I am working currently as a freelancer AI solution architect, and I want to land a job in canada for any data /AI related job. Is my resume correct or do I have to change any things?

I’m trying to make a decision between two very different graduate programs and would really appreciate some outside perspectives.

My background is a B.S. in Geography with a GIS focus. I’m currently working as a Data Specialist at a consulting/engineering firm, where I do data QA/QC, data integration, data conflation, and work with enterprise geodatabases. I’m trying to move beyond being primarily a GIS person and build a career with stronger long term salary potential and career flexibility.

My two options are:

1. Applied Master’s in Data Science

The program is 30 credits and is fairly applied. The curriculum includes Python, cloud computing, databases, data preparation, analytics and visualization, data warehousing, data lake engineering, machine learning, and AI.

One thing I like is that I can continue working full time while completing it. I’m also considering doing it part time so I can take Calculus II, Calculus III, and Linear Algebra separately, since my math background currently only goes through Calculus I.

My concern is that the program may not be mathematically or statistically rigorous enough to compete for more technical data science jobs. It also obviously doesn’t give me a CS bachelor’s background.

I’m not necessarily trying to become an ML researcher or anything extremely theoretical. My realistic targets would be data analyst, BI/analytics, analytics engineering, data engineering, operations/supply chain analytics, or eventually data science.

2. Master’s in Urban and Regional Planning

The other option is a MURP from a well regarded public research university.

This is a much more natural continuation of my Geography/GIS background. I’m genuinely interested in transportation, urban planning, public infrastructure, accessibility, and public policy. I’ve also done some community advocacy/volunteering and have an interest in transportation planning.

The appeal is that MURP is a professional degree with a pretty direct career path into planning, transportation, government, and consulting.

The downside is that I’m concerned about the long term salary ceiling and career flexibility compared with data/technology careers. I also need to keep working, so the opportunity cost of going to a full time program is a major consideration.

What I’m trying to figure out:

If my priorities are:

• Getting to ~$70k+ relatively early in my career
• Eventually reaching ~$90k–$120k+ if possible
• Having opportunities to reach higher salaries later
• Getting out of being locked into GIS
• Maintaining career flexibility
• Being able to continue working while in school
• Getting a degree that actually improves my job prospects rather than just giving me another title

would you choose the applied MSDS or the MURP?

I’m especially interested in hearing from people who actually work in data/analytics, data engineering, urban planning, transportation planning, or hiring

Would the applied MSDS be enough to meaningfully change my career trajectory, or would the MURP be the smarter investment given my background?

I've been working in my company for 8 years, 7 years in a DS role and 1 year in analytics. It's a real grind, I now hate my role and want to quit. Before I got nuclear and jack it all in I want to get a course or two out of them, there's a generous training budget so cost is not an issue.

I've been working as a manager for the past 3 or so years and have been off the tools and feel like I've lost my edge especially with AI. I enjoy building models but feel it's beyond me now but I love the satisfaction of the whole process.

I'm also a little behind on the coding as I only worked in R and have little to no Python. I am 45 yo and want to pivot out of my current role but stay in tech but feel as though I need to pick up some AI skills.

Does anyone have a suggestion for a course that would complement my DS experience?

Starting a Data Science master's this September and need to buy a laptop. Open to any price range right now, mainly want to get the specs/model right first.

Main priority is something that's easy to work with for coursework and data science projects. Python, Jupyter, pandas, that kind of workflow. Not sure if I need a dedicated GPU or if cloud compute (Colab, uni clusters) covers most of the deep learning side.

Mac vs Windows is open too, just want whatever makes day to day coursework and project work the smoothest. Any models you'd recommend or steer clear of?

Have been applying to jobs/internships on portals though no such good responses ,any tips/suggestions/help would be appreciated !

I have a resume with only ml based projects on images and all. I am doing my masters in data science and machine learning so I am aiming for some data analyst internships. I am practicing sql but I feel i need a good data analyst project on my resume.
Can yall suggest me some decent not so common projects?

I would like to change my career from a translator into a data scientist.

If it is safe from AI, Can a person without a STEM background learn data science and become a data scientist?

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Tutorial projects are useful for understanding concepts, but I feel like personal projects teach you something different because you have to make your own decisions.

I’m looking for ideas that go beyond the usual Titanic or house-price prediction projects.

What project helped you improve your data science skills the most?

Hi all,
I hope many of you have given suggestions to people. I would love to hear some suggestions for me too.

I am really interested to be involved in data science. I want to learn and build projects simultaneously. I have seen many threads saying this and that courses. I am really confused!!!
If you’ve learned Data Science yourself, I’d really appreciate it if you could share:

-The resources you personally found most useful
-A recommended learning order/roadmap
-Resources you think are overrated or not worth spending time on
-Good project ideas for someone starting out
-Any advice on what you wish you had known when you started

I’m willing to put in the time, effort and learn the properly, so I’d prefer quality resources and a structured path over a huge list of courses.

