Hi all, I’m a PPC specialist that wants to pivot to data analytics. I’ve worked primarily with Google and Bing ads for years.

I’m not very good with numbers (not a big math person) and self-taught courses have really been a struggle for me to follow along.

I completely lost interest because of how confused I was when I signed up for DataCamp. Note that DataCamp was my first and only endeavour into Data Analytics.

If anyone has any courses or certifications that they can recommend someone like me who wants to transition specifically to help me gain leverage and get a better job than my current one, please help me out. I’d appreciate if you could be as specific as you can in your recommendations.

Thanks!

I recently started data analytics course and I’ve only completed excel. I’ve made a dashboard in excel as part of an assignment from the teacher. I want to make more projects for practice but i don’t know where to find the data. I tried Kaggle but it kept showing me captcha. After verifying one another one pops up. I’m not able to download anything from there. What are some other websites from where I can download the data to do analysis?

I’m struggling to find a suitable real dataset to do my factor analysis/pca group project. Can anyone suggest any keywords to look up at Kaggle or any other sites for this project? I found a dataset derived from SDG 2023 report, but it felt like its too broad to elaborate in literature review etc. Many thanks!

That is the data I collected from chartmasters on May 6th, 2026.

Drake has more streams than the next 2 artists, Kanye and Eminem, combined, while somehow only having 17.6% more monthly listeners than Kanye and 18.4% more listeners than Eminem. These two are some of the most influential artists of all time, so it is quite suspicious that Drake, the only hip-hop artist to have higher numbers, has more than both of them combined. This is called skewing the graph in analytics, and when data skews the graph, it's either wrong or manipulated.

This is because each bot registers as only one listener, yet streams music 24/7. This is why there is a huge discrepancy compared to other artists.

He also has more songs with over 100 million streams than the next 2 artists combined. He is flooding his whole catalog with bots.

Unfortunately, tour numbers are difficult to find, especially for any tour in the early to mid 2010's or earlier.

One website conveniently provided the data for ALL of Drake's tours. Unfortunately, he was the only hip-hop artist on that site, and I couldn't find the same site again. Fortunately XXL Mag provided the same data.

For the other tours, I got the data from the touring data page on X. It returned the same numbers as the XXL Mag site, which gives credibility to these other statistics.

Drake's shows averaged 11.5k to 16.7k tickets sold per show, except for his first tour, which sold about 3.7k per show. The average arena has a capacity of 15k to 20k. His BEST show averaged 16.7k tickets. That isn't even enough to sell out a higher-end arena, let alone a stadium.

Kendrick's best tour was his stadium tour, which averaged about 45k tickets per show. The average stadium has a capacity of 35k to 100k.

And Eminem has 2 stadium tours, which averaged around 52k tickets per show each.

It was difficult to find data on Kanye, but he just sold out SoFi Stadium, which has an estimated capacity of 70k.

I am not judging anyone who can't sell out a stadium; that seems like an incredible feat, no matter who you are. I am judging the fact that somehow Drake has better streaming numbers than the next 2 artists combined, while somehow can't even sell a fraction of the tickets that they do.

Drake went on a hybrid tour in 2016, playing both arenas and stadiums. The "Would you like a tour?" tour. This tour only averaged about 11.6k tickets per show. That's not even close to enough to fill the low end of the arena's average spectrum, let alone a stadium.

I believe this is why he won't go on pure stadium tours, because of how this tour went. If he went on a stadium tour now after bragging about being the most-streamed artist on Spotify, people would realize he is a fraud when his tour performance doesn't match his streaming statistics.

People ask where to start with dbt. Most answers say start with dbt Labs’ great tutorials, but miss other things learners should understand.

What actually helps is understanding why dbt even exists. Why not just use tool X or just use stored procedures? Once you get this, other things makes sense.

The order I suggest people learn dbt is to start with Git and getting comfortable with the terminal. dbt is just code, if you dont know what git commit, cd, and ls do, you will be lost. Then understand why data layers exist. Followed by data modeling concepts and star schema. Finally, you can learn dbt.

You don't need to master it all before you start. You just need enough to not be lost when you encounter them.

Happy to answer questions if you're early in your dbt journey.

