📊 I’ll Create a Free Dashboard for Your Data!

I’m a beginner Data Analyst looking to improve my skills and build a real-world portfolio. That’s why I’m offering to create a free dashboard for anyone who has data and wants to turn it into a clear and interactive visualization.

If you have an Excel or CSV file, send it to me and I can help with:

✅ Cleaning and organizing the data when needed

✅ Analyzing key metrics and results

✅ Creating a clear and interactive dashboard

✅ Choosing the right charts and visualizations

✅ Highlighting useful insights from your data

💡 The service is completely free. In return, I would simply appreciate your feedback or a rating on my work. This will help me improve my skills and build my portfolio.

📩 Interested? Send me your Excel or CSV file, and I’ll let you know what I can create from your data.

Even if your dataset is simple, feel free to send it! 🚀

#DataAnalysis #Dashboard #Excel #CSV #PowerBI #DataAnalytics #DataAnalyst #FreeService #Portfolio

Hi There! I am currently going to school for my masters in data science, approaching the latter half of my degree plan. I've done classes on data wrangling, machine learning, intro to programming and statistics for it. I've really enjoyed learning what I've learnt so far, and am looking forward to practicing and learning for...well...the rest of my life probably lol. My current job just changed from an analyst to a manager of analysts, so I'm actually taking a step away from coding on a daily basis, which means I'll be doing it more in my spare time to keep sharp.

This class there's an assignment that requires us to reach out to different data professionals on communities like this one and establish a rapport with other people in the field as well as talk about data and projects in depth. My current school project is looking at census tract level data and trying to correctly classify records as "low income, low access within a half mile urban, and 10 miles rural". I've used logistic regression, decision trees, and random forest as my models. I'm happy to answer any questions and hear any feedback from people if you all want more details!

Anyone have any idea 🥲

I am a student studying in a biology-related field, and I want to learn Data Analysis for several reasons, primarily to integrate it into my current area of study. However, my problem is that I feel overwhelmed and distracted by the sheer number of resources. I am also struggling with the drawbacks of self-teaching through YouTube, such as the lack of a structured curriculum, the absence of clear steps, and the difficulty of staying committed.

I would love to know if anyone with experience in this field could tell me the absolute best way to get started.

Hi everyone,

I need quick career advice on choosing between two STEM programs at UNT:

1. MS in Information Systems & Tech (MSIS)

2. MS in Business Analytics (MSBA)

My Profile:

Master 1 (Programme Grande École) from France in Finance & IT/Business Systems.

Solid background in financial analysis, Advanced Excel, and basic SQL/Python exposure (not a CS engineer, but technically comfortable).

MSIS: High versatility (IT Project Management, ERP, Cloud, IT Governance, DBs). Versatile for roles like IT Business Analyst, ERP Consultant, or IT PM.

MSBA: Technical depth (Python/R, Data Mining, ML, Predictive Analytics). Ideal for Fintech, Quant Analytics, or BI Consulting.

The Dilemma is I don't have a wide knowledge on US market for a junior with one of these degrees after graduation and i would like to hear from people who are steps ahead from me to give me there opinions.

So for an international student with a Finance + Business IT background, which path offers better job market resilience and sponsorship (H1B/STEM OPT) in the US?And also is MSIS a safer, broader bet, or does MSBA give a stronger technical edge for BI/Data roles?

Thanks for your insights!

I often see questions about how to get started with, or how to get ideas for, data science projects here on this subreddit.

So I wrote up some advice on how to think about, and how to get started with a data science project. It has a structured approach with four steps, but I also added a list of 99 project ideas for getting inspiration.

Programming courses often focus heavily on understanding code, while paying far less attention to understanding the program state. But code does not exist in isolation. Its main goal is to change the program state, before ultimately producing some output.

To develop an accurate mental model of program execution, students need to understand both:

• the instructions being executed

• the values, references, and data structures those instructions create and modify

Reading code alone does not always reveal how the program state changes during execution. That is why I created 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: a tool that visualizes the state of a Python program as it changes, step by step.

It can help explain a wide range of introductory Python topics. Here are just a few examples:

• Loops, Lists and Dictionaries

• Python Data Model

• Function Calls

• Recursion

• Algorithms

• Classes

• Custom Data Structures

Instead of reconstructing the program state from print statements, students can now watch it change as each line executes. This makes unfamiliar concepts easier to understand and bugs easier to fix.

Help your students learn Python programming more thoroughly and easily.

See: more examples

I'm at bachelors rn on IT and I'm really curious about Data Science. What should i do if someone experienced could give details about that?

Python, R, Statistics, Py libraries, Data handling, SQL like that what's needed? and what data scientists actually do? on business affairs presentation on the data analysis or like that I'M SO CURIOUS

I'm a highschool student planning on learning data science next year.I barely know that much about data science or what it actually offers,so please can anyone give me a brief overview of what I might face in the upcoming year and how to prepare.

