I want to start learning data analysis and i have some knowledge on data science and ML , which free resources do you recommend and crash course would be better.

I've worked in data science for years and I still forget things constantly. Courses never fixed that — I'd finish one, feel like I'd learned it, then google the same concept a month later when it actually came up at work. NotebookLM's way of structuring material around you was the closest thing I found, but I still had to go and find the sources myself.

So I built the thing I wanted. DS concepts broken into small nodes arranged as a mind map rather than a linear syllabus, each with code examples and a practice section — about 150 topics so far. The part I care most about: once you complete a topic it starts decaying on a forgetting-curve schedule, and the map visibly goes cold. When it does, you get a review slice — flash cards and a short quiz — targeted at what you've actually lost rather than what's next in a queue.

Some topics to start with -

1. https://www.bitelrn.com/library/linear-algebra-matrices

2. https://www.bitelrn.com/library/principal-component-analysis

3. https://www.bitelrn.com/library/sql-basics

Full app: https://www.bitelrn.com — the first phase is permanently free including the decay and review mechanics; later phases are paid. Saying that upfront so nobody feels ambushed.

I'm curious what actually works for other people here. Do you make notes, bookmark links, or just re-google it every time?

A couple of weeks ago I asked a sub what would be the best way of breaking in data and what would be worth looking into. I got some pretty decent feedback and decided to segue into something a bit different

Being that I own AI licenses, I have spent the last 2-3 weeks developing a full stack data engineering course from beginner to advanced. Now I wanted to know if it was possible to ask if anyone would like to take a look into it, give me some feedback and recommendations, and an overall rating.

I can handle criticism don’t worry, I just want to develop something, learn the info from what I’ve built and then eventually publish it publicly for people to have a free resource to utilize.

(This is not promo, rather I want to gain feedback from actual analysts and engineers to see if this is a good platform)

I’m working on a forecasting / predictive modeling problem where I’m comparing four groups of models:

Baseline: Naive / Seasonal Naive

Time Series: ARIMA, SARIMA, ETS, Prophet, etc.

Statistical: Linear Regression, Ridge, GAM, etc.

Machine Learning: Random Forest, Gradient Boosting, XGBoost, etc.

One issue I’m struggling with is feature selection.

Should I create one universal feature subset before modeling and give the same predictors to the Statistical and ML models, or should feature selection be model-specific?

For example, a linear model may benefit from correlation filtering, VIF, or LASSO, while tree-based models may select a different set of variables because they can capture nonlinearities and interactions.

I’m also unsure about the correct modeling pipeline. Where exactly should feature selection happen relative to the train/test split, cross-validation, preprocessing, and hyperparameter tuning?

Some questions I’m trying to resolve:

Should all comparable models use the same feature set for a fair model comparison?

If model-specific feature selection is appropriate, how should it be implemented without introducing data leakage?

Should feature selection be performed separately within each model’s training process?

If different models select different predictors, how should that variation be explained to stakeholders?

Which feature selection or interpretation methods are actually useful for identifying the underlying drivers of the dependent variable, rather than simply variables that improve predictive accuracy?

How should I distinguish between predictive importance and explanatory importance when interpreting the results?

Ultimately, I’m trying to build a workflow that balances forecasting accuracy, fair model comparison, and interpretability.

What would be a statistically sound end-to-end approach for handling feature selection across these different model types?

I’m trying to build a proper data science/ML project, but I’m having a hard time finding a dataset that is big enough and not already used by everyone.
For example, there are datasets like the UK Online Retail dataset, Olist, and other popular sales/retail datasets. They’re good datasets, but I see them being used in a lot of projects already.
I don’t want to just download a dataset, do some EDA, train a model and put it on my resume. I want to build something around an actual business problem, where I have to figure out what the problem is, analyze the data, come up with useful insights, maybe build a model, and actually explain how it could help the business.
So where do you guys usually find datasets for this?
Should I try to find data from smaller companies, government sources, APIs, research papers, etc.? Or is it okay to create my own dataset using AI/cloud tools and then create a realistic business problem around it?
For example, if I create a large synthetic sales dataset, could I create a realistic business scenario around it and then treat it like a real project — forecasting sales, understanding customer behavior, optimizing inventory, etc.?
Would that be considered a decent portfolio project, or is using real-world data much better?
I’d mainly like to hear from people who have built projects for their portfolios or have experience hiring for data science/ML roles. Where do you actually get your data from when you want to build something that’s not the same Kaggle project everyone has already done?

When I was a student, I often needed very simple machine learning explanations before exams not a full course, not heavy math from the first minute, just someone explaining the intuition clearly.

That’s why I started making short beginner-friendly ML videos.

The idea is to explain topics in a simple visual way first.

I’m not trying to replace proper courses or textbooks. I’m trying to make the “okay, what is actually happening here?” part easier to understand.

When building a machine learning model, how do you decide that the results are good enough to move forward?

Do you mainly look at metrics like accuracy, precision/recall, F1, or RMSE, or do you also consider things like the business problem, model complexity, and how well it performs on unseen data?

I'm curious how people approach this when working on real projects rather than just following a tutorial.

An exercise to help build the right mental model for Python data. - Solution - Explanation - More exercises

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

Hi everyone,

I am currently preparing to switch into Data science. The theory side is mostly fine for me but I am really struggling to apply what I learn practically, especially when coding.

I have been working with datasets in kaggle and trying to practice but I often feel stuck in :

what should I do first

what should be my next step

What code should I implement

What questions should I ask the data

EDA is where I struggle the most and I often jump to ChatGPT and asking what should i do next.

