You want to analyze data from single or multiple sources? You want to create your individual dataset based on these sources and transform your results in beautiful and easy-to-make visualizations? Moreover, you want to share your results with your colleagues or collaborate on your project? Finally, you want to be able to access your data from multiple devices?
Then the Power BI tools are the tools to choose for you!
In this course you will learn why Power BI offers you a comprehensive set of Business Intelligence tools for your data analysis goals and how to use these tools to fulfill all of the above tasks - and more. Imagine to quickly structure your data, to easily add calculations to it and to create and publish nice-looking charts in just a few minutes.
This is what you will learn:
This is what this course offers, but is this the right course for you?
...then this is the right course for you.
I would be really happy to welcome you in this course!
Let's get started by introducing you to the content of this course.
Before we dive deeper it's time to answer one important question first: What is Power BI actually and what tools will we use?
Time to start our first project. Let's install Power BI Desktop and connect it to our source data.
We connected Power BI Desktop to our data, let's work on the data in the Query Editor now.
Let's finish our first project by loading the data into the data model and by creating a beautiful visualization.
After finishing our first project, let's take a more detailed look at what this course offers you.
You will find files attached to various videos to work on the projects. Let's make sure that you are comfortable in using those files.
The Locale is an important option related to the formatting of dates and numbers in Power BI Desktop. This article ensures that the Locale is set correctly to ensure that you get the most out of this course.
Before we start learning more about the functions of Power BI Desktop, we should take a look at the general workflow of this tool.
In the data model, we have different views. Let's understand why we have those views and what we can do in them.
After understanding the interface of the data model, we will now take a look at what the Query Editor is and understand why we need it in Power BI Desktop.
Before we start our course project, let's make sure that the options in your and my Power BI Desktop project are set equally.
Let's take a look at the workflow of Power BI Desktop again and understand why we should start our project in the Query Editor.
Every project starts with source data: Let's learn how we can connect Power BI Desktop to our data sources.
We added our queries, now it's time to take a look at the data and see what adjustments we have to make. First, we will work on the rows.
After cleaning the rows, we now want to combine our queries. Let's learn why the append function can be a big help to structure our data.
The rows are fine already, but what about the columns? Time to take a closer look at those right now.
We have wrong data in our queries: Let's learn how we can work on this problem using the replace function.
We finished our initial data cleaning. Now let's take a look at the Query Editor's formatting options.
Let's dive deeper into the structure of our queries and learn how to use the pivot and unpivot functions.
Time to learn how to split columns depending on a specific delimiter to get rid of information not required in our data.
We need more structure as we have created multiple queries in our project. Let's organize our project by creating groups.
We made a great progress so far, time to add some more structure to the data in our Query Editor and to start shaping our data.
Let's learn about the basic concepts of a star schema and how we can apply this schema to our course project.
We need to copy our combined query. Time to learn the differences between a query duplicate and a query reference.
Let's learn how to create our first dimension table to save our geographical information in it.
We need additional data: Time to learn how to add information without external sources by entering data manually in the Query Editor.
Let's learn why merging queries helps us to structure our data and how we can use that function in our project.
We added a lot of information to our DIM region table so far. Time to finish it by merging it with another query.
We need another dimension table. Let's learn why how we can create the DIM agegroup table.
Our different age groups must be identified. Let's make sure that this works correctly by creating an index column.
Time to learn how to duplicate a column and how to apply the extract function to this newly created column.
We want to define categories for our different age groups. Let's learn how to do this by creating a conditional column.
Time for the last steps in the Query Editor. Let's finish our star schema by adding the FACT population table.
As a last step, let's learn how to apply mathematical operations to specific columns by using the multiply function.
Before we finally load the prepared data into our data model, it's time to take a look at the performance and to understand how we can improve it.
Let's summarize what we learned about the Query Editor and its functions in this module.
Time to take a look at the Power BI Desktop workflow again to understand what we achieved so far and what additional steps we have to go now.
Before we dive deeper, let's take a look at the main differences between the Query Editor and the data model.
We build the base for our data model in the previous module. Time to understand why it is now important to add relationships between the different tables.
Let's understand what we can to in the relationship view and what additional functionalities the manage relationships function offers.
Time to understand cardinality, what different types of relationships we have and how we can use them to enable the communication between different tables.
We need to understand many-to-many relationships and how they are related to the cross filter direction in Power BI Desktop. Let's learn that in an example.
