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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 Completed | Notes | 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 | ||||||||||||||||||||||||

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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. | ||||||||||||||||||||||||

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27 | Probability Videos | ||||||||||||||||||||||||

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29 | Dependent probability introduction | ||||||||||||||||||||||||

30 | Independent & dependent probability | ||||||||||||||||||||||||

31 | Independent Problems | ||||||||||||||||||||||||

32 | Conditional Prob Article | ||||||||||||||||||||||||

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34 | Probability Quiz | ||||||||||||||||||||||||

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36 | Probability & Statistics — Set 6 | ||||||||||||||||||||||||

37 | Probability & Statistics — Set 2 | ||||||||||||||||||||||||

38 | Independent Probability | ||||||||||||||||||||||||

39 | Dependent Probability | ||||||||||||||||||||||||

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41 | Probability Interview Questions | ||||||||||||||||||||||||

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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? | ||||||||||||||||||||||||

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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 | ||||||||||||||||||||||||

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53 | Statistics Concepts | ||||||||||||||||||||||||

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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 | ||||||||||||||||||||||||

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81 | Statistics Post-Quiz | ||||||||||||||||||||||||

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83 | Data Science Probability Statistics 17 | ||||||||||||||||||||||||

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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. | ||||||||||||||||||||||||

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88 | Product And Experiment Design Concepts | ||||||||||||||||||||||||

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90 | User Engagement Metrics | ||||||||||||||||||||||||

91 | Data Scientist’s Toolbox: Experimental Design -Video | ||||||||||||||||||||||||

92 | A/B Testing Guide | ||||||||||||||||||||||||

93 | 6 Themes Of Metrics | ||||||||||||||||||||||||

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95 | Product And Metrics Questions | ||||||||||||||||||||||||

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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: |

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