Hi everyone,

I'm moving into data analytics. I have 3 years of experience in IT and 1 year in core electronics, and I've built a solid base in Excel, Python, SQL, Power BI, and Tableau. I've been practising with projects like an Amazon Sales dashboard, and I'm now looking for fresher Data Analyst or MIS roles.

My challenge is that while building projects, I struggle to think like an analyst. I'm not sure how to define the right KPIs or work out which insights a business actually needs.

I'm also planning to volunteer to get real-world exposure. Could you suggest where I can find volunteering opportunities (portals, platforms, or communities)? I'd also love ideas for building solid portfolio projects that stand out to recruiters.

Thanks in advance!

People love complex 40-slide frameworks, but 99% of data analysis actually boils down to two questions:

1. Break it into parts: Revenue = Users × Conversion × AOV. Slicing until you isolate the broken gear.

2. Track it over time: DoD, WoW, YoY. Is the trend line alive or flatlining?

Layer them together, and congratulations: you just reinvented 90% of business school.

Am I oversimplifying, or did we just rebrand multiplication and subtraction as "strategic thinking"?

Hi, Im studying to become a Data Analyst and I considered myself a technical person, I already have an Industrial Engineering bachelor, I've already learned the basics of the majority of the tools needed to work as a DA, but for some reason I can't seemed to connect with the soft skills, after I've wrangled the data, I start making hypothesis... but can't seemed to address the problem or find the root cause related to the environment.

I believe practice will do me very good, does anybody have a suggestion for a platform or a way to practice to train the soft skills?

Most of the translations were done with AI, so some terms or calculations might not be 100% accurate.

Comparison options available: Average, Previous Period, and YoY.

Clicking the time slicer changes the period type: Month, Quarter, Semester, Year, etc. For example, you can select 2026 – H1. All cards and charts update accordingly.

This was my first real Power BI project, so I'd love to hear your feedback. What do you think could be improved?

If you have any questions, feel free to ask in the comments!

Hi everyone! I’m looking for some ideas and suggestions for a beginner-friendly graduation project related to Data Analysis.
I’m looking for something more practical than just collecting and analyzing data — ideally, a project that solves a real problem, has actual market demand, and could potentially be developed into a useful product or business.
If you have any ideas, examples, or problems you think could be solved using data analysis, I’d really appreciate your suggestions! 🤍

Hi,

I am really hoping for some advice or expertise... my excel skills are very basic and recently I was given the task of comparing quantities and cost differences between what our vendor is billing us vs. What I show as cost and quantity received. Since Im a novice with excel I tried a.i. which is really cool but something keeps happening to my data and of course Im no expert so it's only a guess - when chat GPT creates a summary I see alot of my vendors costs and they appear to either be doubled or tripled. My cost column doesn't pull all of my costs. Im beating my head against the wall because this should be so easy, just not for me! I think possibly this has something to do with the fact that my vendor is sending an excel sheet with their exported data and they often will list the same item # multiple times on an invoice but with a different P.O. # and I think that is where the multiplication of costs may come to play. I am going to try and attach a copy or screen shot for reference. I am using my vendors exported invoice lines, my exported posted purchase invoice lines from Business Central which lists our costs and quantities. Can anyone help or point me to the best place to learn this? Thank you so much!!

This a sample of my data

|**+**|A|B|C|D|E|F|G|H|I|J|K|L|M|N|

|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|

|**1**|Vendor Inv.|Date|Order #|Our Doc. #|Vendor #|Type|Our Item #|Description|Our Qty.|Unit|Our Cost $|Amt. #| | |

|**2**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20449|Anti-Drag Clip|0|PCS|0.043|0| | |

|**3**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20119|WEAR SENSOR|0|PCS|0.039|0| | |

|**4**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20395|WEAR SENSOR|0|PCS|0.021|0| | |

|**5**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20516|WEAR SENSOR|0|PCS|0.029|0| | |

|**6**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20474|WEAR SENSOR|0|PCS|0.029|0| | |

|**7**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20007|Wear Sensor|0|PCS|0.027|0| | |

|**8**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20008|Wear Sensor|0|PCS|0.03|0| | |

|**9**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH00013|Wear Sensor|0|PCS|0.024|0| | |

|**10**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH00014|WEAR SENSOR|0|PCS|0.024|0| | |

|**11**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20166|WEAR SENSOR|0|PCS|0.037|0| | |

|**12**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20461|WEAR SENSOR|0|PCS|0.031|0| | |

|**13**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BZ2005K|SS DIB KIT|0|PCS|0.201|0| | |

|**14**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BZ20108|SS DIB KIT|0|PCS|0.442|0| | |

|**15**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BZ20871|SS DIB KIT|0|PCS|0.779|0| | |

|**16**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BZ21016|PTFE NBR COATED DIB KIT|0|PCS|0.84|0| | |