We understood cardinality and cross filter selection. But what about active properties?
Let's understand the key differences between the M-language in the Query Editor and the DAX in the data model.
Time for an example to demonstrate the differences between the M-language and the DAX in our project.
We understood the differences between the two languages. How do we continue?
Time to get an overview of the DAX language and understand the various functions available. Let's do this in a project example by creating a calendar.
Let's take a closer look the DAX and create some basic formulas to understand the concept of calculated columns.
Now that we understand calculated columns, it's time to introduce measures and how they are different when compared to calculated columns.
Let's learn how measures generally work and apply some basic measures to our course project.
We understood the basics, now it's time to create some combined formulas.
Let's understand why defining categories for our data is important and how this helps Power BI Desktop to present our data correctly.
Time to summarize what we learned about the data model, relationships and the DAX-language in this module.
Before we dive deeper: Time to take a last look at the workflow of Power BI Desktop to see what we achieved so far and why we need to work in the report view now.
Before we start creating our visualizations it's time to take a look at the options we have in the report view.
Time to visualize our data: Let's start with creating a line and a column chart.
After creating our first charts, we now want to understand how these charts interact with each other and how we can add tooltips.
Let's understand how to add color saturation to our visualizations and how this affects the way our charts are displayed.
Let's add more information to our visualizations and change the level of detail of the displayed data by applying hierarchies and enabling drill-down.
Time to take a look at the formatting options in Power BI Desktop and understand how to change the sorting order of the data displayed.
We talked about interactions: Let's now learn how slicers work and how they can increase the interactivity of your report page.
Let's learn how to add multiple slicers and how these can interact with a treemap and a table visualization.
We want to specify what data should be displayed in our report: Let's understand the different filter types available in the report view.
We already know different visualization types. Let's now learn what cards are and how we can use them in our report.
Time to understand how to display different data in one visualization using combined charts. Additionally, let's learn how to create waterfalls.
After finishing this module, it's time to summarize what we learned about visualizations in Power BI Desktop.
We finished our work in Power BI Desktop - What now? Let's take a look at where we are now.
Depending on what we want to achieve, we have different options on how we could continue. Let's understand these options.
We work with Power BI but we have to be aware of the different versions and the changes in 2017. Let's take a look.
Time to leave Power BI Desktop and time to use Power BI Service the first time.
Let's understand what we generally can do in Power BI Service before we start our deep-dive in the next videos.
We worked on our project in Power BI Desktop, now we want to import the project data into Power BI Service. Time to understand how to do this.
We imported our data. How does the dataset menu work and what can we create in there?
We understood the dataset. What about the reports? Where can we find our report created in Power BI Desktop and how can we create new reports?
Dashboards? Let's learn what dashboards are, why we need them and how we can create dashboards in Power BI Service.
Let's take a look at our workspace and at the additional options we have for our datasets, reports and dashboards.
We imported our data. But we want to refresh the imported data regularly. For this purpose, we need to take a look at gateways.
We now know why we need a gateway. But which is the right one to choose for our purposes? And how do we install the gateway? Let's take a look.
So far, we worked alone on our projects. But what if we want to work together with other people? Let's understand how this works.
We know how to import and connect to our data. Now it's time to understand app workspaces to be able to work together on our projects.
We finished our work, now we want to share the results. Let's take a look at the options we have.
Let's learn how we can publish our app and how we and our colleagues can access the shared data.
We worked on our project data. Time to take a look at predefined content packs from online services to understand how these work.
We took a close look at Power BI Service and we know how to access our data from the computer. Let's now understand how to access our data from mobile devices.
We learned how to share our project in an app. But what if we only want to share the dashboard and the report without the underlying dataset? Then we don't need an app, we only need to use a specific function in our workspace.
We now understand why we need Power BI Service and Power BI Mobile. Let's recap what we learned about those two tools.
Let me introduce you to this module.
JSON is a popular data format which allows you to work with nested/ structured data. Learn how to import JSON files in this lecture.
What if you want to import data directly from a RESTful API? That's of course possible with Power BI and this lecture shows how it works.
I also want to show you how to import data from a MySQL server - so let's create one!
This lecture teaches you how to import data from a MySQL Server and also explains how to get the data from any other SQL Server.
Let me wrap this module up.
Which prerequisites should you bring to succeed in developing for Power BI?
Let me introduce to this module and what you're going to learn in it.
