u/Amy165

I’m transitioning into data analytics/data science after many years working mostly in academic/research environments. Recently I completed two more business-oriented analytics projects and I’m unsure how much space they should have in my CV.

Right now my CV mainly has:

• profile,
• technical skills,
• professional experience,
• and a GitHub link.

But I’ve seen many people recommending adding at least one strong project directly into the CV, especially when changing careers.

The problem is that keeping everything within one page already feels quite tight 😅

At the same time, I was thinking that maybe including a project could help demonstrate more concrete business-oriented results and analytical work beyond just listing tools.

For people working in analytics/recruiting:
Would you recommend adding a small “Projects” section directly in the CV?

I have been working in data analytics / BI roles and in the last period I’ve been building several Power BI projects.

I’ve been considering taking the PL-300 certification, but right now the exam cost represents a significant expense for me, so I’m trying to understand whether it realistically helps with job searching/interviews or if strong projects and portfolio work tend to matter more.

I should also mention that my experience so far has been mostly with Power BI Desktop. I don’t currently have access to an enterprise environment or Power BI Service features for hands-on practice beyond what I’ve learned through videos and documentation in that sense.

For those who already took it: (or recruiters searching for BI developers)

• Did it help you get interviews?
• Did recruiters care about it?
• Did it help mainly for ATS filtering?
• Or was the learning itself more valuable than the certification?

I’d especially appreciate opinions from recruiters or people already working in BI/analytics roles.

Thanks

I’ve been trying to improve the visual/design side of my Power BI dashboards recently.

My approach so far has been to keep them relatively clean and not overload them too much. Maybe it comes from my teaching experience. I usually feel that 2–3 simple dashboards are easier to understand than one extremely crowded page.

I also tend to use light backgrounds and soft colors (a lot of blue because it’s my favorite 😄), plus the classic green/red highlights only when I really want to emphasize something.

Recently, during an interview, I was asked about working with mockups and dashboard versions for clients. I know that in many projects people design multiple layouts/mockups first and let the client choose the direction, but I’ve never really worked that way myself yet.

I’ve tried using Figma a bit, but honestly I mostly look at examples and reproduce ideas manually because I still don’t fully understand how people usually work from templates/mockups in real projects.

Curious how do you approach this??

• Do you use templates/mockups?
• Any good free resources or workflows you’d recommend?

Hi! I worked with a frequency vs severity scatter plot in Power BI, using a categorical field in the legend to create multiple groups.

The issue is that Power BI assigns a different color to each category, and since I have quite a few groups, the visual becomes quite noisy and hard to interpret.

If I want to simplify it (for example, keep everything in the same color or just highlight a few key segments), it seems I have to change the colors one by one manually, which is not very practical.

Is there a better way to handle this?

Also, I’m not sure if I’m mixing things up with Tableau, but I remember using something like a “Details” field to separate points without affecting the colors. Is there an equivalent in Power BI, or am I misremembering?

I’ve been working on a Power BI project analyzing a vehicle insurance portfolio, mainly focusing on pricing and performance.

I picked a dataset from Kaggle because I wanted to explore something related to insurance, but it was more challenging than expected. Some fields were not clearly defined, so part of the work was figuring out what the data actually represented. I decided to continue anyway.

For the pricing analysis, I normalized everything per year (using exposure) to make comparisons more meaningful. It became quite clear that some segments were underpriced, especially in earlier years.

I also explored frequency vs severity, and noticed that the most underpriced segments tend to deviate from the general pattern, which made them stand out even more from a risk perspective.

I also tried some basic risk prediction models, and for the first time used Python scripts inside Power BI to improve the visualization of the confusion matrix.

First time sharing something like this here and I’d really appreciate any feedback 🙂

 - How do you usually approach pricing or risk-related analysis in Power BI? 

- Any suggestions on how to better structure these kinds of visuals?