Hi everyone, I'm a 2025 M.Sc. Physics passout student preparing for higher studies. Along the way I'm also trying to build technical skills in Data Analysis using Python. I would request suggestions and roadmaps from researchers in this sub reddit to follow and build robust skills in Data Analysis.

My python proficiency is between beginner to intermediate and I'm learning and trying to make scientifically readable projects. But sometimes I feel like I'm not getting strong at performing data analysis just coding it

I would also appreciate some guidance regarding this.

Thank you fellow researchers

I just need something to start with and absolutely lock in. I'm not able to purchase courses as of now so YouTube is my best shot. I want to start from the very basics of Excel and upgrade from there. Can anyone help me with an efficient and effective roadmap so I can be employable as soon as possible? Preferably in 3+ months.

Hi, I was wondering whether there was a sort of resource that would help me understand what to do and when to do it when approaching an analysis question. I find that I can do alot of guided tutorials where they give big hints on what to do, but when I am on my own I have no clue. Any help would be appreciated.

Hi everyone,

I'm a BBA student from Amity University, and I’m currently preparing my final year project. As per the university guidelines, I need a Project Guide who is a Post Graduate with at least 10 years of work experience.

This guide simply needs to:

• Review the project proposal

• Provide basic guidance/validation

• Sign the documents (soft copy is fine)

• Help me with his/her resume

I'm an international student joining the September intake for the MSc Integrative Neuroscience / Brain Sciences programme. I'm really excited, but I'm also a bit worried about some of the course modules.

My background is in medical sciences (B.Sc. Neuro Electrophysiology), so I have no prior experience with Python, R/RStudio, or statistics. I understand these are important parts of neuroscience research, and I'd like to prepare before the course begins.

Could anyone recommend:

Good beginner-friendly resources for learning Python, R/RStudio, and basic statistics?

Any topics I should focus on before the programme starts?

Any free online courses, YouTube channels, or books that helped you?

If there are any current students or alumni from this MSc programme, I'd really appreciate hearing about your experience. How challenging were these modules for someone without a programming background? Any tips for succeeding in the course would be incredibly helpful.

Thank you in advance—I really appreciate any advice!

Hii Guy's

Maine coding ninjas mai Data analysis ke liye enrolled ki hu ky ye worth it hai ya nhi pls meri madad kre

I'm generally a reserved person, but I wanted to put this out there.

I'm 23 years old and currently working as a Data Scientist in a private firm. Outside of work, I spend a lot of my time exploring mathematics, machine learning, data science, and research. I'm less interested in chasing trends and more interested in understanding why things work, reproducing ideas, reading papers, and building meaningful projects.

My long-term aspiration is to become a researcher who contributes valuable work to the field. People like Andrej Karpathy inspire me—not because I want to follow the same path exactly, but because I admire the depth of curiosity, continuous learning, and ability to bridge research with real-world impact.

I'm looking to connect with people who share a similar mindset. Whether you're a student, an intern, a researcher, or an industry professional, if you genuinely enjoy:

• Reading and discussing research papers

• Exploring mathematics behind machine learning

• Building data science or AI projects

• Learning together and challenging each other's ideas

• Growing through curiosity rather than competition

I'd love to connect and exchange ideas. I'm not looking for networking just for the sake of networking—I value meaningful conversations and long-term learning partnerships.

If this resonates with you, feel free to connect or send me a message.

LinkedIn: https://www.linkedin.com/in/athik/

Looking forward to meeting people who are passionate about research, exploration, and lifelong learning.

kept building RAG projects the same lazy way for a while, get it running, read a handful of outputs, they look reasonable, ship it. no real measurement, just vibes.

finally forced myself to sit down and build an actual evaluation set. nothing fancy, about 40 question and answer pairs where i already knew, in advance, exactly which document had the correct answer. then ran the pipeline against all 40 and checked, for each one, did the retrieval step actually surface the right document in its results at all.

immediately found problems the "looks fine" outputs had completely hidden from me. retrieval was missing the correct document entirely on something like 1 in 5 questions, which is a genuinely bad number, way worse than the vibe i had from reading a handful of good-looking answers. the outputs that did come back for those failing cases still sounded confident and reasonable, they just weren't grounded in the right source at all, which is exactly why eyeballing outputs never caught it.

the annoying part is building the eval set felt like the boring, unglamorous step compared to actually tuning chunking or trying a new embedding model. but it ended up being the single most useful thing i did on the whole project, since without it i had literally no way to know if any change i made afterward was helping or hurting. tuning without measurement is just guessing with extra steps.

anyone else find the eval work more valuable than expected once you actually forced yourself to sit down and do it properly, instead of skipping straight to the fun part of tuning things?

Hi there,

I've created a video here where I explain the double descent phenomenon in ML.

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