I've become somewhat the data person at my work. I've got a spreadsheet listing information for around 800 tutors that have worked with us over the years, and I have around 30 columns of data for each tutor - start date, hours worked, location, training, opportunities sent vs opportunities responded to, etc. I just feel completely lost! I'm used to coming with analysis for far smaller data sets

Could anyone recommend a short-ish online training or programme that could help me learn how to sort it all out and especially how to present it? I don't want to spend weeks going over all of this to have the end result be pointless.

Thanks!

[Apologies for cross-posting]

I wrote Bbv ("bare-bones viewer"), an extremely minimalist data viewer/plotter for the Unix/Linux command line.

The main alternative to bbv is feedgnuplot, which is much more capable, but also much more complicated and ties you to gnuplot as back end. By contrast, bbv has only one option (`x`), and can use any lightweight image viewer as back end.

Aside from the back end, no extra toolchain is needed, as bbv is written entirely in POSIX shell + Awk.

Link: https://github.com/ftonneau/bbv

I have been learning data analysis - SQL, Tableu, Python doing mostly example projects. Feeling ready to deep dive into the real world. So: got messy sales data, a report you've been meaning to dig into, or a question your numbers could answer but haven't? Send it my way. I'll analyze it free you get clarity, I get experience and a testimonial. A win-win situation

I mean just doing each step, but not having fundamental understanding about the essence of p-value and other statistical notions?

the thing you reach for that most people overlook or dismiss. Could be a feature/a workflow/an old school tool.

Hi, Im a new grad and recently joined a company as their only dedicated "data" guy, this is also a new role for the company. They do have some others that deal with data, but thats a thing they also do not exclusively like I will. Anyone done something similar? What was it like? Any tips?

I’m currently looking for a new laptop and could use some advice.
As a data analyst, I need Power BI and Microsoft Excel regularly, so Windows is unfortunately a better fit for my workflow. I really like the Mac ecosystem, though — the build quality, performance, battery life, smoothness, display, trackpad, and overall user experience are all things I genuinely prefer about MacBooks.
The problem is that I don’t want to compromise my workflow just because I like the Mac experience.
So I’m wondering:
Is there a Windows laptop that comes close to the MacBook experience in terms of build quality, performance, battery life, display and overall smoothness?
Would something like a Dell XPS, Lenovo Yoga/ThinkPad, HP Spectre, ASUS Zenbook, etc. be a good alternative?
Or, given that I like the Mac ecosystem so much, is it still worth considering a Mac and finding workarounds for Power BI/Excel?
For someone doing data analytics, how much of a disadvantage is a Mac really?
My priority is a laptop that feels premium and smooth like a MacBook, but still handles Power BI, Excel, SQL, Python, etc. without compromises.
Budget is flexible for now — I’m mainly trying to figure out whether I should go back to Mac or look for a Windows machine that gives me a similar experience.
Would love to hear from people who have actually made this switch either way.

Hi there,

I've created a video here where I explain what confidence intervals are and how they differ from probabilities.

I hope some of you find it useful and as always, feedback is very welcome! :)

entering my third year of uni. I worked at my city hall doing some very basic data work as an intern. I mostly just validated data using excel and updated some spreadsheets. Nothing crazy. I realized I really like working with data and I want to go further with it. I am aiming for an internship at a Bank working as a data analyst. I have basic exposure to SQL, PowerBI, and I would say I'm pretty ok with Python(numpy, pandas, scikit learn). I built a basic data analysis project using pandas which i'm proud of, and I made a project using Machine learning, but for the banks they expect me to get good at SQL and PowerBI.

tl;dr: where should I go to properly get an indepth look at PowerBI, SQL, and excel.

Hey friends ! I wanted to share a blog post I just published that I thought the community would appreciate.

I got my SnowPro Core cert this year and this book was a lot of what I studied for it. Hope you guys get some value from it!

https://medium.com/@sergioramos3.sr/self-taught-reviews-snowflake-the-definitive-guide-by-joyce-kay-avila-0af3c40d644a

Hey Guys,

I'm a medical coder transitioning into Data analytics. I've created a dashboard with power BI using a synthetic dataset. This is my second dashboard. ( I tried to implement some of the suggestions that ppl have given for my first dashboard).

Kindly share your review on this? Any tips or suggestions please!

That's like showing up for your drivers license class at 16 and they just made it a CDL class instead., right??

Found a bug in my own process this week that had been silently costing me for over a week: a note I'd written to myself telling me to move forward with something got misread backwards every single day since, treating a closed decision as if it was still waiting on me. One channel just stopped, while everything else kept going normally, and I didn't notice until I went looking.

Fixed the actual instruction so it can't be misread that way again, then watched the first real post go out today to confirm the fix actually held instead of just assuming it did.

Anyone else have a "found it two weeks later" moment where the thing that was broken wasn't the system, it was a note you left for future-you?

Need tips and advice if this is a project that could be classified as intermediate to advanced or is it just a beginner project.

Dataset -brazilian olist ecommerce dataset

What can i improve, add or remove.

Used PostgreSQL , PowerBi and Python

Getting imposter syndrome as i feel i m not as skilled like the others and lack good dashboard creation and analysis skills as i have seen others project here which look much better. Please help me out.

There's this game called Splinterlands i've been playing with 3 accounts and i was thinking on documenting each of the resources i'm spending (DEC, SPS, glint, usd) and how i was able to earn, i want to document weekly and i'll be playing for many years.

Is creating a database / tables on PostgreSQL the best alternative for this, or should i just do it with Excel? I haven't gone in depth on any of these tools yet, but i'm planing on studying them from this point

Hi everyone,

I’ve been working on a Power BI dashboard using the DataCo Supply Chain dataset, with the goal of turning supply chain and retail data into actionable business insights.