It's of course key to understand how custom visuals work behind the scenes and which skills you therefore need to get the most out of them.
Time to create and use our first visual. This lecture explains how to install the Power BI Developer tools and create a first, very basic visual.
Since we're programming our own visual, we need a development tool. In this lecture, will install a free IDE which makes coding much easier.
How does a custom visual work under hood, which roles does this strange visual.ts file play? This lecture dives into these questions.
What's the role of all the files and folders the developer tools created for us? Learn more about it in this lecture.
As mentioned earlier, we of course want to work with data in our HTML document (which our visual technically is). Learn how to install a fitting package (d3.js) in this lecture.
Time to leave the boring text-only visual and our first shape!
Having a basic, static shape is nice but boring. Time to connect it to some (static) data source!
Thus far, our visual doesn't scale at all - time to change that in this lecture!
Using static data isn't the most exciting thing in the world. Let's prepare our visual to take some dynamic data provided by the user.
Now that the capabilities have been set, let's start adding some code to extract the input data.
This is a basics section, so let's keep it simple and limit the data input to just one dataset.
We're finally there, let's complete our data-extraction code in this lecture!
With the data extracted, let's now add some code to finally display it and connect our shapes to that data.
The code is working but not optimized. Whilst this is no advanced section, let's still put some optimizations into place.
We connected our visual to dynamic data but the coloring is pretty monotone. We can change that! This lecture shows how it works!
Do you remember that you could select shapes in visuals to also affect other visuals? Learn how to add such a functionality to your own visual!
Right now, we have to guess which bar belongs to which category. Learn how to easily add an axis to your visual!
Most built-in visuals give you some configuration options. In this lecture, we'll start adding our own ones for our own visual.
Allowing the setting of options is nice but let's now also have a look at the code we should add to extract the configuration.
To finish the custom config part, we need to inform Power BI about it. Let's do that in this lecture.
Let's have a look at some possible next steps.
You probably don't want to stick to using your custom visual in your own Power BI service dashboard for testing purposes only. This lecture explains how you may package your visual to either use it in Power BI Desktop or distribute it.
Let me wrap this module up.
Want to dive deeper or learn more about the packages and languages used in this module? This lecture is for you!
You finished this course, great job! Let's take a look at what we learned and how we can continue now.
Having worked as a business analyst in both a major consultancy and an investment bank, I always found myself confronted with both various and complex datasets and challenging client demands. Therefore, the increasing amount of data required constant adaption of new methodologies to analyze data efficiently and to make the move from basic Excel-driven analyses over VBA-driven automation to more elaborate business intelligence tools.
Being an early adopter of new and quickly evolving tools, I always enjoyed both learning these tools and passing on my knowledge to my colleagues and fellow students. It's that combination of self-taught data analysis experience and its application in a highly competitive consulting environment for various clients which gave me the ability to evaluate tools from an industry perspective as well as from a learner's perspective. The latter also allows me to identify the pain points students might hit when learning these tools.
Since I always found it hard to find high quality, understandable and comprehensive learning materials focusing on the key capabilities of the specific tools, I decided to take a shot and to create such materials on my own.
And of course I am not only passionate about creating these materials but also about passing on my knowledge using these materials to you.
Experience as Web Developer
Starting out at the age of 13 I never stopped learning new programming skills and languages. Early I started creating websites for friends and just for fun as well. This passion has since lasted and lead to my decision of working as a freelance web developer and consultant. The success and fun I have in this job is immense and really keeps that passion burningly alive.
Starting web development on the backend (PHP with Laravel, NodeJS) I also became more and more of a frontend developer using modern frameworks like Angular or VueJS 2 in a lot of projects. I love both worlds nowadays!
As a self-taught developer I had the chance to broaden my horizon by studying Business Administration where I hold a Master's degree. That enabled me to work in a major strategy consultancy as well as a bank. While learning, that I enjoy development more than these fields, the time in this sector greatly improved my overall experience and skills.
Experience as Instructor
As a self-taught professional I really know the hard parts and the difficult topics when learning new or improving on already-known languages. This background and experience enables me to focus on the most relevant key concepts and topics. My track record of many 5-star rated courses, more than 100.000 students on Udemy as well as a successful YouTube channel is the best proof for that.
Whether working as development instructor or teaching Business Administration I always received great feedback. The most rewarding experience is to see how people find new, better jobs, build awesome web applications, acquire amazing projects or simply enjoy their hobby with the help of my content.