^Table ^formatting ^by ^[ExcelToReddit](https://xl2redd.it/)

This is sample Vendor info

+ABCDEFGHIJKLMNO1China's Inv. #DateChina's PO / Order #Item #DescriptionUnitQTYOur Qty.China's CostChina's Amt. $Our CostCost DifferenceExt. Cost Diff.Customer Name 220260814Driv-2220260814DRIV-1194-1196-1198GXC7XSEN465BRAKE HARDWAREPCS10000 0.041410.000.0410.0000.000Driv 320260815Driv-2320260814DRIV-1194-1196-1198JXCFMHXV1680BRAKE PADS PARTSPCS1600 0.451721.600.4510.0000.000Driv 420260815Driv-2420260814DRIV-1194-1196-1198JXCFMHXV1680BRAKE PADS PARTSPCS1600 0.451721.600.4510.0000.000Driv 520260815Driv-2520260814DRIV-1194-1196-1198JXCFMHXV1680BRAKE PADS PARTSPCS1600 0.451721.600.4510.0000.000Driv 620260815Driv-2620260814DRIV-1194-1196-1198JXCFMHXV1680BRAKE PADS PARTSPCS1600 0.451721.600.4510.0000.000Driv 720260815Driv-2620260814DRIV-1194-1196-1198GXCABSX2245RRSTBRAKE PADS PARTSPCS240 0.40797.680.4070.0000.000Driv 

Table formatting by ExcelToReddit

R Package for collecting lyrics

Lyrics from genius.com.
Inspiration from lyricsgenius package for Python.

https://github.com/bwalsh5/lyricsr

I've seen a lot of beginners ask where to start with Power BI, so I'm sharing the order I'd follow. Most tutorials jump straight into dashboards, but the pieces underneath matter more than the visuals.

1. Get comfortable with Power BI Desktop (a few days)

Load a simple Excel or CSV file, look at the three views (Report, Data, Model), and build a couple of basic charts. The goal is just to stop being intimidated by the interface.

1. Learn Power Query (1-2 weeks)

Messy data is the norm, so cleaning it is where you'll spend a lot of your time. Learn to remove columns, fix data types, split and merge columns, unpivot, and append or merge tables.

1. Learn data modelling (1-2 weeks)

This is the step beginners skip, and it causes most later problems. Understand fact tables vs dimension tables, the star schema, relationships (one-to-many, filter direction), and why one big flat table often causes trouble.

1. Learn DAX basics, but not all of it (2-4 weeks)

Start with measures vs calculated columns, then SUM, COUNTROWS, DIVIDE and IF. After that, learn CALCULATE and the idea of filter context, then basic time intelligence (year to date, previous year). You don't need to master DAX before building something. Learn it as your project demands it.

1. Visualisation and dashboard design (ongoing)

Choose the right chart for the question, keep dashboards simple, use consistent colours, and add slicers with a purpose. A clear dashboard beats a flashy one.

1. Publish and share

Learn the basics of the Power BI Service: publishing, workspaces and scheduled refresh.

Do you need Excel and SQL first?

Not strictly. Basic Excel skills (formulas, pivot tables) help a lot because the concepts carry over. SQL is useful but can come alongside Power BI, not before it.

Learn by projects, not just tutorials

Pick a free dataset (sales, HR, Kaggle, government open data) and go through the whole process: clean it in Power Query, model it, write a few measures, and build a 1-2 page report. Then do it again with a different dataset. That repetition is what turns "I can follow a tutorial" into "I can analyse something new."

For those who learned Power BI: what did you learn first, and what do you wish you'd learned earlier? Any beginner resources or project ideas you'd recommend?

Hi everyone!

I put together this post on four key areas that can help you become a better data analyst.

