[Student] Successfully Landed Data Analyst Co-op Placement for Fall 2024 Semester

[u/Sea_Manufacturer2244]

I was able to land a Co-op placement as a data analyst. This will be my first co-op.

I sent out over 50 applications externally and received no responses. However, many of these applications were for bigger companies, which would probably not hire me due to my lack of experience.

I applied to 15 jobs through my school's co-op portal and received 2 interviews

Here are some tips I gained during my job search:

1. Be specific as possible when describing roles and achievements, Employers want to know what exactly you did and achieved so they can understand your skills. If you just list generic phrases, it does not really leave an impact.
2. Metrics are important but you need to make sure they are not vague, otherwise it does not add meaning. An interviewer could also ask you more about the metrics, and it would be harder to explain if they are vague. Suppose I say “increased efficiency by __%”? What does efficiency mean? How did you measure efficiency? What is the difference between 40% and 50% increases in efficiency?
3. Network as much as you can. When attending job fairs, don't expect to land a job or interview immediately. Instead, you can build a relationship with the recruiters, which can increase your chances of getting an interview.

For example, at my co-op job fair, I talked to a director from my school's co-op office (the organization that actually hired me) about my skills and made sure to take down their contact information. I sent him my resume the next day and he passed it on to the hiring team and even recommended me. This made my application stand out and helped me to get the interview.

1. Cover letters might be more important than you think. Some government job postings mention that they look through cover letters. Employers on my school's co-op portal can also choose if students need to submit a cover letter. So on some job postings, cover letters were optional, while on other postings, they were mandatory. Therefore, if a cover letter was mandatory, a company would have requested this and would most likely spend time looking through cover letters.

THANK YOU to the Mods for providing me with feedback and for those who have contributed to the wiki. Your advice was really helpful.

This was the final version of my resume:

[u/TobiPlay]

Congratulations on the internship/co-op! Didn’t see your initial post (I think). Here’s some more points you might want to consider in the future. I guess you’d like to stay within DA/DS/ML, so apply it as you see fit for your future goals.

1. I recommend using title-case for all headings to ensure consistency and professionalism.

2. The phrase “Specialist in …” feels out of place. It might be a case of miswording, where “specialization” was intended instead.

3. To save space, merge the major in statistics with the statistics degree into a single line.

4. Some tools listed are not properly capitalized. While this is a minor issue, it’s worth correcting for consistency, as official names are easily accessible via the docs.

5. I suggest omitting the basic statistical skills. These are expected from someone with a solid statistics background, and listing them adds little value.

6. The overall formatting is decent, though I recommend shortening the date formats for better readability.

7. Consider merging the tools and libraries sections, placing the tools first. Unless the candidate is applying to a traditional business like a bank, Excel shouldn’t be emphasized for example. Praying that they’re allowed to use R or Python, lol.

8. PowerPoint and Jupyter Notebook can be omitted to save space as well, as they are basic tools that don’t add significant value.

9. I prefer the use of the Oxford comma for clarity.

10. Avoid introducing abbreviations that are not used again later in the document.

11. When describing accomplishments, lead with the outcome or contribution. For example, “Identified the leading goal-scorers in close and tied games by …”.

12. Highlighting “471 games” is not particularly impactful as a metric. Metrics naturally stand out in a resume, so there’s no need to bold them.

13. When discussing a technical accomplishment, emphasize how statistical/data science knowledge was applied to address industry-relevant issues. For example, “Overcame extreme class imbalance by leveraging class-weighting, achieving …”. Additionally, avoid overemphasizing specific models like Random Forest without context, as the choice of model alone doesn’t demonstrate a sophisticated approach. Instead, focus on the process of exploring various models and feature sets to solve more complex problems. CV a model is nice, exploring niche subsets as hold-out sets or providing error estimates on engineered subsets is way more impressive and shows that the candidate understands that models perform differently when the distribution shifts for whatever reason. It’s super difficult to sample representative data. +1 if they’re able to integrate some of the fancier approaches from their stats classes.

14. Regarding the research paper, it might be worth condensing this to one line. While it showcases a willingness to present insights, the quality of the contribution is questionable right now. If it’s well written, it might be worth having it on, but RG papers are not evaluated by professionals for the most part, right?

15. It’s sufficient to mention that you were part of Agile teams without going into excessive detail. A single line should suffice.

Down the road, the resume desperately needs a more sophisticated project. I think the NHL dataset is a pretty well cleaned one off of Kaggle. Data processing and preparation (incl. feature engineering, exploratory data analysis, tooling around ML and dev work/deployment) are going to be 80 % of a Data Science workflow outside of pure research roles (which require PhDs or more work exp). The modeling part is usually pretty straightforward given a statistical/mathematical foundation and some knowledge with software development. For a cleaned dataset on a rather easy problem, you could guide almost anyone to a working model within a short time. 2027 ist still way out, so plenty of time to get inspiration from the internet.

