When done you can download the underlying code, which can then later be imported again. Link: https://vibe-schema.com/star-schema-creator

Hello everyone very happy to share my new course on stastics for data science in complete bangla you can find it on my youtube channel.

https://youtu.be/zjuE766pqbY?si=A8F7a6bWpbHEmZ87

I work in healthcare.

The company I work for is looking at productivity and I am a People Manager.

I currently have a staff of 9 people who essentially make phone calls for most of the day.

Data and Maths people:

What would you feel would be the best representation for Productivity, in a formula form.

I have some numbers.

x would be completed calls. (any call that my staff gets through to the people for any specific time.)

y would be attempted calls. (any call that my staff makes but does a voice mail, etc.)

The company is looking for a productivity number of 120 per month.

I have higher productive people and lower productive people... but I need to evaluate them.

It is not as simple as "you must make 120 completed calls per month." My people have meetings, attempts, education, etc.

Any thoughts?

I'm working on a few Data Analytics projects and noticed that some projects are much more interesting than others.

What do you think makes a project good the dataset, the business problem, the analysis, the insights, or the dashboard?

Curious to know what others focus on when choosing a project

I'm looking to get into data analytics and I'm wondering what learning route I should I take. I was thinking SQL >>> Power BI >>> R and then I don't know after that.

To put this in context, I'm currently brushing up on Excel. That's the level of beginner we're talking.

TIA.

Hi everyone! I’m a beginner in Data Science and learn best by building projects.

I’m looking for GitHub repos, YouTube playlists, or websites with project-based learning from beginner to advanced level, ideally covering different ML models such as regression, classification, clustering, tree-based models, boosting, NLP, time series, etc.

I’d also love end-to-end projects that include data cleaning, EDA, feature engineering, model building, evaluation, and deployment.

Any recommendations?

Hi there,

I've created a video here where I explain how generalized linear models work.

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

i have mysql workbench & have been practicing it on my own. the problem i've run into is low disk storage. i currently have 4.5 gb on my c drive, which i don't think is a lot. i don't have a lot of applications installed, so removing or moving them to another disk isn't an option. neither is spending money on storage 💔

im worried about the rest of my learning journey. i know i'll eventually have to install other programs/tools & it makes me sad that low storage space is what might hold me back from learning something im genuinely interested in.

i wanted to ask if there are online versions of these softwares available? im talking about python, tableau & all other stuff i'll need later on. i've used an online c++ compiler before, so im wondering if it's possible for other tools too. and if so, can they save all my previous data? what about something with an account where it syncs data to a cloud? HALP

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