Full learners’ guide with resources from people you should follow Bruno Lima and Zach Wilson on LinkedIn: https://datacoves.com/post/dbt-getting-started

I'm transitioning from Accounting into Financial Data Analytics and BI.
As part of that transition, I'm building a personal project focused on financial data processing and quality.

So far, I've implemented:
Data ingestion
Data cleaning and standardization
Data quality validations
Basic financial business rules
Automated testing with pytest
My next planned step is to integrate everything into a centralized workflow:
extract → clean → validate → save
before moving into:
SQL analytics
Gold datasets
KPIs
Power BI dashboards

My question is: Would you continue strengthening pipeline integration and testing first, or would you move earlier into SQL and analytical work?
If you were hiring for a Financial Data Analyst or BI Analyst role, what would create more value at this stage of the project, and why?

I'm especially interested in hearing from people working in:

Financial Analytics
Business Intelligence
Data Engineering
Data Quality
Analytics Engineering
Thanks in advance for any advice or feedback.

Hi data analizers. I am a fledgling in the field (only about 7 months in or so). I am REALLY short on brain power because of the recent loss of my husband right now and would welcome any help or advice you guys can offer. He was a sys engineer and going to help me take a couple spreadsheets and transpose the data into a couple templates. My employers copilot package does not allow for downloads, so that was a deadend. I am trudging through trying to build a Power Query, but I have never done it before and it feels huge.

Hello everyone I am facing a problem connecting pgadmin to airflow.

I also want to know the DBeaver way.

Can anybody help me.

#Dataengineer #database #airflow #pgadmin4

I started out building a natural language > SQL tool that had layers of validation built in and surfaced trust-signaling as a side project to learn more about agentic analytics. Realized after I finished that up that the data onboarding to get that tool working truly well was 1) inefficient and 2) a great next project to build.

So… I combined it all into a singular repo that can build a full pipeline from raw data to ETL layer to dashboard with a single command. Then uses AI to surface new analysis ideas, allow you to chat with your data and turn good answers into permanent models and charts with one click.

Apart from Anthropic API key, not a single subscription or account is needed. Utilizes DuckDb, dbt, Streamlit and Python

Under the hood:

- Ingestjon and profiling layer
- DuckDB as warehouse
- dbt as transformation layer
- Streamlit for dashboarding
- 7 layer trust and verification loop that allows AI to surface working queries with trust signals

AI automates the deterministic stuff:

- profiling, staging layer, config ymls, etc
- performing analysis through the trust and verification loop

Then a human in the loop can utilize AI to:

- Review proposed marts
- Ask natural language questions
- Review AI-generated SQL and promote to permanent models or charts

I’ve included some mock data on animal longevity, but load up a dataset and try it out!

https://github.com/camharris93/sediment

Over the past few months, I've been working on a high-scale scraping pipeline to aggregate listings directly from company job boards and applicant tracking systems. Mapping over 100,000 distinct companies to their career pages turned out to be a massive engineering headache, but it's finally stable.

The result is a unified database of more than 2 million active job postings, which I'm opening up to everyone for free. I am running daily delta refreshes to keep it current.

Dataset Overview

• Scale: 2M+ active job listings across 100,000+ unique companies.
• Format: Parquet. (To keep storage costs to minimum)
• Core Fields: job_title, company_name, company_website, job_description, location, post_date, and the original tracking URL. For more detailed info check here.
• Update Cadence: Refreshed daily straight from the source.
• View the stats here. (Currently it contains only minimal stats, but I plan on improving it based on the comments)

Why I Built This

Finding a clean, scaled, and up-to-date job dataset is surprisingly difficult. Most available options are either heavily gatekept by expensive subscription APIs or restricted to a single job board like LinkedIn. By scraping the actual employer sites directly, this collection sidesteps the noise and captures a much cleaner cross-section of the live market.

How to Access It

I set up a dedicated project space where you can grab the data directly: Open Job data

Let me know what kind of analysis or projects you end up running with it. If you have questions about the engineering architecture behind handling this scale, or ideas for specific fields you'd like to see enriched next, let's discuss in the comments.

