r/CanadaUniversities

I’m planning to apply for a Master’s in CS/CSE in Canada and would like to know my chances.

my_qualifications:

B.Tech CSE – IIIT Kottayam, India

CGPA: 8.07/10

~2 years of work experience as a Software/Data Engineer

Skills: Python, SQL, PySpark, Azure, Microsoft Fabric, Snowflake, ETL

Microsoft certifications in Fabric/Azure and agentic Ai

IELTS: Planning to take it; expecting 6.5–7.0

I’m mainly considering:

University of Waterloo

UBC

McGill

University of Alberta

McMaster

Could you suggest my chances (ambitious/target/safe) and whether my work experience helps significantly?

Also, is IELTS 6.5–7.0 sufficient, and are GRE scores required?

I want to get a somewhat decently paid government job which might be a bit far fetched and probably really boring or idk but I honestly dont know what else I want to do. A stable job with good pension sounds pretty good though. I was planning to maybe do political science or maybe smth health policy. Love to hear any advice or anything, would really help thanks!! It'll be great to know what people have ended up doing after taking social sciences. I also heard that uottawa was a pretty good option, mcmaster also sounded pretty nice, not sure though.

PS. A bunch of friends are planning to get into waterloo so it's pretty high on my list right now. I'm not sure if it's personally the best option for me though idk. I know I'll be awesome to rush to class on the same campus together and go home to a group minecraft session while cramming for midterms or smth.

1. [tsLLM-SG: Time Series Forecasting by Fine-Tuning LLMs with Dynamic Soft Prompts and Gated Frequency Transformation Adapters](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_32.pdf)

2. [SMART: A Statistic-Driven Semantic Alignment and Routing Framework for Multivariate Long-Term Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38410-2\_31.pdf)

3. [Dual-MoE: Decoupled Cross-Modal Calibration with Scenario-Aware Expert Routing for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_41.pdf)

4. [TimeGFD: A Lightweight Client-Side Time-Series Forecasting Framework Based on Generative Federated Distillation](https://link.springer.com/content/pdf/10.1007/978-981-92-2497-5\_10.pdf)

5. [PSformer: Parameter-Efficient Transformer with Segment Shared Attention for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-31335-5\_32.pdf)

6. [SA-MoE: Spectral-Aware Mixture of Experts for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_10.pdf)

7. [PCR-Patch: Prompt-Calibrate-Refine Architecture for Short-Term Electricity Load Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_30.pdf)

8. [TFMoE: Bridging Dual Domains for Long-Term Time Series Forecasting via Adaptive Time–Frequency Modeling](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_5.pdf)

9. [DiSRet: Directional Structure-Aware Retentive Network for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_6.pdf)

10. [SFAT-Net: Non-stationary Time Series Forecasting with Multi-resolution Temporal Embedding and Adaptive Alignment](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_4.pdf)

11. [SymMamba: A Symmetric Dual-Stream Framework for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-31930-2\_20.pdf)

12. [Balancing Temporal and Inter-variable Dependencies in Multivariate Time-Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_17.pdf)

13. [WMENet: A Multi-resolution Wavelet Mamba Enhancement Network for Long Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_2.pdf)

14. [STMixMamba: A Lightweight Hybrid Model for User Trajectory Forecasting in Location-Based Services](https://link.springer.com/content/pdf/10.1007/978-981-92-5699-0\_16.pdf)

15. [Dynamic Redundancy-Aware Variate Pruning: Striking the Efficiency-Accuracy Balance in Long-Term MTS Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3447-9\_36.pdf)

16. [R2Net: Recurrent Residual Network Model for Time Series Prediction](https://link.springer.com/content/pdf/10.1007/978-3-032-37933-7\_6.pdf)

17. [SCFT: A Dual-Domain Orthogonal Representation Framework for Efficient Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_22.pdf)

18. [FW-Aug: Fourier-Wavelet Input Augmentation for Transformer-Based Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-2480-7\_11.pdf)

19. [ClusterPatchTST: Uncertainty-Aware Causal Clustering for Heterogeneous Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_31.pdf)

20. [S2DBM: Series-to-Series Brownian Bridge for Time Series Point Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_25.pdf)

21. [Revisiting Long-Term Time Series Forecasting: An Empirical Perspective on Recent Approaches](https://link.springer.com/content/pdf/10.1007/978-3-032-21625-0\_43.pdf)

22. [NuTS: Non-uniform Sampling with Distance-Dependent Adaptive Fusion for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0369-7\_28.pdf)

23. [WaveFi-Patch: Strengthening Channel-Independent Long-Term Time Series Forecasting with Wavelet-Inspired Decomposition and Time-Aware Modulation](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_28.pdf)

24. [A Temporal-Structure-Aware Enhancement Method for Industrial Process Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_29.pdf)

25. [Adaptive Wavelet Decomposition-Based Dual-Path Transformer for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3394-6\_2.pdf)

26. [Chaoslinear: A Time Series Forecasting Method Integrating Sequence Uncertainty with Deep Learning](https://link.springer.com/content/pdf/10.1007/978-981-92-3554-4\_21.pdf)

27. [TFDF-Net: Time-Frequency Decomposition for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38401-0\_6.pdf)

28. [Gaussian Process Conformal Prediction for Risk-Conditional Coverage Consistency](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_33.pdf)

