This year I have decided to follow #bookamonthchallenge. Not just that, I have also taken a decision to read data books in order to expand my learning horizons in the field related to data.
First, let me introduce to you some background on how I came across the book. I am an ardent follower of Daliana Liu’s LinkedIn posts and her youtube talk show. In one of her posts, I noticed she spoke about the book and her talk show with Nick Singh on the book. After listening to the video, I decided to purchase the book. Although the book was a bit expensive, what better investment than investing in books, right?
Now, let's dive into the review of the book:
The book consists of 11 Chapters wherein each chapter focus on fundamental concepts and real interview questions.
Chapter 1 to Chapter 4 focus on career advice which includes 4 resume principles to live by, how to make kick-ass portfolio projects, cold email your way to your dream job, ace the behavioral interview.
Chapter 1: 4 Resume Principles to Live by for Data Scientists
This chapter is the most essential chapter as this chapter focuses on what should go onto your resume or not. The authors have listed 4 principles that provide tips on how recruiters and hiring managers evaluate resumes. The great thing about this interview is that the authors have provided their own resume as an example to shed the light on each point written.
Chapter 2: How to Make Kick-Ass Portfolio Projects
Why create a portfolio of projects? Because that's the only way to show your future employers some real data science experience. Yes, these chapters tell more significance on portfolio projects and how to build them. This chapter lists about 6 tips on what is being expected by future employers. Most of the tips are about the “show” and not the “tell” method.
Chapter 3: Cold Email Your Way to Your Dream Job in Data
What I liked about this book is that not every book covers this topic. Many job seekers get stuck in this phase in order to get an interview. This chapter tells us exactly what needs to be done when you have no connections and referrals to get that first interview for your dream job. The authors have listed 8 tips for writing effective cold emails along with a few successful examples that can be used as a guide.
Chapter 4: Ace the Behavioral Interview
When we think about data science interviews we only think about how to ace technical concepts. Although they are important, one must not forget the importance of behavioral interview questions. Why? Because, if not prepared this will definitely screw your entire interview performance. Another point to be noted is that these interview questions do not have a right or wrong answer and so being prepared is essential to ace this round.
The book gives us a framework to answer the world's most asked behavioral question “Tell me about yourself”. The book also covers how to approach some most commonly asked questions using the STAR method (Situation, Task, Action, Result) with a real-world example.
Chapter 5: Probability
This chapter is solely for probability concepts. The below fundamental concepts are revised in this chapter along with the real-world interview questions.
Conditional Probability
Law of Total Probability
Counting
Random Variables
Joint, Marginal, and Conditional Probability Distributions
Probability Distributions
Discrete Probability Distributions
Continuous Probability Distributions
Markov Chains
These concepts are revised at a basic level and more in-depth questions are covered in the next chapter. There are 30 exercises questions are classified into Easy, Medium, and Hard questions along with the company name. Overall, I personally would solve these questions in order to not forget the basic concepts from time to time.
How I attempt:
I would go about attempting the interview ‘easy’ questions by myself and refer to the solutions. However, I may have to create flashcards on tips and definitions on various distributions for future references. The medium and hard questions are really TOUGH and I would learn how to approach questions by referring to the solutions.
Chapter 6: Statistics
Statistics is an important concept that has frequently been tested in most companies. Having a separate chapter dedicated to this concept is no surprise to me, however, I was surprised to see is that concepts are elaborated in terms of mathematical derivations. Below are the concepts covered:
Law of Large Numbers
Central Limit Theorem
Hypothesis Testing
Test Statistics
Hypothesis Testing for Population Proportions
p-values and Confidence Intervals
Type I and Type II Errors
MLE and MAP
There are about 40 real interview questions to be practiced and learned.
How I attempt:
While I am writing this post, I have already attempted. The easier questions are something I could answer right off the bat however the medium & hard questions were TOUGH. Hence, I had to refer to each solution and learn from the derivation.
Chapter 7: Machine Learning
What I liked about the chapter is that the authors have taken an effort to clear the misconception of “not knowing deep learning would tank their performance”. The reality is that most data scientists are hired to solve business problems — not complicated neural network models.
