Data Science Interview Course

Nail Data Science interviews at FAANG and Tier-1 Tech Companies
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Course designed and taught by instructors from FAANG & Tier-1 Tech Companies

Andrew Treadway

Research Data Scientist
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Qiuping Xu.

Principal Data Scientist
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Bhuvan Venkatesh

Data Scientist
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Data Science Course Curriculum

This is what you'll learn in our Data Science career path!

  • 15 Mock Interviews
  • 6-Month Support Period

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Data Science course and curriculum

Data structures and Algorithms
5 weeks
5 live classes
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

Sorting

  • Introduction to Sorting
  • Basics of Asymptotic Analysis and Worst Case & Average Case Analysis
  • Different Sorting Algorithms and their comparison
  • Algorithm paradigms like Divide & Conquer, Decrease & Conquer, Transform & Conquer
  • Presorting
  • Extensions of Merge Sort, Quick Sort, Heap Sort
  • Common sorting-related coding interview problems
2

Recursion

  • Recursion as a Lazy Manager's Strategy
  • Recursive Mathematical Functions
  • Combinatorial Enumeration
  • Backtracking
  • Exhaustive Enumeration & General Template
  • Common recursion- and backtracking-related coding interview problems
3

Trees

  • Dictionaries & Sets, Hash Tables 
  • Modeling data as Binary Trees and Binary Search Tree and performing different operations over them
  • Tree Traversals and Constructions 
  • BFS Coding Patterns
  • DFS Coding Patterns
  • Tree Construction from its traversals 
  • Common trees-related coding interview problems
4

Graphs

  • Overview of Graphs
  • Problem definition of the 7 Bridges of Konigsberg and its connection with Graph theory
  • What is a graph, and when do you model a problem as a Graph?
  • How to store a Graph in memory (Adjacency Lists, Adjacency Matrices, Adjacency Maps)
  • Graphs traversal: BFS and DFS, BFS Tree, DFS stack-based implementation
  • A general template to solve any problems modeled as Graphs
  • Graphs in Interviews
  • Common graphs-related coding interview problems
5

Dynamic Programming

  • Dynamic Programming Introduction
  • Modeling problems as recursive mathematical functions
  • Detecting overlapping subproblems
  • Top-down Memorization
  • Bottom-up Tabulation
  • Optimizing Bottom-up Tabulation
  • Common DP-related coding interview problems

Data Science
7 weeks
7 live classes
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

SQL Programming (interview-focused concepts and questions)

  • Derive business insights for a food delivery app by writing SQL queries
  • Comprehensive coverage of topics from intermediate-level concepts such as Case Statements and subqueries to advanced SQL functions such as joins and analytical functions
  • Application of window functions as lead, lag functions to evaluate day-over-day insight on business performance
  • Use rank and dense rank functions to understand merchants’ reach in the market
  • Complex SQL problems on customer-merchant pairwise dependence using a variety of functions and operators
  • Deep dive into joins, their type, and comparison of left join vs. right join vs. outer join vs. broadcast join
  • Thematic coverage of frequently asked interview problems through template problems
  • A step-by-step guide to what you can expect in an interview and how to tackle them in a time-constrained environment
2

Probability

  • Challenging combinatorial probability questions involving coin tosses, dice throws, cards, and balls (popular in FAANG+ interviews)
  • Dealing with bias: Given an outcome, finding the probability of the coin being biased
  • Interview questions on conditional probability and Bayes theorem: Given the statistics, what is the probability of success of an event
3

Distributions

  • Random variables, distributions, PDF, and CDF
  • Intriguing properties of normal distribution and related common interview questions
  • The application of normal distribution in various industries/fields such as finance, trading, etc. 
  • Importance of normalization and standardization during data analysis
  • Central Limit Theorem and its real-life applications
  • Extensive coverage of distributions, including uniform distribution, binomial distribution, Poisson distribution, exponential, etc.
  • Relationships among probability distributions such as approximating binomial distribution to the normal distribution under the certain circumstances
  • Say you have X1 ~ Uniform(0, 1) and X2 ~ Uniform(0, 1). What is the expected value of the minimum of X and X2?
  • Suppose a fair coin is tossed 100 times. What is the probability there will be more than 60 heads?
  • The probability of a car passing a certain intersection in a 20-minute window is 0.9. What is the probability of a car passing the intersection in a 5-minute window? 
4