29. [TS-Unlearn: A Dual-Objective Unlearning Framework for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-1462-4\_27.pdf)

30. [UniMixer: unified patch-wise and global inter-series dependency modeling for multivariate time series forecasting](https://link.springer.com/content/pdf/10.1007/978-981-95-4149-2\_30.pdf)

31. [SparseCycleTransformer: Dual-Pathway Sparse Gating with Periodic Semantic Guidance for Efficient Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3447-9\_24.pdf)

32. [Point-Patch Transformer for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0378-9\_25.pdf)

33. [Periodicity-Aware Time Series Forecasting for Electric Load](https://link.springer.com/content/pdf/10.1007/978-981-95-9207-4\_4.pdf)

34. [(model name missing in Scholar): A Multi-scale Multi-level Decomposition Transformer for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38404-1\_20.pdf)

35. [Multi-domain Time Series Reconstruction with Frequency-Enhanced Transformers](https://link.springer.com/content/pdf/10.1007/978-981-92-2480-7\_33.pdf)

36. [Frequency Energy Adaptive Normalization for Non-Stationary Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3391-5\_17.pdf)

37. [DynaRoute: Dynamics-Conditioned Routing for Universal Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_48.pdf)

38. [XML with XPath, XMLStarlet, and Python](https://link.springer.com/content/pdf/10.1007/979-8-8688-2077-9\_11.pdf) (off-topic)

39. [GRFM: Graph-Aware Residual Forecasting via Periodic Decomposition and Residual Graph Modeling](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_9.pdf)

40. [AMGNet: Dual-Domain Scale-Specific Graph Learning for Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_42.pdf)

41. [Multivariate Time Series Forecasting with Frequency-Domain Similarity Clustering and Hierarchical Feature Interaction](https://link.springer.com/content/pdf/10.1007/978-981-92-3384-7\_47.pdf)

42. [AEFTS-Net: Period-Aware Spectral-Temporal Co-design for Efficient Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-2759-4\_22.pdf)

43. [DPFT: A Framework for Decomposed Probabilistic Forecasting with Transformers](https://link.springer.com/content/pdf/10.1007/978-981-92-3450-9\_22.pdf)

44. [DBHE: Dynamic Basis-Driven Hypernetwork Experts for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-38404-1\_38.pdf)

45. [DDCG: Dual-granularity Dual-domain Collaborative Graph Neural Networks for Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-1300-9\_42.pdf)

46. [SSM-Crossformer: Attention Mechanisms Based on Spatial-Spectral Masking in Crossformer for Multidimensional Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-08820-8\_7.pdf)

47. [PAPT: Periodic-Aware Transformer with Polynomial Trend Fitting for General Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0375-8\_1.pdf)

48. [MKAT: Mutual Kolmogorov-Arnold Transformer for Reliable Information Flow in Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-3-032-37676-3\_27.pdf)

49. [PyraTSG: A Hierarchical Scale Transition Framework for Dependency Modeling in Multivariate Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0372-7\_16.pdf)

50. [DPC-Net: A Decouple-Predict-Correct Framework for Long-Term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-0366-6\_40.pdf)

51. [DMNet: Dual-Decomposition and Multi-frequency Differentiated Learning Architecture for Long-term Time Series Forecasting](https://link.springer.com/content/pdf/10.1007/978-981-92-3438-7\_35.pdf)

52. [EC-Flow: Entropy-Enhanced Contrastive Learning for Fixed-Point Constrained Time-Series Flow Matching](https://link.springer.com/content/pdf/10.1007/978-3-032-37676-3\_17.pdf)

53. [MoE-TAPE: Mixture of Experts with Task-Adaptive Parameter-Efficient Module for Time Series Representation Learning](https://link.springer.com/content/pdf/10.1007/978-981-92-3551-3\_23.pdf)

54. [NeuroCEA: Cross-Domain Embedding Augmentation with Contrastive Knowledge Distillation for Electrophysiological Time Series Representation](https://link.springer.com/content/pdf/10.1007/978-981-92-0369-7\_15.pdf)

I'm planning to apply to UBC for a Bachelor of Science in Computer Science and I'm trying to decide whether I should prefer the Vancouver campus or Okanagan campus.

I'm particularly interested in hearing from current students, graduates, or people who have experience with both campuses, especially CS students.

I'm wondering about the actual differences between UBC Vancouver and UBC Okanagan in terms of:

• Quality and difficulty of the Computer Science education
• Professors and access to them
• Co-op and internship opportunities
• Access to tech companies and industry
• Recruiting and networking opportunities
• On-campus CS clubs, projects, hackathons, etc.
• Career outcomes after graduation
• Whether employers actually care which UBC campus you graduated from
• Opportunities to get into companies such as Amazon, Microsoft, Google, Apple, Shopify, etc.
• Whether Vancouver provides a significant advantage for getting internships/new-grad jobs compared with UBCO
• Research opportunities, if relevant

For someone whose main goal is to build a career in software engineering/tech rather than academia, how significant is the difference actually?

Thanks in advance to everyone who takes the time to share their experience and advice. I really appreciate it!

Hello all!! I'm in cégep right now, but I'm stressing a bit on what I should even in the future. What careers can a bachelors in math offer me?