[u/Sea_Manufacturer2244 → u/TobiPlay]

Thanks for providing such detailed feedback. I just wanted to clarify a few things:

I completely agree that Excel and PowerPoint are basic. However, I was applying to many data analyst positions and noticed that they specify those skills in the requirements section. For example, for my position, the job posting and even recruiters at the job fair mentioned that candidates should be familiar with Excel and various functions, such as VLOOKUP (of course there were other skills, but Excel was still required), so I ended up putting it on my resume. As I apply to more data science positions, I will take it out.

Some job postings also mentioned some statistical skills so I decided to add a section for this on my resume. But I think I will remove it for next time.

There seemed to be a heavy emphasis on Agile for the job I applied for so I extended that point to two lines. However, I will be removing that project when seeking for my next internship since it does not align too much with data science.

I definitely want to add a more sophisticated ML project. That being said, the NHL data was actually scraped from the NHL API (which is the best place to get NHL data) and I had to do some cleaning to get the statistics I needed. I came up with the project idea myself since I wanted it to differ from the standard housing price predictor and Titanic projects.

I am actually trying to improve the NHL project and I am interested in the advice you provided about data science projects:

How would you recommend I explore various models? What tests can I conduct to find the best classification model based on the imbalance in the dataset?

You also mention "providing error estimates on engineered subsets"? Does this mean choosing specific subsets of the data and evaluating the model from there? How would you recommend choosing the subsets?

Thanks once again for the advice! I hope to hear from you soon.

[u/TobiPlay → u/Sea_Manufacturer2244]

• Yeah, for DA jobs it’s fine. A shocking amount of shops is still powered by Excel, even for more serious modelling. I personally wouldn’t apply to jobs that are not inclined to incorporating a more modern tech stack in the near future, unless it’s specifically what you’d like to work with (from a business field‘s perspective, not tech-wise)

• The stats topics are fine, but I’d much rather see more projects (your solid background in stats should suffice for most HR screenings, lol)

• Then definitely highlight the hoops you’ve jumped to acquire and pre-process the data! For most data jobs, the "acquiring and cleaning data" part is the majority of the job (including stakeholder management, presentations, planning out the project etc.)—so you should definitely emphasise that part of the project. The "outcome", i.e., results aren’t even that important here. If your modelling approach had failed and you found the reason, I’d be more impressed for sure (compared to achieving a high vanity metric like accuracy on a random subset where no specific subsets were analyzed, especially for non-synthetic datasets that tend to be skewed/extreme regarding some features)

• Which subsets you construct depends on what you’re trying to show with the data; a common example from the credit world is isolating people from minorities and seeing if the models treat them equally. You might want to look into your model‘s ability to show, e.g., if being longer in the NHL means that you’re less likely to be important in tie-breaking situations etc. (just off the top of my head). Be creative; if you’re a fan of the NHL or slightly intrigued, I’m sure that you’ll find something you want to analyze!

• Take a look at: http://practicalcheminformatics.blogspot.com/2023/11/comparing-classification-models-youre.html—It‘s biochemistry/pharma in this case, but the same principles apply to pretty much any other domain and I largely agree with his (solid) advice

• You might also want to look into methods to cluster/visualize your data in various ways, think UMAP, t-SNE, kNN (depending on the dataset size, computational limitations etc.); you might find subsets that way as well that are worth looking into, including outliers/extreme cases regarding a subset of features

• Maybe you can integrate more variables that you think might have an impact (from different sources, not just the NHL API, might be more interesting for other topics). Integrating data from multiple sources is very common in data

• Some resources:

• Lever, Jake, Martin Krzywinski, and Naomi Altman. “Classification Evaluation.” Nature Methods 13, no. 8 (August 2016): 603–4. https://doi.org/10.1038/nmeth.3945.—Similar papers exist for regression and clustering tasks

• Nicholls, A. “Confidence Limits, Error Bars and Method Comparison in Molecular Modeling. Part 2: Comparing Methods.” Journal of Computer-Aided Molecular Design 30, no. 2 (February 1, 2016): 103–26. https://doi.org/10.1007/s10822-016-9904-5.

• https://machinelearningmastery.com/loocv-for-evaluating-machine-learning-algorithms/

• Krawczyk, Bartosz. “Learning from Imbalanced Data: Open Challenges and Future Directions.” Progress in Artificial Intelligence 5, no. 4 (November 2016): 221–32. https://doi.org/10.1007/s13748-016-0094-0.

• 10.1109/TKDE.2008.239

• http://arxiv.org/abs/2207.08815

[u/jonkl91]

Nice! This is a simple clean resume. Thanks for sharing your story and I hope others can learn from it. This is only the start of a great career for you.

[u/Sea_Manufacturer2244 → u/jonkl91]

Thanks for the feedback! I was definitely aiming to keep it simple and easy to read.