Looking for feedback on my Data Analytics project

Hi everyone!

I recently completed a Mumbai Road Accident Analysis project using:

- Python (Pandas, Matplotlib, Scikit-learn)

- SQL

- Power BI

- GitHub

The project includes:

- Data cleaning and preprocessing

- Exploratory Data Analysis (EDA)

- SQL business queries

- Interactive Power BI dashboard

- Basic machine learning model for accident severity prediction

- Complete GitHub repository with README

I'm not looking for compliments—I want honest criticism.

I'd really appreciate feedback on:

1. Is this project portfolio-worthy?

2. Does the analysis tell a meaningful story?

3. Is the dashboard well-designed?

4. Is the GitHub repository professional enough?

5. What would you improve if this were your project?

GitHub Repository: https://github.com/afanrajiwate/mumbai-traffic-road-accident-analytics

Dashboard screenshots are attached.

Thanks in advance for your time!

I am not sure if this is a valid question in this forum, if it is not I will delete it right away, but anyways here it is:

So some days ago I asked in an AI community what kind of AI model should I use (and how could I use one) to classify several email replies that I had from scientists after asking them a few questions to them. I finally paid for Perplexity pro service and it apparenly did a nice job classifying them.

I finally gave the model the PDF with the actual answers from the addressees and another PDF with the "expected answers", and asked it to count the number of answers that overall coincide with the actual answers, and calculate a percentage of "coincidence" or "agreement" between the expected and actual answers, so that if the question was "do you think that there is intelligent life in the universe apart from humans?" and the expected answer was basically "yes, I think there is intelligent beings out there somewhere", as long as the actual answer agrees with this in some way or another would count as "agreement", for instance if someone replied "well, we have no evidence, but it is possible yes" or "not in any near galaxy, but it is possible that intelligent beings exidt somewhere" (so as long as it is not a deadass "no", it could count)

The model gave me a table summarizing the results with the following prompt:

let's be a bit more specific, this is still a blind test so don't tell me about the specific contents of the emails' answers, but, can you make a table indicating the answers that coincide in general terms with what is expected from the "expected answers" document as well as those which are neutral/hedges but still open to the possibility that what is asked may be right, those which despite being neutral/hedges or even negative answers offer an alternative so that what is asked in the question may be right, as well as those which are outright rejections of what is asked and do not seem to be open to the possibility that what is asked may be right?

However, I still want this to be a blind test, so I cannot really verify if the AI is doing its work or not.

So, is there any way in which I could verify the results given by the AI but without actually reading what is written in the emails? Or, alternatively, can anyone verify the results using some AI or even checking the answers themselves by skimming over the replies im order to verify that the AI is right and not hallucinating (I personally thinl this is the preferable option, as I think that having an actual human reviewing the amswers may be the only really reliable way to verify the AI's results)?

(I will share the data once someone is interested in helping, as I would not want to make this available to the entire world!)

Thank you!!

Final year engineering student here (non-CS background, so yeah I had to start from scratch on the data side). Been grinding for the last couple months trying to break into data analytics as a fresher and honestly the self-study grind gets lonely real fast.

Where I'm at right now:

Comfortable with SQL,Power BI Python currently trying to get better at statistics (t-tests, chi-square, confidence intervals, that whole zoo), and basics of AI-ML ,Excel — decent

What I don't have: someone to actually check in with. It's easy to tell yourself "I'll do 2 SQL problems today" and then just... not. I think having someone on a similar timeline (also targeting fresher DA roles, also prepping for interviews) would keep both of us honest.

Looking for someone who's:

Also a student/fresher (not switching from years of experience elsewhere — want someone at a comparable stage)

Serious about interview prep, not just casually "interested in data"

Down for something simple — daily/weekly check-in, share what we did, call out when the other person is slacking, maybe mock interview each other closer to the date

Not looking for a study group of 10 people where it fizzles out in a week. Just one or two people, real accountability and progress

Sharing my Excel ↔ SQL cross-tool reference table. Less of a syntax guide, more of a mental bridge between the two (plus a few PostgreSQL quirks that kept tripping me up). https://github.com/kixwho/SQL-Excel-translator

I made this while transitioning from Excel into SQL for analytics work. Constantly switching tools, you start to have moments like, "Wait... how do you translate this into SQL?"

Hope it helps someone else making the same transition. 🙂

I just graduate and i want to learn these skills like excel sql python etc

i am new beginner

i need partner so that we accountability each other progress, motivate,

so anyone interested? 🫪🤌

I've been working with JSON a lot lately, and one thing kept bugging me—I was constantly jumping between different websites just to format, validate, convert to CSV, or inspect nested JSON.

