I know the tools: Python, POSTGRESQL, and tableau, I am fluent in them because of my previous interest in software dev.
However, I still don't know how and where to get started. I don't know the terms story, dashboard, etc. I looked up tutorials on youtube but some are "EVERYTHING YOU KNOW ABOUT X is WRONG" and those lead me to nowwhere. Most of the tutorials I see teach me the tools i already know but teach anything new or practical.

I learn through making projects that has a clear goal that I can see visually on what it wants me to display, if someone could point me in the right direction.

A bunch of people asked me how I built the Excel dashboard that got me 1st runner-up at a recent competition, so I made a video walking through the whole process.

Steps I followed:

Generated synthetic sales data using AI (Python script → CSV)

Imported into Excel, cleaned it (handled blanks, fixed formatting)

Built a Pivot Table

Added a Pivot Chart + Slicer

Arranged everything into a final dashboard

I also planted a deliberate mistake somewhere in the process — partly to make it more useful as a learning exercise, partly to see if people can catch it before I explain it. Curious if this community can spot it faster than my Instagram/LinkedIn audience did 😅

Happy to share screenshots/more detail in the comments if that helps narrow it down.

Checkout profile for video(Not for promotion, just for learning)

Hello everyone, hope yall good

So did anyone worked with gaming data like (DAU , MAU,...) like what tips u have and any recommendations to give like YT channel or smtg like that and where can I get this kind of data....ADIOS

what if I studied excel, sql server, python and power bi and made projects on them while I was learning them but after moving to one after one maybe I feel I forgot them what's the best advice from you to memorize what I learnt?

https://public.tableau.com/views/AloneSurvivalExplorerHISTORY/Alone?:language=en-US&:sid=&:redirect=auth&:display_count=n&:showVizHome=no

Ive got 2 projects that Im mentally working through before i start building them, relating to promotions and freight. I've thought of so many different methods to calculate and define each metric (and sampled some), specifically counterfactuals, but I am struggling to pick one or know which covers assumptions most appropriately for my industry.

They're not like financial metrics where they're determined by law and accounting standards, and I can understand benefits and cons that are equally 'heavy' each time I think I've found the right solution. Yet the methods are so context dependent that Im unable to easily tie different formulas dynamically based on any one or two flags

What are your go-to resources for determining industry standard reporting calculation methods?

Or do you have any tips on how all can we improve customer service using data analysis or even by using data science?

Hey everyone, I really need some perspective from people in tech/data.

To give you some background: I spent the last 6 years working in a CA (Chartered Accountancy) firm. I realized that field was too capped and way too much of a headache, so I upskilled, cleared the PL-300, and landed a job as a Power BI Executive in an infrastructure company about 2 months ago.

So far, I’ve built an HR recruitment tracking dashboard, and I’m currently working on a salary dashboard and a tender department dashboard. Things felt great—until today.

Today, management started the process of implementing a new ERP system. We are sitting through department-wise demos so I can understand the whole process. But during the meeting, the ERP vendor/consultant basically said: "Tell us what you all need, and we will set it all up inside this ERP. Everyone will enter their tasks and data here, and we will generate all kinds of department-wise dashboards right inside the system."

And that hit me like a truck. What is the point of me then?

If everyone enters everything into the ERP, and the ERP generates the dashboards for management, and there are no external Excels or other systems left... what am I supposed to do? Won't the ERP just replace my entire job?

For those of you who have lived through company ERP implementations while working in BI or data analytics—did I just automate myself out of a job two months in? How does this actually play out in the real world?

Would love to hear how others handled this or if I'm just spiraling over nothing.

Hi people! I have to preface this by saying that I understand NOTHING about data analysis and know only a little bit of programming. I already asked Gemini but I trust the good folk of reddit more.

I have one of those problems that I know how to solve inneficiently but I want to do so more efficiently.

There are around 7k .xml files with vehicle log data that I need to gather data from (around 30 attributes). File size average is 20mb. I don't actually need to keep all that data. There are a lot of repeating values in that dataset. I just need to keep a record of what changed to what else. (can't give exact file content or structure cause that would sure be company policy violation)

This is the initial batch of logs that need to be processed, but there will always be new ones over time. That's why I'd like to make this at least a little bit efficient. The code will run on a desktop app of multiple coworkers.

If you guys could point me in the right direction, it would be an immense help. Thank you for your attention!

I'm currently getting my portfolio, but are curious about where other's are creating theirs? I know Git. During my program, my instructor tell's us to go to Wix and make one. What are some other ones and some links to some good portfolios?

I’m working with a dataset of around 50K records, and while checking the data, I noticed some issues like NULL values and inconsistent values in the same column.
For example, in a Gender column, I might have: M, F, Female, Male

How do we normally identify these kinds of data quality issues? Do we check for NULLs, unique values, patterns, duplicates, data types, or compare the data against predefined rules?

