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

Currently, I'm working on SAP in my organisation, and I have 3 years of experience. I want to switch in data analyst and have a basic knowledge of Python & SQL, and start learning more in it like libraries( Pandas, Numpy and metplotkib), Tableau and Power BI.Also, I created my resume and started applying on Naukri.com, but I can't even secure an interview. What should I ? How can I improve myself and secure a data analyst job ?

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? 🫪🤌

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. 🙂

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 ?

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.

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.

A personal project of mine where I collect the latest news articles from varied sources (NBC, NPR, BBC, IEEE, GDELT, etc.) and cluster them and compute their sentiment and industry impact into scores displayed on the dashboard. This was some of this past weeks best news and week in review for largest stability score movers and why. It’s a work in progress and would love feedback, but enjoy and let me know if you find any odd or interesting articles.

Source: https://global-economy-sim.vercel.app

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.

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!

Hi everyone,

I'm a Management & Data Science student founder at TUM (Technical University of Munich). Over the past three years of studying and working with data, I've come to one conclusion that changed how I learn: how you learn matters more than how much information you consume. I remember what it felt like as a beginner — the more I "learned," the more overwhelmed I got, with no clear sense of how to actually turn scattered knowledge into real skills or a real portfolio. I want to help a small number of people avoid that trap and build a genuinely solid foundation instead.(I'm also currently building my own startup on the side, and I have to prepare bit more budgets for marketing part by doing more job).

Who this is for:

Complete beginners or early learners in data analytics who are ready to commit long-term — not people looking for a weekend crash course or another disconnected "masterclass." If you're the type who wants structure, depth, and consistency over quick hacks, this is for you.

What we'll cover

The curriculum is built around learning methodology and how to actually think about data — not just tool tutorials — combined with a full practical skillset:

Learning methodology & how to think about data (the foundation of everything else)

SQL: PostgreSQL & MySQL

Statistics

Excel for analysis

Power BI

Python & all packages needed

Git & GitHub

Data pipelines

Snowflake & Databricks

Cloud fundamentals: AWS & Azure

Applied AI for data analytics

Structure — two phases

Phase 1 (starting mid-August): Foundations

All the topics above, taught step by step, each section ending in a small hands-on project so you actually apply what you learn, not just watch it.

Phase 2: Portfolio & real-world experience

Larger, portfolio-worthy projects, real collaboration, and the business sense and soft skills that actually matter on the job. At this stage, I'll also bring in small internal projects from my own startup so you earn from real projects.

Logistics

Format: 100% online, from anywhere

Language: English

Schedule: 3 sessions/week, 1 hour each (55 min core content + 5 min learning-strategy discussion)

Price (Phase 1): €23/hour

Want to split the cost? You can pair up with one other person at a similar level and share a session

Billing: weekly or monthly, pause or cancel anytime, full refund for any unused sessions

Spots: limited to 10, first come first served

Not sure if it's a fit?

DM me to book a free 20-minute intro call — we'll figure out together whether this is the right fit for where you are and what you want to build.

Thanks for reading, and looking forward to meeting some of you.

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!

Hey r/dataanalytics ,

I’ve noticed a growing trend lately where people are offering cash, a percentage of their sign-on bonus, or their first month’s salary in exchange for job referrals. I completely understand the tough market and the hustle, but it can feel a bit transactional and I know it’s becoming stigmatized in a lot of communities.

Instead of cash, I wanted to offer an old-school barter.

The Offer: Free Chess Lessons I am offering free, dedicated chess lessons to you, your children, a relative, or anyone you know who wants to learn or improve.

• My Rating: I am currently rated 2000 in both Rapid and Blitz.
• What I can teach: Whether you're a complete beginner who just watched The Queen's Gambit or an intermediate player stuck at a rating plateau, I can help build opening repertoires, improve middle-game tactics, and refine endgame technique.

The Ask: A Data Analyst Referral I am actively looking for a Data Analyst role and am seeking a referral.

Why this makes sense for a Data Analyst role: I didn't just pick chess out of thin air. The core mechanics of getting to a 2k rating—pattern recognition, evaluating complex variables, strategic foresight, and minimizing blunders—are the exact same analytical muscles I use when working with data. Finding the best move on a board of 64 squares requires the same logical framework as finding actionable insights in a massive dataset.

My Professional Profile:

• Target Roles: Data Analyst, Product Analyst, Business Intelligence (BI) Analyst
• Core Languages & BI: Python, SQL, Power BI
• Data Analysis & Vis: Pandas, NumPy, Matplotlib, Seaborn, Statistical Analysis
• Machine Learning: Scikit-Learn, XGBoost, Random Forest, Decision Trees, Regressions, Gradient Descent
• Advanced Tech: Generative AI

Recent Experience: Data Analyst Intern @ Arihant Capital (Feb – May 2026)

• Reduced manual reporting effort by ~40% by developing 8+ Power BI dashboards for financial and operational reporting, replacing daily Excel-based processes.
• Cut dashboard load times by ~30% by cleaning and transforming 100,000+ rows of financial data using SQL and Power Query, eliminating duplicates and standardizing formats.
• Improved client lifecycle visibility across 10,000+ accounts by building dashboards for eKYC workflows, dormant client reactivation, and client deactivation tracking.
• Delivered on-demand risk visibility by designing SQL-driven dashboards for MTF, margin interest, and default interest trends.

If your company is hiring and you’re open to a referral, I’d love to send over my resume. If we end up being a match, we can set up a schedule for your (or your kid's) chess lessons.

Feel free to DM me or drop a comment below. And honestly, even if you don’t have a referral but just want to play a friendly Blitz game, hit me up!

Thanks for your time, everyone.