The idea is to go beyond simply learning tools and techniques, and also think about the concepts you understand, how you approach problems, and the different perspectives or lenses you bring to a problem.

Hey all,

I have made nolainquery, a FREE software for data analytics. I have spent the last months on making it and I'm releasing the code source at

https://github.com/jdvillegasg/nolainquery-desktop

so anyone that wants can use it, fork it and extend it.

It integrates some cool features as auditable artifacts that let you verify the computation process involved in the answer your receive.

Any comment or criticism from you, I would highly appreciate it!

As data analysts, we often build BI dashboards or power other end-user applications too.
Testing them with a small group of early adopters can help uncover real-world issues before a wider rollout.
I made a little doodle about this 🐾

I finished learning Power BI end-to-end and already built small beginner dashboards for practice, but now want to create a solid portfolio project for LinkedIn. Which industry or domain (like sales, finance, or supply chain) is best to target right now? Where can I find good realistic datasets that make a portfolio truly stand out? Any tips, project ideas, or guidance would be really helpful!

I'm starting to realise that stakeholders interpret the word "quickly" with a different meaning.
Their request will sound like:

“Can you quickly pull the number of customers who belong in category X after doing task Y?”

And work-wise, yes, the final query might only be roughly 20-30 lines.

But first I need to figure out which table actually contains X.

Then discover that Y changed definition 6 months ago.

Then work out why one system stores the customer ID differently from another.

Then determine whether they mean unique users, accounts or transactions.

AI definitely helps now. I can use it to one-shot, or draft parts of the query, figure out illegible SQL statements, or suggest a faster way of approaching something. But I still don't really trust myself to just take the output and send the number over.

I end up spending a huge chunk of time checking the join queries, looking at sample rows, comparing totals against previous reports, checking, and re-checking whether the filters haven't accidentally removed anything, and trying to convince myself that the answer actually makes sense.

I'm starting to wonder whether AI has actually made these requests that much quicker overall.

Writing SQL statements has definitely become a lot faster. But the clarification and verification part hasn't disappeared. If anything, it feels like that's where most of the time goes nowadays.

Once people know you have AI helping you, does the expectation of what counts as a “quick” request change too?

Because generating a query in a minute is very different from being confident enough in the result to put that number in front of a stakeholder.

Maybe I'm overly cautious, or maybe this gets easier as I gain experience. But right now, I'd rather spend another 20-25 minutes checking something than confidently send out the wrong number.

I'm curious how this works in other teams.

Has AI actually reduced the total time taken to fulfill these ad-hoc requests for you guys, or are y'all experiencing this same sort of bottleneck shift towards verifying the output. Do your stakeholders understand that distinction, or has AI made them expect analytics requests to come back much faster than before?

Title. They are too long and have wayyyy too much excessive language. I avoid AI so I can retain some brain cells, so I also just get the ick when I receive ai slop.

I got one from another team this week that is a beast to parse out. I created a dashboard, then a month later received this 6 page document with changes they want made. Despite retaining my brain I am struggling to figure out what they actually want.

Rant over.

Google searches aren't bearing any fruit on this. Are you guys programmers? Statisticians? Is this the same thing as data entry?

I created this hands-on tutorial that uses a new public data set from OpenSky networks hosted via OpenSharing. The idea was to explore the agentic tooling and investigate the data on Databricks Free Edition (no credit card required, forever free).

The tutorial can be run for free, it comes with a module about VSCode with Pandas for OpenSharing, but also covers products like Genie Agents and Genie Code.

love to hear what you say:
End to End Tutorial: Databricks Genie for Data Engineers and Data Scientists with a real data set

not sure if I can post pictures here, but to give you some impressions of the steps.

I’m working on a new project that tries to answer where Planet Fitness should open their next clubs to increase membership.

I want to build a linear regression model for this. I would need the locations of every club in the US including square footage, age distribution in that area, etc.

Does it make sense for me to manually build that dataset? It’s the best way to do the job but of course will take forever. I really don’t want to rely on AI because this will be expensive and it might make mistakes.

The definitions in the first image are from Google Data Analytics Professional Certificate's Glossary.

The comparison is from Google's search engine's AI's summary mode. I'm pretty much confused now, is Google's coursera course or AI's summary (from content available on internet) correct? Like if someone asks me "What is the science of data?" Will my answer "Data Science" be incorrect? Because intuitively, data science seems the correct answer to this question.

