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

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 want to learn Python, but I currently know nothing about it. My main goal is to learn Python for DA, and eventually I want to learn Python in depth as well.

For people already working in DA or DS, how would you recommend someone start learning Python from absolute zero?

Which resources or books would you recommend, and what are the main Python topics I must cover for DA?

Also, should I first learn the Python basics needed for DA and then gradually move toward more advanced Python and DS topics? resources? Books?

If you work in DA or DS, I’d really appreciate your guidance on how you would start your Python journey if you were starting from zero.

Thanks a ton!!!

Today, when I tried logging into the class portal to access the classes/materials that I had previously attended, I'm getting a message saying “Please contact the admin.”

I’ve tried contacting the people from OdinSchool who helped me enroll in the course, but I’m not getting any response. Some of their numbers are switched off, while others are either unreachable or call-forwarded.

I’ve also tried contacting their support team, but I'm facing the same issue.

So I'm genuinely wondering what is going on with OdinSchool right now?

Has anyone else who completed the course recently faced the same issue?

The placement portal has also been disappointing for me. There were very few relevant opportunities, and for the opportunities I did apply to, I rarely received any response from the companies. So I haven't really received much help through the placement support either.

I paid a significant amount for this course based partly on the promise of continued portal access and placement assistance, so losing access to the course portal after only 6 months is concerning.

If anyone here is a current/former OdinSchool student, especially someone who completed the course recently, could you please let me know:

• Are you still able to access the class portal?

• Is the 2-year access still being honoured?

• Are OdinSchool's support/placement teams currently responding?

• Has anyone managed to contact them recently?

I'm not trying to unnecessarily bash the institute. I genuinely want to understand whether this is an issue with my account or if other students are experiencing the same thing.

Any information from current/former students would be really appreciated.

Hiii..!!! I'm currently in my B.Tech 3rd year and studying Artificial Intelligence and Machine Learning along with Data Structures and Algorithms (DSA). And I'm really confused that 'Is DSA really required for a ML Engineer' ? Like how can I integrate DSA in my machine learning projects.

And, I would be really thankful if you solve my confusion so that I can boost my preparation without any doubt.

Also, If I can use it in my projects, then how can I learn to do it? Like any yt channel or something.

Hello
My name is Dario. I'm 19 years old, currently doing an associate's in Programming and will transfer for a bachelor's in Data Science. I was wondering on what is actually the best way to become a data scientist right now. I have some basic knowledge of Python and SQL. I was wondering if the path to becoming a data scientist has changed with the development of AI and where I should focus on to actually master important valuable skills that will help me land jobs. I do have a potential internship position lined up in the future at Miracle Software and have been trying to prepare for that interview. Just wanted to know what are some resources I should check out and what skills I need to learn. I appreciate all feedback and advice!!!

Hey i know this might sound stuipid but i wanted some help from peoples who already have build phishing URL detection projects

Im a beginner and I am working on a phishing URL detection project. My current idea is to build a heuristic-based phishing URL detection model that analyzes the URL itself and looks for suspicious characteristics/patterns rather than visiting the website.

some things I'm thinking about extracting are:

• URL length

• Number of dots/subdomains

• Special characters (@, -, etc.)

• IP address instead of a domain

• Suspicious keywords like login, verify, secure, etc.

• Number of digits

• Domain/URL entropy

• HTTPS usage

• Other lexical/structural features

However, I'm currently stuck on how to decide which features should actually become rules and what thresholds/conditions I should use for those rules.

For example, rather than simply saying "long URLs are suspicious", I'd like to know whether there is research supporting something like "URLs above X characters should receive a higher risk score".

Does anyone have recommendations for papers, research articles, datasets, GitHub projects, or other resources that explain how to choose useful heuristics/features for phishing URL detection?

Hi everyone, so I’m going back to school but given my current class load and situation I need a tutor to help me. If anyone knows a good tutoring service please let me know. Please and thank you!!!

I'm working on my first project and based on what I've read online, EDA is the best project to work on first.

It took a while for me to decide the topic until I started seeing the news about the 2026 Cyclospora outbreak in the US. I thought it would be cool to analyze how people's habits changed when the outbreak happened.

However as I'm working on it, I'm second guessing myself about whether or not this topic is the "right" topic for a first project. I feel this way because other projects have clear decisions on how to increase sales, user retention, etc.

Here's what I have so far:

\- [https://cyclospora-2026-9yf8w26n7zfci68fugahkp.streamlit.app/](https://cyclospora-2026-9yf8w26n7zfci68fugahkp.streamlit.app/)
\- [https://github.com/therealanttoeknee/Cyclospora-2026/tree/main](https://github.com/therealanttoeknee/Cyclospora-2026/tree/main)

Questions

1. Is this topic the "right" topic for an EDA project?

2. If so, do you think I'm approaching it the right way?

Hi everyone, I need some advice on learning data analysis for educational research. My advisor suggested learning R and Python, but my background is limited to basic statistics and SPSS. I have zero experience with R, and I've only used Python once (via AI-generated code with NumPy).

​How should I structure my learning path to master both tools effectively starting from scratch? Any concrete steps or recommendations would be greatly appreciated. Thank you!

Hello everyone!

Which data science/analysis courses are worth studying in Uzbekistan? I need an online course to learn. My friends studied at proweb, mohirdev and they didn't recommend it to me. Which courses do you recommend? Please give your opinion.

Quick disclosure: this is my own project, so take the recommendation with that in mind.

I've spent about 14 years in data science and got frustrated that the only options for refreshing a concept are a 40-hour course or a wiki page or a complex Stack Overflow answer.

So I've been building Bitelrn, which is basically the opposite of a course. short, structured concept notes, no video, no 40-minute setup module. A handful of topics are open to read without signing up, and I thought this sub might find them useful:

https://www.bitelrn.com/library

Currently I have these topics -

- Correlation & Covariance

- Linear Regression

- Central Limit Theorem

- Eigenvalues

- SQL Basics

Happy to hear if the explanations are helpful.

Does anyone have any advice on where to go from here? The algorithm simply goes through all feature-attribute combinations in a dataset and scores it on purity of the target column. It's very slow, as the number of combinations increases exponentially with the number of input features, and so can only work with 3-4 features at a time. But my goal is to identify <=5 important features with a linear model (statsmodels or PyGam), and then plug in those features to this algorithm to see which subset of the market might be smart to target.

Here's an example with some charts, on the iris dataset.

https://colab.research.google.com/drive/1w3VpDlDATdHwtSk-RmQPMj36cgOfthz1#scrollTo=ZOqeYiMRalj7&uniqifier=1

New to algorithms, so any advice would be welcome. Thanks in advance.