Data Science Design: A/B testing

  • Hypothesis testing, develop null and alternative hypotheses
  • Familiarity with p-value and general misconceptions like p-value is the probability of the null hypothesis being false
  • How to find the confidence interval? What are Type-1 and Type-2 errors?
  • One side vs. Two side testing. When to use when?
  • T-test vs. Z-test: How can we test whether the avg. car speed on the highway exceeds 65mph with a significance level of 0.05?
  • Chi-square test and ANOVA (ANalysis Of VAriance)
  • Learn how FAANG+ companies do A/B testing for their business
  • Tough interview questions such as determining whether a new video recommendation algorithm has been better than the current one
  • Performance metrics: Answer business questions, such as opportunity estimation and gap analysis
  • Application of AUC-ROC, Accuracy, Precision, Recall, F-score, etc.
  • Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?
5

Regression, MLE, EM, and MAP

  • Regression: Investigate the relationship between two variables
  • Assumption of Linear Regression and common interview questions such as what if one of the assumptions doesn’t work?
  • Least Square Estimator vs. Maximum Likelihood estimator: Under what conditions they are the same?
  • Ridge Regression vs. Lasso Regression: Which regression can make a certain coefficient to exactly zero and how?
  • Likelihood function: Measure how well observed data fits the assumed distribution
  • Maximum Likelihood Estimation: A car speed on the highway follows a normal distribution: N(μ,25), After observing the n car speed, what is the MLE for μ?
  • Expectation-Maximization: Understand it through the example of Gaussian Mixture Models
  • Maximum a posteriori (MAP) and how it’s different from Maximum Likelihood Estimation
6

Supervised Machine Learning

  • Defining the steps for data preprocessing with the help of intuitive examples
  • Best practices of data type identification, data quality correction, feature engineering, dimensionality reduction
  • Model training and the importance of training, validation, and test datasets 
  • Interview-focused concepts like objective functions and evaluation metrics are revisited to help understand the topic as a whole
  • Optimization techniques like Gradient Descent, SGD, and Adam Optimizer with challenging questions
  • Describing interview-focused Supervised Machine Learning Algorithms like Logistic Regression, Naive Bayes, kNN, and SVM
  • Learn to break down problems with logistic regression and understand issues with logistic regression
  • Limitations of  Naive Bayes explaining why it is naive
  • Visualizing the KNN algorithm in the context of classification and regression 
  • Graphically distinguishing between various cases for classification using the Support Vector Machine algorithm
  • SVM kernel tricks and related interview questions 
  • Interview questions on kernel: Can it be used with KNN?
  • The intuition behind the decision tree: how to arrive at a decision by asking a series of binary questions
  • Building a decision tree from scratch
  • Overfitting and underfitting in the context of machine learning algorithms
  • Bagging vs. Boosting
  • Interview Questions: Why random forest? Why is it random? Common problems with decision trees and random forest
7

Unsupervised Machine Learning

  • Defining recommendation systems through examples from video streaming and online shopping
  • Illustration of different approaches to build recommendation systems like collaborative filtering, content-based filtering, and hybrid approaches
  • Drawbacks of item-based recommenders and why to use matrix factorization
  • Singular Value Decomposition and other alternatives for SVD
  • Classify the measure of similarity of data points by using Euclidean, Manhattan, and Cosine Similarity
  • Explain clustering by describing Gene Expression and Image Segmentation 
  • Graphically depicting the K-Means Algorithm and how to choose the value of K
  • Understand DBSCAN Graphically depicting the K-Means Algorithm and how to choose the value of K
  • AN Algorithm and its parameters in detail and when it is preferred
  • Interview questions based on the preference of K-Means and DBSCAN Algorithm
  • Explore PCA and how to use it for Dimensionality Reduction
  • Learn to compute principal components iteratively and by using eigenvalues and eigenvectors
8

