Power BI

Power BI is the industry-leading business intelligence platform, and one of the most popular data analysis and visualization tools in the world. As demand for data and business analysts grows, so does the need for data analysis skills across a variety of job functions.

Data Analysis Expressions (DAX) is a library of functions and operators that can be combined to build formulas and expressions in Power BI, Analysis Services, and Power Pivot in Excel data models.

Microsoft Power BI Desktop is free built for the analyst. It combines state-of-the-art interactive visualizations, with industry-leading data query and modeling built-in. Create and publish your reports to Power BI. Power BI Desktop helps you empower others with timely critical insights, anytime, anywhere. More about Power BI Desktop

I refresh my Power BI skills at Datacamp. Datacamp has Power BI courses, tutorials at all levels, projects, and a workspace to ensure my progression on my Power BI journey.

I am a Microsoft Certified: Power BI Data Analyst Associate, that confirm my knowledge of Power Query and writing expressions by using Data Analysis Expressions (DAX).

As a Data Analyst in Power BI I can:

  • development of business solutions built in Power BI,
  • integrating, cleaning, transforming, maintaining and analyzing data.
Analytics

My Power BI Learning Path

With Data Camp and Microsoft Learn I build my skills and experience and validate my knowledge:

Power BI Fundamentals (Datacamp) 17 hours (skill track ⇒ certificate)

In this course I learned how to use Power BI – one of the world’s most popular business intelligence tools. It can be used by everyone to quickly clean, analyze, and visualize my data. I learned how to organize and analyze data, create presentation-ready visualizations, and build insightful dashboards and reports. I also learned how to prepare data in Power Query before getting introduced to the topic of Data Modeling.

In this course, I learned how to use this popular business intelligence platform through hands-on exercises. Before diving into creating visualizations using Power BI’s drag-and-drop functionality, I first learned how to confidently load and transform data using Power Query and the importance of data models. I also learned to drill down into reports and make your reports fully interactive.

I learned fundamental concepts and best practices for implementing DAX in my reports. I learned to write DAX code to generate calculated columns, measures, and tables while learning supporting knowledge around ‘context’ in Power BI. Finally, I rounded off the course by introducing time-intelligence functions and show me how to use Quick Measures to create complex DAX code.

In this Power BI course, I learned to create insightful visualizations through built-in and customized charts and conditional formatting. I discovered how to create a plethora of visualizations such as scatter plots, tornado charts, gauges, and how to visualize everything without overwhelming my audience.

In this Power BI case study, I learned investigate a dataset from an example telecom company called Databel and analyze their churn rates. Analyzing churn doesn’t just mean knowing what the churn rate is: it’s also about figuring out why customers are churning at the rate they are, and how to reduce churn. I answered these questions by creating measures and calculated columns, while simultaneously creating eye-catching report pages.

In this interactive Power BI course, I learned how to use Power Query Editor to transform and shape your data to be ready for analysis. I also learned how to grips with various text and numerical transformations, including multiplication, rounding, and split and merge text columns, to help me become even more efficient in data preparation.

In this course I learned how to explore a toolbox of data cleaning, shaping, and loading techniques, which I can apply to my data. I also learned how to choose between Power Query and Power BI, and discovered the foundations of data modeling by going into star and snowflake schemas.

Data Analyst in Power BI (Datacamp) 51 hours (skill track ⇒ certificate)

In this course I learned how to import, clean, manipulate, and visualize data in Power BI—all critical skills for any aspiring data professional. Through hands-on exercises, I learned data analysis best practices and discover a world of Power BI functionalities, including data modeling, DAX, Power Query, and many others.

In this course, I learned how to use this popular business intelligence platform through hands-on exercises. Before diving into creating visualizations using Power BI’s drag-and-drop functionality, I first learned how to confidently load and transform data using Power Query and the importance of data models. I also learned to drill down into reports and make your reports fully interactive.

I learned fundamental concepts and best practices for implementing DAX in my reports. I learned to write DAX code to generate calculated columns, measures, and tables while learning supporting knowledge around ‘context’ in Power BI. Finally, I rounded off the course by introducing time-intelligence functions and show me how to use Quick Measures to create complex DAX code.

In this Power BI course, I learned to create insightful visualizations through built-in and customized charts and conditional formatting. I discovered how to create a plethora of visualizations such as scatter plots, tornado charts, gauges, and how to visualize everything without overwhelming my audience.

