Data Science Interview Guide
 Share
The version of the browser you are using is no longer supported. Please upgrade to a supported browser.Dismiss

View only
 
ABCDEFGHIJKLMNOPQRSTUVWXY
1
Sign Up For Future Free Content, Videos, Etc
2
Intro
3
This is a data science study guide that you can use to help prepare yourself for your interview. This was developed by people who have interviewed and gotten jobs at Amazon, Facebook, Capital One and several other tech companies. We hope these help you get great jobs as well.

In order to use this, you can make a copy of this sheet and follow along with the study guide. Keeping track helps you know where you are and how you are doing.


Date CompletedNotes
Personal Difficulty 1-5
4
Machine Learning Algorithms
5
Logistic Regression — Video
6
A/B Testing? — Video
7
Decision Tree — Post
8
SVM — Post
9
How SVM — Video
10
Principal Component Analysis: PCA — post
11
Principal Component Analysis — Video
12
Adaboost — Post
13
AdaBoost — Video
14
A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning — Post
15
Gradient Boost Part 1: Regression Main Ideas — Video
16
K-Means Clustering — The Math of Intelligence — Video
17
Bayesian Network — Post
18
Neural Network — Post
19
Dimensionality reduction algorithms — Post
20
How kNN algorithm works — Video
21
22
23
Probability And Statistics
24
A common question you might get at FAANG companies and other tech companies alike is the occasional probability or statistics question. The questions won’t necessarily require complex math. However, if you haven’t thought about independent and dependent probabilities in while. It is good to review setting up the basic formulas.
25
26
27
Probability Videos
28
29
Dependent probability introduction
30
Independent & dependent probability
31
Independent Problems
32
Conditional Prob Article
33
34
Probability Quiz
35
36
Probability & Statistics — Set 6
37
Probability & Statistics — Set 2
38
Independent Probability
39
Dependent Probability
40
41
Probability Interview Questions
42
43
A die is rolled twice. What is the probability of showing a 3 on the first roll and an odd number on the second roll?
44
In any 15-minute interval, there is a 20% probability that you will see at least one shooting star. What is the proba­bility that you see at least one shooting star in the period of an hour?
45
Alice has 2 kids and one of them is a girl. What is the probability that the other child is also a girl? You can assume that there are an equal number of males and females in the world.
46
You’re about to get on a plane to Seattle. You want to know
47
How many ways can you split 12 people into 3 teams of 4?
48
49
Statistics Pre-Quizzes
50
Statistics is a broad concept so don't get too bogged down in the details of each of these videos. Instead, just make sure you can explain each of these concepts at the surface level.
51
Data Science Probability Statistics 14
52
53
Statistics Concepts
54
55
Bias Variance Trade Off
56
Confusion Matrix
57
ROC curve
58
Normal Distribution
59
The Normal Approximation to the Binomial Distribution
60
P-Value
61
Naive Bayes
62
Normal distribution problem: z-scores (from ck12.org)
63
Continuous Probability Distributions
64
Standardizing Normally Distributed Random Variables (fast version)
65
Statistics 101: Simple Linear Regression, The Very Basics
66
Statistics 101: Linear Regression, Outliers and Influential Observations
67
Statistics 101: ANOVA, A Visual Introduction
68
Statistics 101: Multiple Regression, The Very Basics
69
Statistics: Variance of a population | Probability and Statistics | Khan Academy
70
Expected Value: E(X)
71
Law of large numbers | Probability and Statistics | Khan Academy
72
Central limit theorem | Inferential statistics | Probability and Statistics | Khan Academy
73
Margin of error 1 | Inferential statistics | Probability and Statistics | Khan Academy
74
Margin of error 2 | Inferential statistics | Probability and Statistics | Khan Academy
75
Hypothesis testing and p-values | Inferential statistics | Probability and Statistics | Khan Academy
76
One-tailed and two-tailed tests | Inferential statistics | Probability and Statistics | Khan Academy
77
Type 1 errors | Inferential statistics | Probability and Statistics | Khan Academy
78
Large sample proportion hypothesis testing | Probability and Statistics | Khan Academy
79
Boosting and Bagging
80
81
Statistics Post-Quiz
82
83
Data Science Probability Statistics 17
84
85
Product And Experiment Designs
86
Product sense is an important skill for data scientists. Knowing what to measure on new products and why can help determine whether a product is doing well or not. The funny thing is, sometimes metrics going the way you want them to might not always be good. Sometimes the reason people are spending more time on your website is because webpages might be taking longer to load or other similar problems. This is why metrics are tricky and what you measure is important.
87
88
Product And Experiment Design Concepts
89
90
User Engagement Metrics
91
Data Scientist’s Toolbox: Experimental Design -Video
92
A/B Testing Guide
93
6 Themes Of Metrics
94
95
Product And Metrics Questions
96
97
An important metric goes down, how would you dig into the causes?
98
What metrics would you use to quantify the success of youtube ads (this could also be extended to other products like Snapchat filters, twitter live-streaming, fort-nite new features, etc)
99
How do you measure the success or failure of a product/product feature
100
Google has released a new version of their search algorithm, for which they used A/B testing. During the testing process, engineers realized that the new algorithm was not implemented correctly and returned less relevant results. Two things happened during testing:
Loading...