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?

In today's AI world Data becomes important asset but the main problem with that data is messy, unstructured not easy to understand - especially handling missing values and duplicates across specific columns.

I built a free simple tool for this. Would love feedback from people who do this regularly.”

https://dataspa.streamlit.app/

this is my first proper dashboard that i built for my data analytics portfolio

i'd really appreciate any criticism or suggestion on where i can improve, i am still learning

Hey everyone,

I've been learning data analysis for about a month now, and this is my first proper portfolio project.

It's a supermarket sales dashboard built entirely in Excel using a Kaggle dataset. The dataset isn't perfect, but I wanted to use it as a starting point to practice cleaning data, building KPIs, PivotTables, slicers and visualizing the results.

I'd really appreciate some feedback on it:

• Does the dashboard answer useful business questions?
• Are any of the charts or KPIs misleading/unnecessary?
• What would you improve about the layout or design?
• What analysis would you add to make this a stronger portfolio project?

It took me about a week to finish, which felt a bit long, but I'm still new so I'm assuming I'll get faster with practice.

I'm learning SQL now and planning to move into Power BI next, so eventually I'd like to build something similar using SQL + BI.

Hi everyone!

I’m back with another one of my data analysis cat doodles. This time, I tried to incorporate some of the feedback I received on my earlier work—especially around making the fonts and text more readable.

Would be happy to hear your thoughts / feedback!

I'm curious about the actual workflow people use for day-to-day data analysis.

For example, when you receive a messy CSV or Excel file, what's your usual process?

Do you typically:

• Clean everything with Python/Pandas
• Use SQL first and then Python
• Use Excel/Power Query for cleaning
• Move straight into a BI tool
• Use a combination of several tools

And at what point do you decide that a dataset has become too large or complicated for Excel?

I'm especially interested in workflows where you need to repeatedly clean, transform, analyze, and visualize similar datasets.

What does your typical workflow look like from raw data → cleaned data → analysis → visualization/report?

hey friends i hope you are all doing well i just have a concern in my journey of learning data analytics the worst obstacle for me is building a dashboard in power bi i know this might be strange that i did not struggle that much with sql or dax and excel but this is really hard for me so can any of you guide me on how to overcome this crisis by the way i am fine when it comes to data cleaning and modelling it is just that i cant build the final view of the dashboard

I have a table where policy numbers are rows and months (like apr-25 to jul-26) are columns containing revenue data 25 and 26 are years btw. Some months are blank because there is no revenue. I want to group these monthly columns into a single date format for Power BI. When I use the unpivot method, it creates duplicate rows and adds incorrect data where it shouldn't be. I want to be able to use the months just like a filter in excel 2025 and its months and 2026 and its month.

Hi everyone!

I recently completed an end-to-end Data Analytics project on Customer Shopping Behaviour.

The goal of this project was to analyze customer purchasing patterns, identify important trends, and create meaningful business insights from the data.

🔧 Tools & Technologies:

• Python – Pandas, NumPy, Matplotlib

• Jupyter Notebook – Data Cleaning & EDA

• PostgreSQL – Data Storage & SQL Analysis

• Power BI – Interactive Dashboard

• GitHub – Project Documentation

📊 Project Workflow:

1. Loaded and explored the dataset using Python

2. Cleaned and prepared the data

3. Performed Exploratory Data Analysis (EDA)

4. Stored the cleaned data in PostgreSQL

5. Wrote SQL queries to analyze customer behaviour

6. Built an interactive Power BI dashboard

7. Generated insights and prepared a project report

📈 Some areas analyzed:

• Customer purchasing behaviour

• Product/category performance

• Customer demographics

• Purchase patterns

• Ratings and sales trends

I’m sharing this project to get feedback and improve my data analytics skills.

GitHub: [https://github.com/nniteshbarik9-lab/customers_behavior_analysis-\]

I would really appreciate your feedback and suggestions. Thank you!

What it does:

A lightweight app that takes any CSV, automates data profiling/descriptive stats, renders multi-chart visualizations (Seaborn/Matplotlib), and uses Groq (llama-3.3-70b) to explain findings and answer ad-hoc questions in plain English.

Why I built it:

Built this for the AI Data Analysis Challenge (Track A) to explore integrating fast LLM inference with automated exploratory data analysis (EDA).

Tech Stack:

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Groq API (llama-3.3-70b-versatile)
• Streamlit UI

Key Learnings:

• Dynamically adapting charts based on detected data types.
• Passing structured dataset summaries to LLMs for grounded answers without hallucination.

GitHub: https://github.com/binarylaiba/AI-Data-Analysis-Asistant
Live demo: https://ai-data-analysis-asistant.streamlit.app/

Would love feedback on how to improve the charting logic or prompt design!

From French Literature to Data Analytics. I have spent one year to learn Data Analysis tools and digging about the role's works. Even though i saw so much people trying to say the entry level sucks and it is almost impossible for a career switcher to get into DA, it does not make me any less want to get DA job! Finally i am confident to build my first project.

What I did in this project:

- Cleaned and processed transaction records in Python

- Built and validated RFM logic in both Python and SQL

- Segmented customers into six groups (Champions, Loyal Customers, At Risk, Needs Attention, New Customers, Lost)

- Designed an interactive dashboard in Tableau Public

I am worry a bit since i needed help from claude to make a few descriptions & vocabs because i still have to learn more about DA job & Business terminologies.

Will really appreciate to have any feedback! Have a good day.

do you recommend me to watch it or there are better sources

i have mysql workbench & have been practicing it on my own. the problem i've run into is low disk storage. my fuckass c drive is running on 900 mb ✌️😂 i don't have a lot of applications installed, so removing or moving them to another disk isn't an option.

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

Hi

If you are remotely interested in deep diving how differently quantum computers work compared to our transistor-based and also the algebra behind in a fully interactive way that teach computer science from scratch, oh boy this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 10 years (3+ during PhD, the visual method I developed ended up being my thesis, it is a complete Hilbert space visualizer), the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 12yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.

This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.

Stuff you'll play & learn a ton about

• Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
• Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
• Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
• Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
• Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.
• Build & See Quantum Algorithms in Action – instead of just writing/ reading equations, make & watch algorithms unfold step by step so they become clear, visual, and unforgettable. Quantum Odyssey is built to grow into a full universal quantum computing learning platform. If a universal quantum computer can do it, we aim to bring it into the game, so your quantum journey never ends.

Nice to watch:

Khan academy style tutorials in qm/qc: https://www.youtube.com/@MackAttackx

Physics teacher stream with 400hs in https://www.twitch.tv/beardhero

When working with a dataset, there can be many variables that might seem useful.

How do you decide which ones are actually worth including in your analysis?

Do you mainly look at the business question, correlation, data quality, or something else?

I'd like to hear how experienced analysts approach this in real projects.

I'm trying to build a data analysis portfolio to strengthen my candidacy for more data-focused roles. I put together this analysis on great ape habitat loss, and I'm seeking feedback on whether this is a strong contribution to my portfolio and what could be improved. Here is the link:

https://019fd814-974a-9d9c-98af-ba485ec47718.share.connect.posit.cloud/

Still cleaning up the repo and documentation, but wanted to share here for some early feedback. The analysis examines 25 years of satellite-derived forest loss data in primary great ape range countries and attempts to demonstrate whether protected areas are associated with reduced loss.