In this Power BI case study, I learned investigate a dataset from an example telecom company called Databel and analyze their churn rates. Analyzing churn doesn’t just mean knowing what the churn rate is: it’s also about figuring out why customers are churning at the rate they are, and how to reduce churn. I answered these questions by creating measures and calculated columns, while simultaneously creating eye-catching report pages.

In this interactive Power BI course, I learned how to use Power Query Editor to transform and shape your data to be ready for analysis. I also learned how to grips with various text and numerical transformations, including multiplication, rounding, and split and merge text columns, to help me become even more efficient in data preparation.

In this course, I learned all about table transformations in Power BI. I learned how to (un)pivot, transpose, and append tables. I also got introduced to joins and discover when it makes sense to use them. Finally, I gained power with custom columns, including how to use M language and the Advanced Editor, to help me be even more efficient in data preparation.

In this course I learned how to explore a toolbox of data cleaning, shaping, and loading techniques, which I can apply to my data. I also learned how to choose between Power Query and Power BI, and discovered the foundations of data modeling by going into star and snowflake schemas.

In this course, I extended my knowledge about facts, dimensions, and their relationships. I learned about the cardinality of relationships and how I can use bi-directional cross-filtering in my model. I also explored the use of quick measures and hierarchies and write DAX to fully customize my data model. Finally, I got introduced to Power BI reporting best practices to improve the performance of my reports.

In this Power BI case study, I learned exploring a dataset for a fictitious software company called Atlas Labs. This course focused on helping me import, analyze and visualize Human Resources data in Power BI. I learned how to effectively work with Power BI using example data. I carried out exploratory data analysis and used DAX to help build powerful visualizations. I finished my analysis by diving deeper into attrition and what factors impact attrition.

DAX, or Data Analysis eXpressions, is a formula language used in Microsoft Power BI to create calculated columns, measures, and custom tables. Once mastered, DAX gave me powerful control over visuals and reports, allowing for better performance and more flexibility. This course covered the core concepts such as row query and filter context, with exercises focusing on filtering, counting, ranking, and iterating functions.

This course introduced me to new DAX functions and its many use cases. First of all, I expanded my core DAX knowledge by learning how to write logical functions. Secondly, I discovered how I can write DAX functions for row-level security (RLS) purposes and how to use DAX to manipulate tables and create nested functions.

In this course I learned how to design with users in mind. I also learned to use R and Python to create unique visualizations, adding custom chart types that would otherwise not be available in Power BI. I got introduced to some best practices in data visualization and optimize my visualizations to be more accessible to visually impaired individuals.

I started by using descriptive statistics to spot outliers, identify missing data, and apply imputation techniques to fill gaps in your dataset. I also learned how EDA in Power BI can help me discover the relationships between variables—both categorical and continuous— by using basic statistical measures and box and scatter plots.

In this course, I learned how to analyze time series, visualize your data, and spot trends. I also built new date variables, discover run charts, and get into calculating rolling averages. Finally, found out how to identify which variables exhibit the most influence on the target variable using Power BI’s decomposition trees and key influencers.

In this advanced course, I learned complex alternative data storytelling techniques to simply building dashboards, including buttons and bookmarks to create more interactive visualizations. Customized the user experience by drillthrough filters and emoji, and learned how to tweak the Q&A feature for personalized reports.

I learned practical techniques for incorporating DAX measures and calculations in my reports—empowering users to filter, highlight values, and group data effectively. Through hands-on exercises, I also learned how to progressive disclosure, a user experience (UX) technique to make reporting easier before discovering how to change report themes and optimize them for mobile users.

In this course, I learned the differences between Power BI Desktop and Power BI Service when it comes to data connections. I also learned about the different ways that Power BI stores data when connecting to a source, and how to able to amend connections after they have been made. Finally, I learned how to use parameters and M Language in Power BI Desktop to level up my handling of data import processes.

I learned how to securely report access by managing access to datasets, implementing row-level security, or applying sensitivity labels to prevent unauthorized data re-use or exfiltration. I also discovered how to promote and certify content in Power BI before learning how to save time by subscribing to reports and setting up data alerts—making it easy to keep on top of changes to data in my reports.

My badges:

Power BI Data Analyst Associate
data analyst

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