Department toolkit

The Data Science Student Toolkit

Everything to set up before you apply — in the order analytics teams actually check it.

Why this order, for data science & analytics

In data science and analytics, hiring teams want to see how you think with real data, not just a list of tools. A project with a genuine dataset, a clear question, and a written explanation of what you found does more than naming a library ever will.

That's why this path leads with a portfolio built around real analysis — notebooks and readmes that show the reasoning — before moving to the resume language that gets an analytical background past the first screen, then the accounts that round out the profile.

  1. 1Set up GitHub so your code speaks for youNotebooks with a written narrative — what question you asked, what the data showed, what you'd check next — matter more here than clean code alone; the story around the analysis is the evidence.
  2. 2A simple personal site you can stand behindA short write-up linking your notebooks with a plain-language summary of each project's finding lets a reviewer skim results without opening every file.
  3. 3A one-page resume structure that worksAnalytics resumes read best organized around questions answered and decisions informed, with the tools — SQL, Python, a visualization tool — named plainly alongside each one.
  4. 4How applicant tracking systems actually read your resumeAnalytics postings filter on specific tool and method names — matching the posting's own vocabulary for your tools and techniques is what clears the first automated pass.
  5. 5Set up LinkedIn like a professionalA headline naming your track — analyst, data engineer, applied science — helps recruiters searching by specialty find you among many generalist 'data' titles.
  6. 6Set up ChatGPT for study and job prepTreat an AI assistant as a second pair of eyes on your analysis — useful for exploring an approach, but the written interpretation of what the data means still has to be yours.
  7. 7Set up LinkedIn Jobs and let openings find youSetting alerts for both 'analyst' and 'scientist' titles catches roles that use different language for very similar analytical work.

Before you apply — the checklist

  • GitHub or notebook host has 2-3 projects with real data and a written summary of findings
  • Each project's readme explains the question, the approach, and what you found
  • Resume organized around questions answered and decisions informed, not just tools listed
  • Resume saved as PDF, named FirstLast_Resume
  • LinkedIn headline names your specific track (analyst, engineer, scientist)
  • Job alerts set for multiple analytical job titles, not just one
  • Know where you stand: your free Knock Number
Print the checklist (PDF)

All toolkit guides

The Data Science Student Toolkit · Knock