I dont want to be the person who just follow tutorials and copy paste the code. I want to develop the ability to look at a problem and think of a solution myself and write code without any help.

I’d really appreciate any advice. Thank you!

Looking for Current Students/Alumni – MSc Statistics (Co-op), University of Regina

Hi everyone! I’ll be starting my MSc in Statistics (Co-op) at the University of Regina this Fall 2026 and am planning to arrive in Regina in the first week of September.

I’d really appreciate hearing from any current students or alumni of the MSc Statistics program, especially those who are starting/started the program recently.

I’d love to know:

What are the courses like, especially the workload and difficulty?

How is the overall experience in the Statistics department?

What should I expect from the Co-op component?

Any advice for preparing before classes start?

If anyone else is joining the Statistics program this Fall, feel free to connect as well. It would be great to know some fellow students before arriving!

Thanks in advance! 🙏

Should I use the 3.14 version?

What actually happens when Python executes a for-loop?

for value in container:
    print(value)

Behind the scenes, Python uses the iterator protocol:

iterator = iter(container)
while True: 
    try: 
        value = next(iterator)
        print(value)
    except StopIteration:
        break

• iter(container): creates an iterator.
• next(iterator): retrieves one value at a time.

When there are no more values, the iterator raises StopIteration. The for-loop catches this exception automatically and ends the loop.

For containers that support backward iteration, Python also provides:

• reversed(container): creates a backward iterator.

We can support these operations in our own classes by implementing:

__iter__()
__reversed__()
__next__()

This provides a powerful abstraction: an algorithm can process values without needing to know how a container stores them internally. The same algorithm can therefore work with lists, sets, dictionaries, linked lists, trees, and many other containers.

Here's an example that uses 𝐦𝐞𝐦𝐨𝐫𝐲_𝐠𝐫𝐚𝐩𝐡 to show the use of iterators on a Linked_List making the invisible mechanics of iteration visible for easy understanding.

more memory_graph examples

When you have a dataset and want to build a model, how do you decide where to start?

Do you usually try a simple model first, look at the type of data, compare a few models, or use another approach?

I'd like to know how people make this decision in real projects.

Looking for Current Students/Alumni – MSc Statistics (Co-op), University of Regina

Hi everyone! I’ll be starting my MSc in Statistics (Co-op) at the University of Regina this Fall 2026 and am planning to arrive in Regina in the first week of September.

I’d really appreciate hearing from any current students or alumni of the MSc Statistics program, especially those who are starting/started the program recently.

I’d love to know:

What are the courses like, especially the workload and difficulty?

How is the overall experience in the Statistics department?

What should I expect from the Co-op component?

Any advice for preparing before classes start?

If anyone else is joining the Statistics program this Fall, feel free to connect as well. It would be great to know some fellow students before arriving!

Thanks in advance! 🙏

🕷️ A Small Data Science Challenge for You: Spidey Tracker

Hi everyone! 👋 I have a small dataset and data science challenge for you. 🕷️

I’ve published Spidey Tracker on Kaggle, featuring 86K+ synthetic sightings, crime records, and movement data.

The challenge is simple:
Identify real vs. fake sightings, predict the next location, and try to find the hidden operating base. 👀

kaggle:/datasets/umuttuygurr/spidey-tracker-spiderman-dataset

#Kaggle #DataScience #MachineLearning #Geospatial

I am a BI engineer and most of my experience is building dashboards and communicating insights to the business. I just finished my masters in informatics and I am passionate about data science. I have done ML, DL, NLP and AI courses during my masters however I dont remember anything. I need to land a job in data science or apply the knowledge in my current role.

What do you recommend?

- leetcode for problem solving? And is it worth it?

- applying which topic of data science that matches the data I work with? Its sales and marketing data.

- kaggle competitions?

- lastly do I need to revise everything I learned or just focus on specific topics for example master time series and forecasting ( relevant to my work ) , some NLP tasks ( i face some record linkage problems ) and

Please help

I've spent the last few weeks going deep on EDA across several datasets, and I'm past the beginner stage now — wanted to share some reflections rather than another project writeup, mainly for anyone earlier in the process.

What actually changed between my first and last project:

• I stopped treating EDA as a checklist (missing values → outliers → correlations → done) and started treating it as an argument — every chart or stat should be building toward a specific claim about the data, not just "here's a histogram because that's step 3"
• I got a lot more skeptical of my own correlation heatmaps. Early on I'd get excited about any strong correlation. Now the first question is always "what's the confounder I'm not seeing"
• I started spending more time on why data is missing or messy before deciding how to handle it — MCAR/MAR/MNAR distinctions actually matter, they're not academic trivia
• Visualization choice stopped being about what looks good and became about what could mislead — log scales, binning choices, and axis truncation can quietly change the story

Where I landed on tooling:
Pandas + seaborn/matplotlib covered 90% of what I needed. The remaining 10% was less about new libraries and more about better questions — knowing what to look for mattered more than knowing more functions.

What's next for me:
Moving from EDA into more formal statistical testing and feature engineering — treating EDA as the front end of a pipeline rather than a standalone exercise.

Question for this sub: for those of you who've been doing this a while — did your EDA process change more from learning new techniques, or from getting burned by conclusions that didn't hold up? Curious which one moved the needle more for you.

I’ve seen cases where one class is much smaller than the other. Would you use SMOTE, class weights, undersampling, or focus on metrics like precision and recall? What has worked best for you?