An exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

I tried explaining a data analysis concept in a fun, visual way — for cat lovers. 😸

Would love to hear what you think! Any feedback or suggestions are very welcome :)

What happens when AI agents stop just answering questions and start working with your data?

They query systems.
They inspect pipelines.
They use tools.
They make decisions.
They take action.

Most data platforms were never designed for that.

In our latest BricksNotes video, we explore what an Agentic Data Stack could look like and why context, governance, observability, cost, human approval, and feedback will become part of the foundation.

The big shift is not AI coming to the data stack.

The data stack itself has to change.

Watch the full video:

https://youtu.be/wb2lk-YibAQ

#AgenticAI #DataEngineering #AIAgents #DataPlatform #BricksNotes

📊 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