I am slowly morphing into a true data nerd. This week I started learning the data engineering part of data. Learning about lambda and delta lake architecture, ETL, OLTP, OLAP, Apache Spark, notebooks, parquet files, SCD Types 1 2 3 and 6, pipelines, serverless and dedicated SQL pools, PolyBase and there’s more to come.
My head is exploding.
The past two weeks I spent most of my time studying for the MS DA-100 exam, also known as ‘Analyzing Data with Microsoft Power BI’. This morning I took the exam and passed with a very decent score of 893/1000 (although I have to admit I was a bit annoyed not breaking the 900 barrier). After the training and passing the exam I am now skilled enough to start my own data analyzing projects. I’m looking for ideas where to apply my new skills.
The past two weeks I was introduced to the ins and outs of Power BI. Four full training days I’ve been practising doing transformations on columns, making calculated measures and dragging columns and measures into visualisations. For those who are not into data analyses, Power BI is a piece of software developed by Microsoft to handle data sets. When spreadsheets are no longer sufficient to handle your data, you can step up the game by using Power BI.
Before this training I practised with SQL and Python to create scatter plots and calculate summations, and I have to admit that after using Power BI I finally understand what kind of actions I was doing to data sets when using Python. Power BI is a visual tool, so you click on the transformations you need to do to prepare your data and the results are immediately visible. And you can easily undo a step with one click.
I wouldn’t say Power BI is data analysis for dummies, because you still need to know conceptually understand what you’re doing to the data, but I totally see why many people prefer using Power BI over messing about with Python. It is visual, quicker and can create interactive reports and dashboards. The reporting part is (for now) least interesting to me, as I don’t work in a big company with lots of (sales) data that needs to flow through the organisation. However, I do feel more confident after the past weeks that I’m capable to get meaningful information from data sets. And that was the whole point of investing in this course.
If my memory is correct the last exam I took was in 2004, when handing in my master thesis (on blogging and Habermas, when blogging was still new and shiny). That was an oral exam, for two of my professors. I really can’t remember the last paper-based exam I took before being allowed to hand in my master thesis. It probably was not a memorable subject or one of those mandatory statistical analysis exams. Since 2004, I never needed to sit an exam for anything. Not even for an assessment for hiring purposes, as I’ve been self-employed since finishing university.
Today I broke that examless streak.
The program at Techionista is thoroughly sponsored by Microsoft and therefore I’m learning all about Microsoft Azure. And to be able to learn that you don’t just read documentation, you increase your knowledge by practicing for an exam. Today I took my first exam, on the Azure Fundamentals (AZ-900 for insiders) and passed it with a proper score of 820 (700 needed to pass).
To avoid installing proctoring software on my computer I reserved a slot at the nearest test center. That happens to be in my home town and I learned later that it’s run by an institute that teaches IT skills to (young) people who are either on the autistic spectrum or highly gifted (many of whom can’t manage to fit into the standard school system and drop out without a degree). I noticed that the person who took me through the sign-in procedure made sure every rule in the procedure was followed in a kind manner, he properly guarded the silence in the hall next to the exam room, and as a bonus earplugs were available for all examinees. I’ll schedule my next two exams here as well.
Het is natuurlijk absoluut simpel om te registreren of een deelnemer aan een klinische studie fysiologisch man of vrouw is. Als je dan een nieuw ontwikkeld vaccin mag testen op pandemische schaal zijn de getallen ook best snel statistisch significant. Hoe logisch is het dan om ook even een kolom m/v op te nemen in je database van gerapporteerde bijwerkingen? Ik dacht dat medische onderzoekers allemaal wel een kopie van Invisible Women op hun nachtkastje hebben liggen. Wat een naïeve gedachte van mij zeg.
[…] de vaccinmakers hebben het element ‘sekse’ goeddeels genegeerd in het vaccinonderzoek en de behandelmethoden van Covid-19. Zo had geen van de gepubliceerde klinische proeven van vijf coronavaccins de opgetreden bijwerkingen uitgesplitst naar sekse.
Hoe vrouwen vergeten werden in het Covid-19-onderzoek (bron: Trouw)