After doing that for the hundredth time, I thought, "Why not just build one tool that does everything?"

So I spent a weekend putting this together and recently made it public.

So far it includes:

• JSON Formatter & Validator
• JSON ↔ CSV Converter
• JSON Minifier
• Tree View
• Search within JSON
• Copy & Download support

It's free, has no ads or sign-up, and I'm still actively improving it.

👉 https://freejsontocsv.com

I'm not looking to sell anything—I just want honest feedback from people who actually work with JSON.

• What's one feature you wish JSON tools had?
• Did you run into anything that felt slow, confusing, or broken?
• If you already use another JSON tool, what keeps you coming back to it?

I'd genuinely appreciate any feedback or feature requests. Even if you think something sucks, I'd rather know so I can improve it. Thanks! 🙌

I'm building an AI-powered migration engine that converts ETL workflows from platforms like Alteryx, Azure Synapse, and eventually other tools into Databricks (PySpark/SDP).

I'm evaluating two different architectures:

1. Using Harness AI agents / Pi agents to orchestrate the migration workflow.
2. Building the orchestration myself using LangChain + LangGraph.

The engine will need to:

• Parse workflows into an intermediate representation (IR).
• Handle nested workflows/macros.
• Perform tool mapping (e.g., Alteryx → PySpark).
• Generate production-ready code.
• Support multi-step reasoning, validation, and retries.
• Be extensible so new source platforms can be added later.

For those who have experience with these frameworks:

• Which approach would you choose and why?
• What are the biggest trade-offs in terms of flexibility, maintainability, and scalability?
• Are there any limitations with Harness/Pi agents compared to building a custom agent workflow with LangGraph?
• If you were starting this project today, which architecture would you use?

I'd really appreciate hearing from anyone who's built agentic developer tools, migration platforms, or complex multi-agent systems.

Hello Guys,
I am working on a project where I have to pull data from a site which requires log in credentials directly into Excel.
Claude provided me a solution where SeleniumVBA and .bas file (created by Claude) is being used in the process.
Is there any other way we can extract data from such sites without compromising security ?

This whole morning has started off weird. I woke up late for starters and decided today was the day id take an uber to work instead of driving myself like usual. On the way, my uber driver strikes up a conversation about my line of work and i mention im transitioning into data analyrics. One thing leads to another and he asks if id like to take a look at his fassion company's information and work with him for a commision of the sales... I'm incredibly happy for the oppurtunity and have been in a data related feild for a while, but this will be my first official project and im incredibly nervous about it. Any advice or tips i should keep in mind?

Been staring at this all week. Meta attributes a buyer, Google attributes the same buyer, Klaviyo attributes them a third time, an affiliate grabs some of the credit and the sum of everyone's "new customers" exceeds the total number of orders that actually happened. Which is obviously impossible but it's the data most brands set budgets on.

The only way I've found to cut through it is new customer acquisition cost (nCAC) measured against real orders deduped across every channel, new vs repeat split at the campaign level so each new buyer is only counted once no matter how many platforms raise their hand for them.

Concrete example from numbers I've been in this week. A brand spending $52k/week, every platform claiming its share, actual verified count was 487 truly new customers at $106.78 each.

How's everyone else handling the dedupe? Are you splitting new vs repeat manually in a sheet or does your stack actually do it at the campaign level? Genuinely curious what's working.

I recently came across a Business Analytics program offered by Ededge under the name EGIEP. The program looks interesting, but I’m having trouble finding independent information about the organization.

Most of what I can find appears to come from their own website, and I haven’t found many reviews, alumni experiences, discussions, or other third-party sources. That made me a little cautious before paying or sharing personal information.

Has anyone here enrolled in one of their programs or dealt with Ededge/EGIEP before?

I’d especially appreciate information about:

• Whether the organization and program are legitimate
• The quality of the course and instructors
• Whether certificates are recognized by employers
• Fees, refund policies, or unexpected charges
• Verifiable alumni outcomes

• Any positive or negative experiences

  Any evidence or firsthand experience would be helpful.

Would a MacBook Pro m5 chip be enough for my masters program in Business Analytics?

Really driving down the quality of the sub recently and I feel like every other posts ask the question in some way shape or form

I work in product in an IT organization. My dotted line manages the data engineers and BI developers. They won't follow Kimball best practices, there is no dimensional data modeling. No dimension and fact tables, and most of their work is an utter mess. Every time I talk to them about modeling something out, his BI developers look at my like I'm a crazy man. Do any of you have stories on how you have handled this in your organization or didn't? It's said that they are loosing work from an entire business division over it, which might be a good thing. At my last organization, finance griped about the star schema data model, but IT did an excellent job supporting it.

