Always customize your application materials to the job you’re applying to.
The first people who read your application may be HR recruiters with no expertise in the job you’re applying for so write your application accordingly. Alternatively, it could be an AI filtering software so make sure to include any important keywords.
The first people who read your application may be HR recruiters who are grumpy baby boomers who care deeply about “professionalism”. Make sure there is nothing on your job application that is unprofessional. This includes, but is not limited to, typos & grammar and GitHub handles or usernames that are anything other than a variation on your name (note you don’t need to change your GitHub handle for this class). Furthermore, make sure there is nothing on your social media that could be disqualifying, like pictures or videos of you partying/drinking.
Be prepared to discuss anything on your resume in a job interview.
Jobs care A LOT about communication and teamwork skills. Anything you can do emphasize your skills in this area is worth including in your application. Make sure that all of your application materials have ZERO typos and grammatical errors.
DON’T GO MORE THAN 1 PAGE. You should only ever go more than 1 page if you have done a lot of stuff. As college students, you haven’t… It may feel like you have… but you haven’t. Going over 1 page is a good way to get your application thrown out without being read.
CUSTOMIZE YOUR RESUME TO THE JOB AD!
Rename “Skills” as “Technical Skills” and indicate level of proficiency next to each. You can include programming languages (e.g. R (Proficient)) and major packages (e.g. tidyverse (proficient)). It is OK to be a A LITTLE BIT optimistic about your skills. However, DO NOT LIE. If you claim you’re proficient they may put you through a coding interview. A good rule of thumb is to ask yourself “What level of proficiency could I reach with one weekend of HARD studying?”
Add a link to your personal website and GitHub to your resume.
Don’t list relevant coursework near the top but if you have experience with certain content that you think is important (e.g. Linear Regression, K-Nearest Neighbors, Cross-Validation, etc.) include it in your Skills section.
Your resume may first go through an AI filter or be reviewed by an HR hiring manager without expertise in the field you’re applying for. Make sure you include important key words in your resume that match what’s in the job description. For example, if the job ad says they want expertise in tidymodels, say tidymodels in your resume. Make sure that all of your application materials have ZERO typos and grammatical errors. Pat from HR may not know what cross-validation is, but they sure know what a period is.
Think of the “preferred qualifications” as a wishlist rather than a list of necessary skills. Remember… you miss 100% of the shots you don’t take.
When possible, quantify your achievements. An example from my old resume (emphasis added): “Devised cache replacement policy for Video-on-Demand settings which takes advantage of”chunked” files and achieves a 32% improvement install duration compared to the LRU policy; work published in peer-reviewed article.”
If you don’t have a lot of Experience, include a Projects section. Upload your project to GitHub or your website and link to them from your resume.
Always save your resume as a PDF. (1) You never know what software they will be using to view your resume and you want it to ALWAYS looks the same. (2) You don’t want anyone to accidentally edit it.
Remember that people may not be carefully be reading your cover letter from top to bottom. Design your cover letter so that it still makes sense if someone skims it.
You can use bold, italicized, or underlined text to highlight or emphasize important skills. Don’t overdo it though. If you feel like you’ve done too much, you probably have.
Sample Analysis
While not required, I recommend you start with something you’ve been working on
If you have a side project, use may use that.
If not, I recommend starting with one of your homeworks.
Your data analysis should do the following
Tell a coherent story and narrative. If you start with a homework, make sure you take out all of my narrative and the exercise headings.
Include something similar to the following sections:
Introduction outlining and motivating the problem
Exploratory data analysis and pre-process/feature engineering
Modeling or Model Selection
Performance Analysis
Conclusion summarizing your results
Strike a balance between addressing all of the relevant technical qualifications listed in the job ad without feeling like you’re just doing a bunch of unnecessary a non-relevant work. Keep in mind that it is not just important that you know HOW to apply different machine learning methods, but understand WHEN apply different techniques to data. Imagine that you’re being hired to be a construction worker. If you are asked to hammer in a nail, don’t take out a sledgehammer.
Show that you are able to communicate well. Communicating well involves ensuring that you incorporate the appropriate level of detail (not too much/not too little) and that you are able to interpret the results of your analyses. It goes beyond just having perfect grammar and spelling.
Pro-tip: Write up a draft of your report and then go through it once taking out anything that isn’t absolutely necessary.
Be professional and be grammatically correct and devoid of typos.
Things to avoid:
Walls of code without context. You should be explaining what you do every time you write code. However, don’t put too much detail. Assume the reader is familiar with machine learning. For example, say “We now fit the logistic regression model to the training data.” instead of “A logistic regression model is a machine learning model that blah, blah, blah”.
Walls of data without context. Sometimes you may want to print out data for yourself to make sure some analysis step was done correctly. You don’t need to include that in your final report. Make sure that anything you display is done with purpose and that you’re talking about that purpose.
Talking about functions rather than what code is doing. For example, say “We now fit the logistic regression model to the training data.” instead of “We not use the fit function to fit the logistic regression model to the training data.”
Ensure all plots have professional axis labels.
When talking about your variables, mostly refer to them by what they represent rather than their name. For example say “We will be predicting whether a customer will default on their loan.” instead of “We will be using default as the response variable. There are situation in which it is appropriate to refer to them by their names in R but be careful.
Including a bunch of unnecessary analysis steps to prove that you can do them. If the project you’ve chosen does not call of more advanced analysis steps, choose a different one.