clarity in explanations about adding background images.

Card Configuration

• Adjustments are made to card settings by removing backgrounds and modifying size parameters for better visibility of data points.

Creating Measures in DAX

Setting Up New Measures

• Instructions are provided on how to create a new measure within Power BI using DAX, focusing on naming conventions and formula structure.

Writing DAX Functions

• Emphasis is placed on proper syntax when writing DAX functions, including line breaks that do not disrupt formula functionality.

Practical Example of Measure Creation

• A practical example illustrates creating a measure named "total piezas rechazadas," demonstrating how to navigate through columns using keyboard shortcuts.

This structured approach captures key insights from the transcript while providing timestamps for easy reference.

Creating and Managing Measures in Power BI

Setting Up the Card Format

• The speaker demonstrates how to copy an existing card format for a new card, emphasizing the ease of modifying measures directly from the visual object or through the compile panel.

Adding New Measures

• A new measure titled "Total Pieces Rejected" is created by removing unnecessary elements from the previous measure, showcasing a straightforward approach to updating visuals.

Utilizing Existing Measures

• The speaker explains how to sum previously created measures (good pieces produced and rejected pieces), highlighting that explicit measures allow for further calculations unlike implicit ones.

Creating Total Production Measure

• A new measure called "Total Production" is introduced. The process involves using brackets to list existing measures, demonstrating how to combine them effectively with a simple addition formula.

Advantages of Explicit Measures

• The flexibility of writing explicit DAX measures is discussed, allowing users to create reusable variables that can be utilized across different calculations within Power BI.

Understanding DAX Functions: Calculate

Introduction to Calculate Function

• The speaker introduces one of DAX's most powerful functions, "Calculate," which will be explained in detail as it relates to productive hours in a production context.

Contextualizing Productive Hours

• Before diving into function creation, the speaker provides context on what constitutes productive hours and how they are derived from total worked hours minus obstacles encountered during production.

Analyzing Obstacles Impacting Productivity

• Different types of obstacles affecting productivity are outlined, such as equipment failure or quality control issues. This analysis helps clarify which hours should be considered non-productive based on specific criteria.

Understanding Productive Hours Calculation

Defining Productive Hours

• The speaker explains that productive hours are defined as those without obstacles, indicating that when the obstacle field is empty, all hours can be considered productive.

Counting Productive Hours

• To count productive hours, one must only consider instances where the obstacle column is empty. This sets the context for calculating total productive hours.

Using the Calculate Function

• The speaker introduces the calculate function as a tool to achieve this calculation and emphasizes its importance in understanding how to manipulate data effectively.

Creating a New Measure

• A new measure named "horas productivas" (productive hours) will be created in the production table to facilitate this calculation.

Understanding Calculate Function's Purpose

• The calculate function evaluates an expression within a modified context by applying filters. The speaker reassures that they will clarify how to use it step-by-step.

Structure of Calculate Function

• The calculate function requires two arguments: an expression and a filter. In this case, the expression involves summing up total hours while ensuring that obstacles are accounted for correctly.

Summing Total Hours with Conditions

• The operation aims to sum total hours but only when there are no obstacles present. This condition is crucial for determining which hours qualify as productive.

Applying Filters Correctly

• To apply the filter, the speaker indicates that they will check if the obstacle column is blank using specific syntax within DAX (Data Analysis Expressions).

Finalizing Calculate Function Syntax

• After constructing the necessary components of the calculate function, it’s highlighted that clarity in syntax is essential for proper execution of calculations.

Reassessing Understanding of Calculate Function

• The speaker checks audience comprehension regarding how to utilize the calculate function effectively and encourages feedback on their understanding through comments.

Practical Application of Created Measure

• Once created, "horas productivas" can be added to visualizations or dashboards. Adjustments such as setting decimal places are also discussed for better presentation of results.

Creating Visual Objects in Data Analysis

Importance of Practice in Function Creation

• Emphasizes the necessity of practicing function creation to fully grasp its power and application.

• Mentions the upcoming task of creating a visual object to compare productive and non-productive hours by location using a grouped bar chart.

Developing Non-Productive Hours Measure

• Discusses the need to sum total hours while considering obstacles, indicating that empty fields signify productive hours.