If you aim at ML Engineering roles, then this book will come in handy too as the hard questions will give a tough match to the ML practitioners as well. The chapter starts off with 3 ways the ML interviews can be asked:
Conceptual questions
Resume-driven questions
End-to-end modeling questions
A successful candidate must know the ways to approach such questions.
Below are the concepts revised in this chapter:
Linear Algebra
Gradient Descent
Model Evaluation and Selection
Bias-Variance Trade-off
Model Complexity and Overfitting
Regularization
Interpretability & Explainability
Model Training
Cross-Validation
Bootstrapping and Bagging
Hyperparameter Tuning
Training Times and Learning Curves
Linear Regression, Classification, Random Forests
Dimensionality Reduction, Clustering
Neural Network, Perceptron, Backpropagation
Reinforcement Learning
End-to-end ML Workflow
All of these concepts are revisited in depth along with real interview questions which are categorized as Easy, Medium, and Hard.
How I attempt:
There is no easy way to attempt this chapter. I felt this chapter to be the heart and soul of this book although other chapters are equally important. I would try creating flashcards on these topics covered and attempt the questions based on my knowledge at the time.
Chapter 8: SQL & DB Design
As a data scientist, a great amount of your work revolves around working with data and manipulating data and that’s what is exactly mentioned in this chapter. The authors have discussed how the SQL interview questions are asked, tips for solving them, Basic SQL commands, and Advance SQL commands.
Basic Commands:
CREATE TABLE, INSERT, UPDATE, DELETE, SELECT, GROUP BY, WHERE, HAVING, ORDER BY, DISTINCT, UNION
Joins
Advance SQL Commands:
Aggregation
Filtering
Common Table Expressions and Sub-queries
Window Functions: LAG and LEAD, RANK
The authors have also emphasized database systems such as which columns should be indexed for rapid lookup of data.
How I attempt:
SQL is a language that I work on in daily life, hence I would go about solving the problems as I attempt and refer to the solutions if there are any ways to improvise or optimize the solution.
Chapter 9: Coding
Personally, I felt this is the most important chapter as every data science interview is composed of a coding round. This chapter covers algorithms to be kept in mind while solving problems as well as their run time & space complexity.
Topics covered:
Data Structures: Arrays, Linked Lists, Stacks & Queues, Hashmaps, Binary Search Trees, Heaps, and Graphs
Algorithms: Recursion, Greedy algorithms, Dynamic programming
Three main commonly asked (Mergesort, Quicksort, Matrix multiplication) techniques are discussed in this chapter as well. At the end of the chapter, there are about 30 interview questions that are categorized as Easy, Medium, and Hard questions.
How I attempt:
I would go about grasping concepts related to data structures and their related time& space complexity. At the same time, while I practice the questions, initially I would refer the solutions and then try attempting other questions based on the idea being revised.
Chapter 10: Product Sense
Having a reliable understanding of the product features, metrics and framework can be very helpful when interviewing at product company interviews. Ways to build your product and business sense are discussed in this chapter.
Below are the topics covered in this chapter:
Framework for Approaching Product Interview Questions
Metrics for product & case interviews
3-step framework to answer product metrics definition questions
4step framework for diagnosing metric changes
Assessing metric trade-offs
A/B testing & experimental design
There are 18 product interview questions discussed with solutions in this chapter.
How I attempt:
As I am still consuming the concepts/framework from this chapter. I would go about incorporating the daily habits to improve my product/business sense about products in my daily life. This way, I wouldn’t be someone with no prior knowledge about the industry and would help expand my learning horizon.
Chapter 11: Case Studies
This section is also mostly asked interview round in a data science interview. Either it can be a form of case study interview round or take-home data science challenge. Either way this chapter.
Most of the approaches for solving a case study interview question are covered in chapter 10. However, there are about 8 case study interview questions that also have solutions with multiple follow-up questions.
How I attempt:
As a reader myself, I would attempt taking up a case study question and try asking follow-up questions myself using the concepts grasped from Chapter 10 and try solving the questions rather than looking up for solutions right away.
Thoughts
My thoughts about the book are:
Should be named the “Must-have” books which every data scientist (aspiring or experienced).
It’s a holy grail for cracking data science interviews.
Although the book is expensive by Indian standards, I would definitely recommend it as the book is worth every penny spent.
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