Deep Learning

  • Define Common Activation Function and the advantages of using CAF
  • Neural network covering interview questions such as the strategy of trying learning rate, linear or logarithmic scale
  • How do forward propagation and backward propagation work?
  • Dense Neural Network (DNN) on image processes and advantage of using CNN over DNN
  • Defining CNN Architecture: Kernels, Pattern Finding, and Feature Map
  • Common interview questions on CNN
  • Implementation of CNN using Tensorflow
  • Learn Dropout: Is dropout used in the test dataset?
  • Why RNN over N-gram models?
  • RNN Architecture, backward propagation over time, covering interview questions such as: what is exploding vs. vanishing gradient? Does RNN suffer both?
  • Bidirectional RNN (BiRNN) and Stacked RNN
  • Advantages of using BiRNN
  • How to go from Naive RNN to Long short-term memory (LSTM)
  • LSTM architecture: Forget Gate, Input Gate, Intermediate Cells, and Update Cell
  • Interview-relevant strategies: What is the interviewer expecting when they ask about LSTM basics?
9

Time Series Analysis

  • Understand trends, seasonality, cyclic, and irregularity in time series data
  • Importance of stationarity, Augmented Dicky Fuller (ADF) test, Interview Questions: What is the null hypothesis in the ADF test?
  • Interview questions on AR, MA, and ARIMA such as the difference between ACF and PACF, how to find p,d,q in ARIMA
  • Extension of ARIMA: SARIMA, SARIMAX, and their advantage
  • How does Facebook Prophet work? Demonstrate Facebook Prophet
  • Neural Prophet vs FB prophet.
Career Coaching
3 weeks
3 live classes
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

Interview Preparation

Interview Questions
Placement assistance
Behavioral Coaching
2

Resume & LinkedIn Masterclass

3

Salary Negotiation Masterclass

Support Period
6 months
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

15 mock interviews

2

Take classes you missed/retake classes/tests

3

1:1 technical/career coaching

4

Interview strategy and salary negotiation support


Next webinar starts in

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Best Suited for

  • Current or Former Data Scientists
  • Software Engineers working on ML Models

Data Science Interview Process at Tier-1 Companies

We prepare you for all stages of a typical data science interview process at FAANG and Tier-1 companies

1 Coding Round

Easy to medium Leetcode problems or Python-based data manipulation, wrangling questions. SQL is often a part of these rounds.

Behavioral Round

  • Questions related to your job experience
  • Discussions on past projects
  • Open-ended questions to gauge if you're a "good fit”

3-4 Onsite Rounds

  • 1 Problem-Solving/Discussion Round: Discussing past work experience, projects, and approach; questions based on statistics, coding, probability, and quantitative aptitude.
  • 1 Take-Home Assignment Round: Some companies give a dataset and inference-based questions to judge your approach to the problem and deduction skills. The usual deadline is 24-48 hours.
  • 1-2 Domain Rounds: Deep dive into Data Science fundamentals. Questions in these rounds typically focus on designing experiments to meet certain business goals, A/B testing, and ML algorithms.

Top companies love hiring our candidates
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Top companies love hiring our candidates!

10K+

Experienced engineers enrolled

7

Years of successful training in Silicon Valley

18

Highest number of offers received by an alum

5

Avg years of experience of our alumni

What our students say

Akshay Lodha
Data Engineer
Offers from
Google_logo

"The way the instructors taught was awesome, the career coaching and the mock interview sections were also really helpful. Interview Kickstart helped me a lot in orienting myself and getting into the rhythm., and eventually transition from Goldman Sachs to Facebook."

Sujay Ghosh
Software Development Manager
Offers from
Google_logo

“Interview Kickstart's program is wonderful. I found the classes as well as the materials provided by Interview Kickstart very helpful. In the mock interview sessions, I was able to clear my concerns on a 1-on-1 basis.”

Rupesh Dabbir
Offers from
Google_logo

Interview Kickstart (IK) provides you a solid platform to not only strengthen your algorithm and interview game, I've had the pleasure of meeting some of the best/brightest minds in the industry (Faculty and students included). It was a humble experience, to say the least.

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