• Highlights the importance of filtering out empty fields when calculating non-productive hours due to existing obstacles.

Using DAX Functions for Calculation

• Introduces the DAX function for creating a new measure called "non-productive hours," explaining how to format it correctly with line breaks.

• Describes how to use the CALCULATE function, specifying an expression (sum of total hours) and a filter (obstacles must have content).

Filtering Logic Explained

• Clarifies that only total hours where obstacles exist should be summed, as these indicate non-productivity.

• Explains how to set conditions within the CALCULATE function, ensuring that only relevant data is included in calculations.

Finalizing Measures and Creating Visualizations

• Requests feedback from participants on clarity regarding the calculation process before finalizing settings like thousand separators and decimal points.

• Suggests flexibility in filtering specific types of obstacles by adjusting text criteria within quotes.

Constructing Grouped Bar Chart Visualization

• Guides through inserting a grouped bar chart for visual comparison between productive and non-productive hours across locations.

• Details steps for positioning elements within Power BI, emphasizing aesthetic considerations versus flexibility in design choices.

Formatting and Analyzing Productivity Data

Adjusting Data Fields

• The speaker discusses the addition of a new data field, ensuring it aligns with existing formats.

• Emphasizes the importance of differentiating between productive and non-productive hours, particularly when the "obstacles" column is not empty.

Visualizing Productivity Metrics

• After adjustments, the speaker highlights the need for formatting to enhance visual clarity in productivity metrics.

• Acknowledges audience interaction and feedback while adjusting background settings in the visualization tool.

Customizing Graph Elements

• The title color is changed to white for better visibility; customization options are briefly mentioned.

• Discusses modifications to axis titles and values, opting for a clean look by removing unnecessary titles.

Analyzing Country-Specific Data

• Observations on productivity levels across countries, noting Japan's high productivity and France's unusual balance of productive versus non-productive hours.

• Suggestion to analyze France’s data further due to potential inefficiencies indicated by their numbers.

Finalizing Graph Appearance

• The speaker selects specific colors for different data series (productive vs. non-productive hours), enhancing visual distinction using hexadecimal codes.

• Activates data labels within the graph, ensuring they appear in a specified position for clarity.

Engaging with Audience Feedback

• Invites audience participation by asking for feedback on progress and results observed so far in the session.

Modifying Measures and Filters

• Explains how to modify measures directly within the tool, including adding multiple filters for more complex analyses.

Color Codes Reference

• Provides information about where to find color codes used in reports, emphasizing accessibility through downloadable materials.

Transitioning to Area Graph Visualization

• Introduces plans to visualize production metrics over time using an area graph format as part of ongoing analysis efforts.

Creating an Area Chart in Power BI

Setting Up the Chart

• The area chart requires information on both the X and Y axes. The X-axis will represent months, while the Y-axis will show the number of correctly produced pieces.

• For the Y-axis, a measure named "Total, piezas buenas producidas" is selected to visualize production data effectively.

Formatting Adjustments

• Important formatting changes are made: removing background images, changing titles to white for better visibility, and adjusting axis titles.

• Data labels are added with white text color for clarity. The color of lines in the chart is also modified to orange for consistency.

Identifying Issues in Visualization

• Viewers are prompted to identify any issues with the chart's appearance. Feedback is encouraged through chat interaction.

• A transparency issue is noted where lower production values appear misleading due to automatic scaling starting at 150,000 instead of zero.

Correcting Scale Errors

• It’s explained that comparing production values visually can be deceptive if the minimum range starts too high; this leads to misinterpretation of data.

• By setting the minimum value on the Y-axis to zero, a clearer visual representation emerges that accurately reflects differences between low and high production points.

Finalizing Visual Elements

• Additional adjustments include removing unnecessary units from axis labels for improved clarity and ensuring accurate visual comparisons between production levels.

• Markers are added to enhance point differentiation on line charts. Smoothing options are discussed for aesthetic improvements in line transitions.

Creating a Quality Percentage Measure

Introduction of New Measures

• Discussion shifts towards creating a combined graph similar to Excel functionalities within Power BI, specifically focusing on quality percentage visualization using gauges or speedometers.