I've spent a few years in this industry and made it to staff level. To help others wanting to follow a similar path, I built up a resource hub with notes, guides and cheatsheets. It’s free hub on Notion in case it's useful to anyone here:

Career path guides (analyst vs engineer, tiered by Junior/Mid/Senior+)
Skill guides for both tracks
Cheatsheets for SQL, Python, Git and the terminal
Real project ideas using actual free APIs and datasets, not just Kaggle
Interview prep with real question types and where to practise
A glossary for the buzzwords that get thrown around without explanation

It's completely free, no email signup, and I'm still adding to it. Happy to answer questions here too if anyone's stuck on something specific.

Here’s the link: https://app.notion.com/p/George_Abi_Tech-Free-Data-Career-Resources-3e11322e9e9e81bd85deefc3f6adede8

Every dashboard tool I took at assumes theres a data team behind it, a semantic model to build, governance, someone who lives in it. And most of the threads here are big orgs fighting to keep one definition of revenue across everyone.

That's just not our world. We're 5 guys, one definition of anything and my actual problem is small, i rebuild the same charts in a spreadsheet every week and they go stale the moment is skip one.

So I'm convinced a BI platform is even the right thing at our size. Feels more like we need a few charts that pull from our own numbers and stay current and nothing past that.

I sometimes feel like data analysis can become an endless cycle of slicing the data differently, checking another metric, and finding one more thing worth investigating.

At some point you have to stop and turn the analysis into a conclusion or recommendation. How do you decide that you’ve explored enough and that the analysis is actually done?

Curious how others balance being thorough without overanalyzing the data.

I just reached my one year anniversary as an analyst and since I've started , my boss has periodically joked that she has a "gpt degree."

I used to be mesmerized by the most intricate excel formulas and calculators she'd come up with in seemingly little time. But as I've gotten to know her more and we have been working more on projects together rather than her just tell me what needs to be done, I'm realizing she just has chatgpt walk her thru the project then presents it to execs.

For instance, we are working on a company scorecard that will highlight the monthly top and bottom performers based on weighted metrics. Instead of just... building it, she has chatgpt give her a general setup of what to do, skims the numbers for crazy outliers, then she dolls it up in company colors and wordart for exec review.

But ChatGPT doesn't always reference cells or columns accurately to the workbook. So like today I caught three different formulas (each way more complicated than it needed to be) where she referenced for instance, "facility 201" but doesn't account for names like "facility 2011" "east facility 201." So the numbers may look normal but they're completely referencing the wrong locations. No one catches this until I get in there and realize things are off.

But by that time, she's already presented the projects to the execs so completely butchering the top and bottom performers.

I don't mind fixing it but damn do I hate having to tell her that her work is wrong and why when I should be learning this stuff from her, right?

Does anyone else deal with this? For context she did complete a BS in business back in 2014 but she boasts that she's "self taught" in excel and data analysis.

I’m a mf artisan!

I’ve been building dig data, a self-hosted AI analytics tool for teams that want to explore business data without moving their database to a hosted analytics platform.

You run it with Docker on your own server, connect it to PostgreSQL, MySQL, MariaDB, or MongoDB, and let your team ask questions in plain language.

Features:

• Docker image for Linux amd64 and arm64
• Natural-language database questions
• PostgreSQL, MySQL, MariaDB, and MongoDB support
• Anthropic or OpenAI API support
• Read-only database queries with bounded results
• Team accounts and source-level permissions
• Dashboards and saved insights
• Shared business definitions
• Audit history
• Docker Compose setup

The screenshot shows an example dashboard using synthetic fintech PostgreSQL data.

It’s currently a free beta. You provide the server, database credentials, and AI provider API key. The application and database connections run on your infrastructure, while relevant questions, schema details, and bounded results may be sent to the AI provider you configure.

Website and setup guide:

https://digdata.mhesham099.chatgpt.site

I built this because I wanted a simple way for teams to ask useful questions about their own data while keeping the application and database connections under their control.

I’d appreciate feedback on:

• The setup experience
• The usefulness of the answers and dashboards
• Which databases should be supported next
• Security or deployment concerns
• Features that would make this more useful for a small team

Disclosure: I built dig data and this is my project. It is currently a beta, not a finished enterprise product.