How to standout as an entry level data scientist candidate

Today I found a nice post in LinkedIn: How to Stand Out as an Entry-Level Data Scientist Candidate. I liked it very much, and I think it contains pretty much true points. Here they are.

  1. Software engineering skills
  2. Good GitHub projects, writing production-level code
  3. Be able to deeply understand commonly used ML algorithms AND to explain them to a laymen:
    • Linear regression
    • Logistic regression
    • SVM
    • Random forest
    • Boosting
    • K-means
    • KNN
    • PCA
    • Collaborative filtering
  4. Be able to compare and contrast commonly used ML algorithms, explaining their relative pros and cons
  5. Be able to walk through the business value of every project they worked on
  6. Be able to complete every exercise from Cracking the Coding Interview in Python, writing clear, efficient code
  7. High ranking on a public Kaggle board (top 3%)