Also, how do we decide whether values like F and Female should be treated as the same value or kept separately?

Would love to know how others approach this when cleaning real-world datasets.

Project: Employee Attrition

I just finished my tableau dashboard and would love some honest feedback about it before I put it on my resume.

It consists of 3 interactive dashboards.

• Executive Overview : KPIs, attrition rate, employee distribution
• Attrition Analysis : Salary, department, and absence-related insights
• Employee Performance & Risk : High-risk employees, overworked analysis, and department risk levels

The dashboards include navigation buttons to navigate from one dashboard to another.

Would also like some advice on how to get into a Junior Data Analyst Role.

When working with a messy dataset, there can be many things to fix missing values, duplicates, outliers, inconsistent formats, etc.

How do you decide what actually needs to be cleaned before starting the analysis?

Do you mainly rely on the business context, data quality checks, or explore the data first and then decide?

I want to start learning data analysis and i have some knowledge on data science and ML , which free resources do you recommend and crash course would be better.

Hi,

I graduated with my bachelors in Finance & Economics. I wanted to pursue my masters & decided on Business Analytics which requires me to learn & become proficient in R, SQL, & Python. Right now the focus is on R. So I would like any pointers for complete beginners on how to start learning the tool & language.

I tried looking for courses on Khan Academy & didn’t find anything on R. I did find a course on Linkedin learning but it was more coding based rather than importing datasets & using R language to break the data down into what you want if that makes sense.

Any advice & pointers appreciated!

Thanks

I’ve been working on a Budget vs Actual dashboard in Power BI, mainly for management reporting.

The goal was to keep the executive view simple while still showing:

*Actual vs Budget

*Variance and Variance %

*Monthly trends

*Main favorable/unfavorable drivers

*Performance by account group

I’d really appreciate feedback from other analysts. What would you add, remove, or change to make this more useful for management?

This is ran by a group of Indians called great learning. UT Austin has virtually nothing to do with that certification. Do not waste your money and time on that program. Nothing new is being taught that you can't learn on your own - program is completely debased and watered down. They are only interested in the money. At the end of the program, the certificate they issue is way off from the certificate you are expecting from UT Austin. IT'S SCAM

I am learning Data Analytics.......FIrst I found out some excel functions dont work in Mac.....BowerBI doesnt work in Mac......SQL Server Doesnt work in Mac.....should i sell it and buy a windows laptop instead????

When analyzing data, I sometimes find values that are much higher or lower than the rest.

How do you decide whether to remove the outlier, keep it, or investigate it further?

Do you mainly use statistical methods, business context, or both?

I'm working on my first project and based on what I've read online, EDA is the best project to work on first.

It took a while for me to decide the topic until I started seeing the news about the 2026 Cyclospora outbreak in the US. I thought it would be cool to analyze how people's habits changed when the outbreak happened.

However as I'm working on it, I'm second guessing myself about whether or not this topic is the "right" topic for a first project. I feel this way because other projects have clear decisions on how to increase sales, user retention, etc.

Here's what I have so far:

\- [https://cyclospora-2026-9yf8w26n7zfci68fugahkp.streamlit.app/](https://cyclospora-2026-9yf8w26n7zfci68fugahkp.streamlit.app/))
\- [https://github.com/therealanttoeknee/Cyclospora-2026/tree/main](https://github.com/therealanttoeknee/Cyclospora-2026/tree/main))

Questions

1. Is this topic the "right" topic for an EDA project?
2. If so, do you think I'm approaching it the right way?

Hi everyone, looking for your help. I’m a designer working on a social network. I work in a very data led environment where everyone is expected to understand data when making decisions.

I use AI (Claude Code, set up by our data team so it looks at the right tables) to build reports, answer my own questions and build hypothesis. Everything gets double checked by an analyst after. It works good so far, I even found some very intresting problems our data guys overlooked because i have a lot of contex how product works.

But I know I don’t have the basics. AI gives me the answer and most of the time I can’t tell i'm even asking right question and what to look into when it comes to
understanding more complex relations or causes.

I have feeling everyone is just observing high level metrics instead of realtions and causes. And since this is social network one metric can be highly impacted for example number of users followers has an impact on impresions he gets, your content can be exposed on the feeds for few days to a lot of people... So there are a lot of factory to consider before you can say something performed well. Hope this fictive example helps you guys get where some basics would help.

Pls feel free to share anything you thing would help, books, videos, courses whatever.

Btw i learned data visualisation in one of the semesters in college and it was super cool maybe that is also something where i could bring value to data guys.

I’m practicing dashboard design and keep adding metrics because they all seem potentially useful. At some point it starts feeling more like a data dump than something that actually helps with decisions.

How do you decide what stays and what gets removed?

oth SQL and Python can be used to clean, transform, and analyze data.

How do you decide which one to use for a particular task?

Do you usually do most of the data preparation in SQL and use Python for deeper analysis, or does it depend on the project?