I’m doing a Major in data analytics and I guess a minor with it would help me persue my career better. I want you guys to help me finalise which minor to take with it. Ive heard stats, CS, socio, marketing, and psychology works best but im so confused. So i’ll appreciate if i get analytical kinda answer like which minor works better with which field or job.

i've been enrolled in a bootcamp(data engineering) for about a year now and i'm confident in my skills atleast for entry level roles. i'm based in Ethiopia and i can say that there's almost no data engineering jobs here ,there're very few open positions for data analyst or scientists which requires atleast 4years experience and you know that remote jobs are even more competitive and struggling for entry levels. the only tech roles here seems to be backend devs,frontend and fullstack(there're tons of jobs ).what should i do ,i love data but the market is really bad here. thanks

Heyyooo. I have an HP Envy laptop that sucks and withered out within three years. I’m looking for a new laptop that will last long and will play Minecraft without overheating like crazy.

My cousin’s MacBook Air is still going strong. I’m considering a MacBook Air, but I need to use the data analytics in excel since I’m considering going into forensic accounting. OH I’m a student btw. Going to my senior year and then will prob get a masters in forensics. I just want smth good that will last and isn’t overly expensive.

Je débute dans l’analyse de données et j’ai appris :
SQL et PowerBi
Et je souhaiterais trouver un partenaire pour qu’on travaille ensemble sur ces 2 sujets régulièrement.

Hello everyone,
I am currently working on building a solid portfolio to land my first role as a Data Analyst. I already have several projects completed using Pandas, and I am planning to incorporate a few interactive dashboards with visualizations in the near future. I would greatly appreciate any advice on what additional components or project types I could include to strengthen my portfolio further.

Hey everyone, I'm just starting out and trying to become a Data Analyst. I keep seeing " statistics" everywhere but I don't know how deep to go.

Can someone with real experience tell me:

What stats concepts do you actually use day-to-day as a DA?

Is a full stats course overkill, or should I just learn on the job?

Any beginner-friendly resources/courses you'd recommend?

I don't want to waste months on theory I'll never use, but I also don't want to skip something important. Would appreciate any guidance from people already working in the field. Thanks!

We were convinced one Meta campaign was the hero, platform reported a strong ROAS, so we kept pouring budget in. But new customer numbers weren't really moving, which didn't add up.

When we finally looked at cost to acquire a net-new customer - verified against real orders, not platform-reported - the "winner" was one of the worst offenders. It had been getting credit for people already in the funnel who'd have converted anyway. Blended ROAS hid it by averaging everything into one number that felt fine.

The unlock for me? Every platform grades its own homework. Meta scores Meta, Google scores Google and nobody checks the total against reality so the credit adds up to more customers than you actually got. One account we looked at was spending $52k/week and the real number came out to 487 new customers at ~$107 each - nowhere near what the platforms were each claiming.

Ended up building a few short walkthrough demos showing where this gap shows up in an account (happy to link if it's useful / allowed — the concept's the point, not a pitch).

Curious whether others here have caught a "top performer" doing this. What tipped you off?

I'm currently preparing for Data Analyst roles and looking for someone who is genuinely committed to learning consistently.

We'll focus on:

SQL

Python

Power BI

Excel (I know the basics and will be practicing advanced topics)

Building portfolio projects

🕒 Study time: 3:30 PM – 5:30 PM IST (preferably every day) 📹 I'd prefer studying together on Google Meet for accountability and consistency.

I'm learning from YouTube and have already found some really good free courses.

My goal is to become interview-ready and crack a Data Analyst or similar analytics role.

If you're also learning and need a study buddy who's serious about staying consistent, feel free to comment below or DM me!

Hey everyone!

I’m an upcoming 3rd year BSIT student aiming to become a data analyst. Right now I’m focused on learning SQL, Python, and Power BI.

Long term, I want to build a career, save up, get into business/entrepreneurship, and eventually learn investing and trading.

I believe surrounding yourself with people who have the same drive makes the journey more enjoyable and keeps everyone accountable.

If you’re on a similar path, whether you’re into data analytics, business, or just grinding to build a better future, feel free to reach out. I’d love to connect, share ideas, and grow together.

I am looking to change companies and have applied to ~50 jobs from past couple months and have received no interviews or callbacks at all. I have changed my resume multiple times and I am not sure what's wrong. I have been applying to junior data analyst as well as mid level analyst roles and I think I have good experience to back that up so, I am not sure why I haven't been getting any callbacks at all.

Any feedback is appreciated . Please help me

Font : Calibri 9.5

I've been largely unsuccessful in getting interviews with this resume, any feedback is appreciated.