Interview Topics Flashcards
1
Q
Probability and statistics
A
Conditional probability (Bayes' Theorem) Probability distribution Hypothesis testing (null hypothesis, p-values, confidence intervals) Covariance and correlation
2
Q
Machine Learning
A
Supervised Learning (Linear Regression, k-NN, SVM, Random Forest, Gradient Boosting) Unsupervised Learning (k-means, hierarchical clustering) Deep Learning General Predictive Modelling (Choosing the right evaluation metrics, train and test sets, cross-validation)
3
Q
Computer science
A
Coding (Python/R)
Data Structures (Lists, Hash Tables, Stacks, Queues, Trees, Graphs)
Algorithms (searching, sorting, graph traversals)
Databases (SQL, noSQL)
Distributed computing (mapReduce, spark, Hadoop)
4
Q
Data Engineering
A
Data Wrangling, Cleaning and Visualisation
Feature Engineering
5
Q
Domain Knowledge
A
Knowledge of the industry, key metrics of the field
6
Q
Behavioural and “Fit”
A
Alignment to company culture
Teamwork: Worked with someone different to me
Ability to Adapt: Lots of pressure, when you failed and how you dealt with that
Communication: Time you successfully persuaded someone at work. Technical concept to a non-technical audience
7
Q
STAR
A
Situation
Task
Action
Result
