Shin Nix

It is finished! The second to the last vlog talked about the different visualizations and when they are appropriate to be applied while the last vlog was about the final project which utilized all the lessons learned from Vlog 1 up to now. And, here is my final output. In the above dashboard, we can see some demographics as indicated by the 630 total survey participants with an average age of 29.87 years old. We can also see that data scientist has the highest average salary ($94k) followed by data engineer ($65k) then data architect ($64k) among data professionals. We can also see that the favorite programming language of  data professionals is Python, followed by R and other programming languages and the distribution of data professionals in each of the given programming languages. For example, for those who selected Python as their favorite programming language, 255 of those were data analyst, 54 are either a student or still looking for a job or no job at all, while 54 have oth...

In this lesson, the focus was on bins and lists. From what I understand in the video, list is a grouping of items into one specific heading / group name while a bin is a sort of box where you put data inside this box once it is within the range of values acceptable to a specific bin. I also noticed that bins and lists must be done prior to visualization which means that all these preparations must be done in the Power Query Editor. Here is an example of a list. And, here is an example of a bin. After executing the previous two windows, two additional columns will be added to our data. Basically, these bins and lists capability is like an automatic "IF-ELSE" statement which gives the name of the bin / group when it satisfies a condition. Now, that we have prepared the data, we now proceed with the visualization. Here is for the Customer group list: Here is for the Data Purchased bins:

In this lesson, the focus is on "Conditional Formatting" which is very much similar to the conditional formatting in MS Excel which I could relate to following the making of the joint reporting system for personnel during my second job. Basically, we click the data columns that we want to be displayed in a tabular visualization.  Next, we select the columns that we will be applying conditional formatting to > Right-click on that column > Select Conditional Formatting > Select from among the options which is more appropriate for your application In this particular exercise, we utilized Background conditional formatting using gradient (applied on the first column of the first table) and rules (IF ELSE which was applied on the second column of the first table), Icons conditional formatting (applied on the second column of the first table), Data bars conditional formatting (applied on the second column of the first table and the fourth column of the second table). In the...

In this lesson, the topic discussed was regarding drill-downs. I can compare it to a "Prezi" equivalent in Power BI. In Prezi, we can zoom in and out of a powerpoint presentation to see details encapsulated in a mother topic. In drill downs, we are shown a summarized graph of the data but if we want to know some of the nitty-gritty stuff within that graph, we can "drill down" further to see the specifics within that summary. As an example, we have the following summarized visualization for the Price by Product and Store. If we activate the drill-down icon and click one of the bars i.e. Costco, we will be able to see the total price of the products that were bought at Costco as shown below. We can further drill down on specific quarters to see how the product sold in those quarters. In this case, more purchases of bottled water were recorded during the first quarter. Note: Drill-downs are specifically useful when conducting presentations and stakeholders want to know...

In this blog, I will be changing my approach to how I write the blog. In the previous blogs on Power BI, I was simultaneously doing what Alex is doing in Power BI and then, whenever I encounter issues, I will be writing it directly in the blog and then continued where I left off with the video once I am done. Here, I will watch the video first, take note of important keywords and lessons that Alex emphasizes, and then replicate what he did on the video. This lesson focuses on DAX or Data Analytics eXpressions. Based on what I understood on the video, DAX can be likened to the formulas associated with MS Excel. And, since Power BI is also a Microsoft product, there are DAX that are similar, if not, the same with what can be found in MS Excel (One example is the IF expression in Power BI). In this exercise, the process of using DAX was explained starting with creating "New Measure" and then using various DAX to be able to visualize the intended outcome in a table. For example, ...

I am now in the third tutorial on Power BI. In this lesson, the topic is on creating and managing relationships. Important concepts that were introduced are cardinality i.e. many-to-1 (*-1) or (one-to-many (1-*), cross-filter where if we select "Both", this means that we can think of many separate tables as one table. I have these three separate tables. Apocalypse Sales: Apocalypse Store: Customer_Information: Notice that each of the values in the Customer ID column of the third table can be seen in the Cust ID column of the first table. But notice that there are only four rows in Customer_Information table while there are many rows in the Apocalypse Sales table. The cardinality of the relationship means having unique or multiple instances per value for the joining field between two tables. This means that the cardinality of the relationship from Apocalypse Sales and Customer_information in terms of Customer ID = Cust ID  is Many-to-one. Conversely, the cardinality of the rel...

Power Query is a  preliminary step in subjecting the raw data to Power BI to create the necessary visualizations. In Power Query, we can transform the data, rename/add/delete columns, change data type among other things. To be familiar with Power Query, we will convert the following Summary Table to the Purchase Tracker table right below it. Summary Table: Purchase Tracker: The first thing that we will do is to import the data from Excel to Power BI. We will only select the "Pivot Table" and the "Purchase Overview" sheets and then click "Transform" so that we will be led into the Power Query Editor. In this particular exercise, we will do most of the transformations on the Purchase Overview sheet and the reason we included the Pivot table is to serve as a reference. We will now perform data cleaning by removing rows that have "null" in its entries. Performing the steps that were outlined on the video, we were able to transform the Purchase Overvi...

Just want to pass by here and update you regarding learning about the data visualization tool - Power BI. By the way, BI is an acronym for business intelligence. I am using the YouTube videos from Alex the Analyst. Hopefully, this works out. I am already finished with video #1 and appreciating how this tool was able to create visualizations (graphs) just from clicks and a few editing and navigation in the software. Currently, I realized that the ones presented can still be done using MS Excel although the same graphs could be quite tedious in Excel than in this platform.