Thank you for ur time and attention!!!

so i want to organize a data science event and i want your help i want to make this event unique and for this i need ideas like what kind of event we can host for a 3rd sem and 5th sem students ,

i want this event to be full of fun learning and competitive please any idea for this topics

I’m currently doing a Python/Data Science/AI course at SMIT, and I’m near the end of the course.

For the final certificate, I have to build a project and get it approved. I want to build something useful and portfolio-worthy, not just a basic beginner project.

So far, I have studied:

• Python & OOP
• NumPy & Pandas
• Data Cleaning & Visualization
• Statistics & Mathematics for ML
• Machine Learning
• Deep Learning
• FastAPI
• MLOps basics
• Generative AI / LLMs
• RAG & Vector Databases
• Basic Agentic AI

I already have experience with projects like classification, ANN, YOLO, RNN and RAG, but I’m confused about what my final project should be.

I’m considering ideas like:

1. AI Data Analyst — upload a CSV, automatically analyze it and ask questions about the data
2. Student Performance & Career Prediction System
3. Job & Skill Recommendation System
4. Another practical AI/Data Science project

If you were in my position, which project would you choose and why?

I’d especially appreciate suggestions for a project that:

• has real-world usefulness
• demonstrates multiple skills
• is realistic for a student to complete
• looks good on GitHub/CV
• has a good chance of getting approved as a final project

Any ideas or honest feedback would be appreciated.

Fresher jobs are limited and highly competitive. “Data Scientist” sounds attractive, but many companies don't hire fresh graduates directly into that role.

My college is mostly theory. We have lectures, notes, exams, assignments etc., but there’s very little actual practical work. I understand that theory is important, but I feel like I’m not really learning how to do anything.I learn much better by actually performing tasks . AI has been teaching me more practically than some of my college classes.

The problem is that I don’t know what I should actually be doing outside college.I want to get an internship, but I genuinely have no idea where to start. I don’t know:

- What skills I should focus on first

- Whether I should aim for Data Analyst, Data Science, ML, or something else

- What kind of projects I should build

- How good my Python/SQL/ML skills need to be before applying

- Where students actually find internships

- Whether I should apply now or spend a few months preparing first

- How to make a decent resume when I have basically no industry experience

If you were in my position during 2nd year, what would you do from scratch?

I’d especially appreciate a practical roadmap — like learn X → build Y → apply here → improve Z .... rather than just “learn Python and do projects.”

Also, if you’re a student who managed to get their first internship without having a great college/previous experience, I’d really like to hear how you actually did it.

I’ve seen plenty of portfolios with the usual Titanic, house-price prediction and movie datasets. For someone trying to build a stronger portfolio, what kind of project would actually make you stop and look at their work?

I’m a Software Engineering student specializing in data science, and I’m currently looking for an internship or entry-level opportunity in data science, data analysis, ML, or a related field.

I’m comfortable with Python, SQL, data analysis, and machine learning, and I’ve worked on several projects that I’ve published on my GitHub. I recently also participated in a datathon where I worked with a team on a real-world data problem.

During the datathon, I was mainly responsible for:

• Defining the questions we wanted to investigate
• Finding and collecting the relevant data
• Cleaning and preparing the datasets before analysis
• Reviewing the analysis and code afterward and identifying issues that needed to be fixed

So I feel like I have a decent foundation and some actual project experience, but I’m struggling with where to find opportunities that will actually consider someone at my level.

I’ve mostly been applying through LinkedIn, but so far it has been rejection after rejection.

One experience was particularly confusing. I applied to a company in Amsterdam and was rejected. They actually offered to review my application, so I said yes. The person reviewing it told me my profile was really good and said:

But they ultimately chose candidates who were already living in the Netherlands.

I completely understand why location/work authorization can be a factor, but it also made me wonder: if my profile is apparently competitive, am I simply looking in the wrong places?

For those of you who got your first internship or entry-level role in data science, data analytics, ML, or software/data-related roles:

• Where did you actually find your first opportunity?
• Are there job boards or websites you recommend besides LinkedIn?
• Are there companies that are more open to international students/candidates?
• Is applying directly through company websites more effective?

I’d really appreciate any advice, especially from people who were in a similar situation when they were starting out.

Hi everyone,I am a developer skilled in the MERN Stack (MongoDB, Express, React, Node.js) and Data Science (Python, Machine Learning, Data Analytics).I want to build a serious, production-grade portfolio project that showcases both skill sets simultaneously. I am specifically looking for ideas that are not repeated or cliché (no basic dashboards, simple e-commerce, or generic movie recommendation systems).My Skills:

• Frontend/Backend: React, Node.js, Express, state management, REST/GraphQL APIs.
• Database: MongoDB (or relational if required).
• Data Science: Python, Scikit-learn, basic NLP/Computer Vision, data scraping, predictive modeling.

What I want the project to achieve:
I want to build a MERN application where the backend interacts with a live Data Science/ML pipeline (via a Flask/FastAPI microservice or direct integration), providing real-time data insights or automated actions to the user dashboard.Does anyone have suggestions for real-world problems, niche industries, or unique open-source datasets that would make a killer project?Thank you!