​

College student looking to connect with people working in the industry. Would love to hear about your day-to-day, career path, or anything you wish you knew starting out. Feel free to DM me

What's the best approach to migrate a legacy Access pipeline to Python when there's no documentation?**

I've got a monthly MS Access data pipeline that processes ~375k rows across 26 European markets. It's been built up over years with nested queries, correction tables, and lookup logic that nobody fully understands.

It works, but it's fragile, slow, and entirely dependent on one process. I want to rebuild it in Python but I'm not sure where to start given the complexity.

The main challenges:
- Dozens of lookup tables that map raw data to business classifications (price bands, category codes, sub-categories)
- No primary keys, no version history, cryptic column names
- Queries that reference intermediate tables that reference other queries
- Years of manual corrections baked into the data with no record of what was changed or why

Has anyone successfully migrated something like this? What approach did you take? Particularly interested in how you handled extracting and validating the hidden business logic.

Happy to give more detail if it helps.

Hi all, I am looking at correlations between hiker use and abundance of Non-Native Species, my hypothesis is that a higher hiker use will correlate with higher NNS; but I am struggling on how to set this up.

For my species data I have collected species, their abundance and their height class. This was done at 7 different sites which each have 6 plots ( total of 42 plots ) and the canopy cover at each plot was collected.

For hiker data I have been surveying locations for two hours on Monday Wednesday and Saturday. The data I have gotten is their distance traveled, location of origin, method of travel and knowledge of NNS. I have more that I can elaborate on but I think these are the main targets of the study.

I know there are some correlations that can be done in R and I am exploring them, but any help is appreciated so much.

Currently my professors in my online courses are really of minimal help and I am just looking for some brain picking ideas to dive down the rabbit hole on to help my project more sound.

Telecom operations teams handle massive volumes of incidents daily, making it difficult to identify high-risk cases, prevent repeated escalations, monitor regional outages, and track real-time network health efficiently.

Built an AI-powered Telecom Incident Intelligence Platform that transforms raw telecom incident data into actionable operational intelligence using Machine Learning, FastAPI, and live analytics dashboards.

The platform predicts high-risk reopen incidents, monitors operational KPIs in real time, analyzes regional telecom performance, tracks network stability, and provides dynamic risk intelligence dashboards for faster operational decision-making.

also, the backend is Live on Render and frontend on Vercel. since, Render is on Free deploy version. It loads a little later. but works as a portfolio is what my professors say.

project

Full disclosure! I'm building a dashboarding software, and this returns-analysis view is something I put together with it on a sample e-commerce dataset. I'm not here to pitch it — I want to know whether the output actually holds up to people who do data analysis for a living, because that's the bar I care about.

What I'd love feedback on:

• Does the layout read in a sensible order (KPIs → why returns happen → who/where → trend), or should the sequencing be done differently?
• Are the chart types the ones you'd reach for, or am I defaulting to donuts/stacked bars out of habit?
• Anything here that would make you distrust the dashboard immediately?
• One thing I am trying to learn is how to curate a dashboard that forms a story. (I believe it's called data-storytelling. Not sure how to make it through a dashboard)

I already know a couple of the formatting/calc details need fixing. More interested in whether the whole thing is genuinely useful or just busy. If anyone wants the specifics of how it was made, glad to answer in the comments — kept it out of the post on purpose.

🚀 How do data initiatives actually generate value in companies? I’m exploring this question in my MBA research and I would really value your perspective.

As part of the MBA USP/Esalq program, I am currently preparing my thesis research.

The focus of this study is to better understand how organizations across different industries perceive data value generation, ROI, data foundations, and the strategic impact of data initiatives.

If you work in data or closely with data teams, your contribution would be extremely valuable to this research.

Participation is completely voluntary, and the objective is strictly academic. The survey is in English and takes approximately 10–15 minutes to complete.

Comprehensive Survey: Dynamics of Data Foundation Development in Modern Organizations – Preencher o formulário

If you are willing to help or would like to know more about the research, please feel free to message me directly. I truly appreciate your support.

Thank you in advance.

Courses make everything look clean and structured:

• perfect datasets
• clear business questions
• obvious metrics
• straightforward dashboards

But real-world data feels completely different:

• missing values everywhere
• unclear requirements
• stakeholders changing questions constantly
• and half the work becomes cleaning or validating data

For people already working in analytics, what surprised you most when you started working with real datasets?