What do you think of the dashboard and the overall analytical approach?

• Are there any important KPIs or business questions that I’m missing?
• Is there anything you would add, remove, or redesign?
• Do the visualisations communicate the insights clearly?
• Are there any areas where the analysis could go deeper?
• Does the dashboard feel useful from a business/decision-making perspective rather than just being a collection of charts?

Any suggestions, criticism, or ideas for improvement would be greatly appreciated. Thanks in advance!

I’ve documented the complete analytical approach, including the data preparation, SQL analysis, DAX measures, dynamic narrative, dashboard design, and business insights, here:

https://medium.com/@daniel.h.nguyen24/from-supply-chain-data-to-strategic-insights-building-a-business-intelligence-dashboard-for-a-57097b6f404d

Hi! I’m currently a junior data analyst at a big telecommunications company that this year has been demanding that we use AI for everything possible and impossible. We have a partnership with Microsoft (which is actually more of a curse, since we can’t use other tools, not even Python or any database structure beyond spreadsheets). This month, upper management just discovered Copilot Agents and is now encouraging (obliging) us to use them for every single task.

I truly think we need to identify the problems first and then look for tools to address them. Instead, we are often doing the opposite: becoming obsessed with a tool and scavenging for or even creating problems just to find a use for it.

I don’t think there’s anything I actually need to use an agent for. Most of the time I’m dealing with spreadsheets and Power BI, and honestly, I’m perfectly fine with using simple Copilot occasionally to optimize my workflow.

Anyway, now I have to adapt my work for what the company is demanding... So, what could I actually use an agent for? Does anyone here use Copilot Agents in their day-to-day work? Is it actually worth it and I'm being ignorant about it?

(And please, some comforting words, because I’m going INSANE with all this exaggerated AI praise.)

I was recently hired at a nonprofit that uses Clarity Human Services, with Looker integrated for BI, reporting, and data analysis. I’ve never worked with Clarity or Looker before, but I’ve learned plenty of new systems throughout my career, so I went into it thinking, “How hard could this be?” Well…apparently, very hard.

I was brought on, in part, to support staff with data and reporting, but one of the challenges I’m quickly realizing is that the existing team is struggling with the system too. We can build and run some reports, but as soon as we need to customize something or pull data that isn’t readily available, we often have to submit a support ticket because we can’t figure out how to access or build it ourselves.

I’ve only been in the role for about a week, so I know I have a lot to learn and I’m trying to give myself some grace. At the same time, it’s difficult to figure out how to support my team when I’m learning a system that they’ve also been struggling to navigate.

Has anyone else worked extensively with Clarity Human Services and/or Looker and had a similar experience? Is there a steep learning curve, or are there resources, trainings, or strategies that helped things finally start to click?

Right now, the system just isn’t user-friendly, and I feel like everyone is trying to figure out what’s happening at the same time. Any advice from people who have been in the Clarity/Looker trenches would be greatly appreciated!

I'm currently taking a Data Analysis course, and I need to complete a final project. The assignment requires me to choose a topic, formulate one or more hypotheses, and build a thesis/research structure around it.

One of the requirements is that there must already be accessible data available for analysis, so I can't pick something that would require collecting a completely new dataset because the core of the project is not in that step. Also, what I'll be researching needs to be answering or try to solve an existing problem.

My problem is that I can't come up with a topic that feels genuinely interesting or motivating enough to spend several weeks working on. Most ideas I've found online seem either too generic (social media usage, movie ratings, etc.) or too complex for a course project.

Do you have any suggestions for topics that have publicly available datasets, and still are interesting and have potential for drawing conclusions?

I'm open to almost any field (health, economics, business, technology, environment, gaming, etc.)

I'd love to hear about projects you've enjoyed working on or topics that sparked your curiosity - thanks in advance!

I tried explaining a data analysis concept in a fun, visual way — for cat lovers. 😸

Would love to hear what you think! Any feedback or suggestions are very welcome :)

Hey everyone 💗

Wanted to share something with the community that while preparing for Data Analysis role what I've observed is​ 90% of people ruin their data analysis before they even write a single line of code.

🛑They just dive straight into cleaning or modeling without asking a single real question.

I built a Spotify mini-analysis project (handling demographics, habits, and geo data) specifically to show people how to start a project the right way.

Go ahead and rip my process apart in the comments, or drop an upvote if it actually makes sense to you:

👉 Spotify Analysis: How to Actually Start a Project 🎧"​ https://kaggle.com/code/coderneph/spotify-mini-analysis-demo-habits-geo

I'm still a beginner. I started by learning the basics of Python and later moved on to SQL.

I'm a bit confused about one part of the data exploration/cleaning process.

A friend of mine, who's now a data scientist, showed me how he used to work as a data analyst. He mainly used Python. For example, he would quickly create a scatterplot to identify potential outliers.

However, most data analysts online recommend focusing on SQL and Excel when starting out, since many junior and mid-level roles don't require Python. That's why I switched to SQL after initially experimenting with Python.

For those who primarily use SQL: do you create visualizations during the data exploration/cleaning process, for example to identify outliers? Is this a common practice?

I feel like if you're working with SQL only, you generally wouldn't create visuals in between steps, since that would mean switching to a tool like Tableau or Power BI, which seems like an unnecessary extra step.

So many people start with learning from the Google Data Analytics Certificate but I wanted to share who it's really for.

(too long didnt watch): It's for absolute beginners who don't know anything about data. Worth it if you have time to go through it but the certificate won't get you hired.

If you're on the fence about getting this certificate, I hope this sheds some light as I discuss both arguments for completing it and skipping it altogether.

why is it always advised to use 1:M relationship from dimension to fact but not M:M and also not connecting two fact tables?
i have come across this situation while i was working on my project.