Calculation Methodology

• A new measure called "porcentaje de calidad" is introduced by dividing correctly produced pieces by total production figures. This calculation aims to derive an accurate quality percentage metric.

Creating a Quality Percentage Meter

Setting Up the Quality Percentage

• The speaker discusses configuring a quality percentage metric, emphasizing that it will be displayed without decimal points for clarity.

• A visual representation of this metric is introduced, specifically using a gauge or meter format to illustrate the quality percentage effectively.

• The speaker details how to input the quality percentage into the meter's value settings and mentions potential configurations like maximum value and target value, although these are not set at this moment.

Formatting the Meter

• Adjustments are made in the formatting panel, including removing background colors and setting title colors to white for better visibility.

• The data label color is also set to white, ensuring all text elements are legible against the chosen background.

Communicating Quality Metrics

• The current quality percentage is reported as 99%, indicating high production quality compared to rejected items.

• The speaker invites feedback on understanding the creation of this quality measure and its application within their project.

Transitioning to Productivity Measurement

Copying Existing Configurations

• To maintain consistency, the speaker copies the existing meter configuration for productivity measurement purposes.

Defining Productivity Percentage

• A new measure called "percentage of productivity" is created by dividing productive hours by total hours worked. This calculation aims to provide insight into overall efficiency.

Finalizing Productivity Display

• After defining productivity metrics, adjustments are made to ensure that this new measure replaces the previous quality measure in the same visual object.

Adding Data Segmentation Features

Implementing Operator Segmentation

• The next step involves adding a data segmentation feature based on operators, which allows users to filter reports according to specific operators' performance.

Customizing Data Segment Appearance

• The speaker explains how they will use standard data segmentation tools while hinting at more advanced features available in future sessions.

Enhancing User Interface Design

• Formatting changes include adjusting background colors and styles for improved aesthetics and usability of data segments within reports.

This structured approach provides an organized overview of key concepts discussed in creating visual metrics related to quality and productivity while enhancing user interaction through segmentation features.

Productivity Report Creation and Key Insights

Visual Enhancements in Reporting

• The speaker discusses activating visual borders and rounded corners to enhance the attractiveness of the report, indicating a focus on aesthetics in data presentation.

• Final adjustments are made by copying existing designs for data segmentation, showcasing efficiency in report creation through design reuse.

Learning Outcomes from the Session

• The session concludes with a summary of productivity reporting steps, emphasizing learning DAX (Data Analysis Expressions) and gaining valuable insights for achieving impressive results.

• The instructor expresses enthusiasm about class participation and announces the second keyword of the day, reinforcing engagement among participants.

Engagement and Attendance Confirmation

• A QR code is presented alongside a call to action for attendees to confirm their attendance by commenting with the keyword "datos" (data), highlighting interactive elements used during the session.

• Instructions are provided for filling out an attendance form via QR code or shared link, ensuring that all participants can easily confirm their presence.

Importance of Keywords for Certification

• Participants are reminded to note down keywords throughout the event as they will be necessary for downloading certificates at its conclusion.

• The instructor emphasizes that tomorrow's class will involve creating a financial report, encouraging students to prepare for advanced topics like interactive data segmentation.

Feedback and Future Classes

• Attendees are encouraged to provide feedback on social media regarding their experience in today's class, fostering community interaction and continuous improvement.

• Tomorrow’s session promises advanced features such as waterfall charts and custom visuals, enticing participants with upcoming learning opportunities.

Overview of Upcoming Events and Social Media Presence

Social Media Engagement

• The speaker mentions that they do not use Instagram but encourages the audience to find them on Facebook and other social media platforms under the name "Aldo con Excel." They highlight that the brand is in transition, indicating a broader focus beyond just teaching Excel.

Class Reminders and Key Information

• The speaker urges participants to continue sharing links related to the event, including a form and today's keyword. They emphasize the importance of activating reminders for upcoming classes.

Anticipation for Future Classes

• A strong emphasis is placed on attending class number three, which promises to be an incredible learning experience focused on creating high-level reports using Power BI. The speaker expresses enthusiasm for participant engagement and energy during this session.