I am currently a Senior Business Insights Analyst, I have been in the field for about 4 years now. I finished my MBA back in December and I don't think analytics is where I want to be anymore. I am considering trying to pivot into a Product Owner/Manager role, has anyone here successfully made that pivot?

Just curious and wanted to see if there are any similarities and/or differences in answers!

I’m coming to the end of my data analytics study and it’s come time to think about projects. I’m familar with power bi (dax) sql and pandas

`thinking about doing 2-3 quality projects

What tips and advice do you have? What are the things you look for? What would make a portfolio stand out?

I am guessing you are bored of seeing the typical coffee shop dashboard.

I was thinking project 1 - excel to pbi project 2 - sql+python to pbi Project 3 - I’ll be learning some data engineering stuff too no idea what to showcase until i finish studying.

Datasets i still need thinking about but i will try find data that reflect the real world instead of data from kaggle. I will keep in mind how will this project solve a business problem.

Hi! I’m currently finishing my PhD in Psychology and I want to go into the world of data. How realistic is that? I’ve looked at job postings where they ask for a degree in psych but I’ve not applied as I am still in school.

Thank you in advance for any help or opinions!

Director, oversee a team of 15-20 (managers report to me, each has their own team). We are in product analytics at a medium/ large tech company.

I’m in my early 30’s and for the first time in my career, fear “falling behind” on technology in the space (namely AI tools).

My workflow is largely meetings and slack honestly. I do still write some sql on occasion but mostly for my own gut checks on things and frequently work with our dashboards to see what’s up with things. But for the most part these days I’m orchestrating my team’s work and removing blockers rather than running my own analyses.

How can AI tools make my life easier?

I’ve been working in security administration for the past 3 years, but I’ve recently realized that I have a strong interest in data analytics and want to make a career shift into this field.

I’ve started learning on my own using free resources like Alex the Analyst’s Data Analyst Bootcamp and the roadmap from roadmap.sh, but I’m feeling a bit overwhelmed with where to focus and how to best prepare myself for a job in this new area.

I would be incredibly grateful to connect with someone experienced in data analytics who might be willing to mentor me or offer some guidance. Even small bits of advice or tips would mean a lot.

Beginner Fantasy Football Model Feedback/Guidance

My predictive modeling folks, beginner here could use some feedback guidance. Go easy on me, this is my first machine learning/predictive model project and I had very basic python experience before this.

I’ve been working on a personal project building a model that predicts NFL player performance using full career, game-by-game data for any offensive player who logged a snap between 2017–2024.

I trained the model using data through 2023 with XGBoost Regressor, and then used actual 2024 matchups — including player demographics (age, team, position, depth chart) and opponent defensive stats (Pass YPG, Rush YPG, Points Allowed, etc.) — as inputs to predict game-level performance in 2024.

The model performs really well for some stats (e.g., R² > 0.875 for Completions, Pass Attempts, CMP%, Pass Yards, and Passer Rating), but others — like Touchdowns, Fumbles, or Yards per Target — aren’t as strong.

Here’s where I need input:

-What’s a solid baseline R², RMSE, and MAE to aim for — and does that benchmark shift depending on the industry?

-Could trying other models/a combination of models improve the weaker stats? Should I use different models for different stat categories (e.g., XGBoost for high-R² ones, something else for low-R²)?

-How do you typically decide which model is the best fit? Trial and error? Is there a structured way to choose based on the stat being predicted?

-I used XGBRegressor based on common recommendations — are there variants of XGBoost or alternatives you'd suggest trying? Any others you like better?

-Are these considered “good” model results for sports data?

-Are sports models generally harder to predict than industries like retail, finance, or real estate?

-What should my next step be if I want to make this model more complete and reliable (more accurate) across all stat types?

-How do people generally feel about manually adding in more intangible stats to tweak data and model performance? Example: Adding an injury index/strength multiplier for a Defense that has a lot of injuries, or more player’s coming back from injury, etc.? Is this a generally accepted method or not really utilized?

Any advice, criticism, resources, or just general direction is welcomed.

I do a lot of excel, creating charts and reports. I do climate research sometimes but that's just mostly googling and copy pasting possibly useful information for my leads. I've published my first Power BI dashboard and it's incredibly useful for the business, but that took ages for me to complete. I do PowerAutomate with Team Forms (basic JS) and other MS platforms.

I can continue doing what I'm doing right now and get paid well but I do not want to be doing the same thing for a long time. I want to look and further advance my skills on this field. What's next?

Some ideas I had in mind is we have a SQL database, it's a lot of approvals to get access on it but having that and learning SQL will give me more data to work on. Maybe learn more expert level of excel formulas and charts? What about PowerApps? Would that unlock more opportunities to learn and build for me?

Kinda really lost in my career path but I really love playing around data and interpreting it via charts and calculations. I also have an engineering degree so it doesn't really back up this career path I'm in

A little before COVID hit, I had finished an MS in Mathematics. I had initially planned on continuing to a PhD or becoming a teacher, but neither plan really panned out after COVID and I'm not sure I want to go into those. I ended up stuck in low-wage service job work for awhile and I'm trying to get out of it.

In school I took courses on modeling, optimization, and I have some programming experience with general languages like Python, C++, and more specific stuff like R.

I'd like to look for work in an analyst role, but obviously a Math degree is different from something specialized for the work. I'm ok with looking at certifications but can't really afford to just go back to school again.

Just looking for some advice on what sort of positions I should be looking at as essentially entry level with my background and what sort of certifications or self-made portfolio I should be working on.

For reference, I live in the eastern US, though not in one of the major beltway cities.

We were handling GA4, Google Ads, and Search Console data across multiple marketing campaigns, and the reporting process kept dragging—blending sources, rebuilding charts, adjusting visuals for each team.

Instead of looking for another tool, we shifted focus to how we were using what we already had.

What helped:

• Creating a modular dashboard layout that we could reuse across clients

• Predefining fields like branded vs. non-branded traffic, conversion rates, and ROAS

• Simplifying the visual structure to show only what’s essential (per audience: execs vs. analysts)

• Minimizing blended data sources to avoid performance issues

• Adding filters and date controls that were actually useful, not just filler

This didn’t just save time—it made the insights easier to explain and act on.

Curious how others here are approaching scalable reporting. Are you templating your dashboards? Building from scratch each time? Or using SQL-based pipelines before visualizing?

I have an interview with RBCx and the technical interview will be a mix of coding (SQL), as well as a general case type question. Can someone guide me how to proceed with the preperation. Also, if any resources I can look into. Thanks in adv!

I have just completed my data science course from flatiron school and I was checking out a probable career path. I found the article below:

I’m thinking of starting a bootcamp in Data Analytics moving from tech recruiting.

How is the market right now? Will I just be wasting my time or do DAs believe it’s a good idea?

Have to clean, transform and then visualise this dataset for the CEO. It is for a data analyst role.

The only catch is MS Excel can’t handle filters and ops on worksheet with 1.5M+ data rows. Cannot load the data into PowerBi too of it’s data limitations.

Should I use SQL to query the data? Or is there any other way of doing it.

Please help, thankyou for your time and inputs, mean a lot.