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Nail Data Science interviews at FAANG and Tier-1 Tech Companies

Research Data Scientist

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Data Scientist

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

Data Structures & Algorithms

Data Science

Career Coaching

- 15 Mock Interviews
- 6-Month Support Period

To learn more about this course

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Data structures and Algorithms

5 weeks

5 live classes

1

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

2

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

3

**Case Studies:**on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube

Design real-time data-intensive applications like Google Maps, Netflix, etc.

1

- 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 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

- 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

- 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 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

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

2

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

3

**Case Studies:**on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube

Design real-time data-intensive applications like Google Maps, Netflix, etc.

1

- 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

- 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

- 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

- 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: 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

- 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

- 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

- 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

- 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

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

2

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

3

**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 Questions

Placement assistance

Behavioral Coaching

2

3

Support Period

6 months

1

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

2

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

3

**Case Studies:**on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube

Design real-time data-intensive applications like Google Maps, Netflix, etc.

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

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

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

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

**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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Experienced engineers enrolled

Years of successful training in Silicon Valley

Highest number of offers received by an alum

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Akshay Lodha

Data Engineer

Offers from

"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

“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

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.

What do the statistics/probability rounds look like in a Data Science interview?

What do the coding rounds look like in a Data Science interview?

What coding topics are essential for Data Science interviews, and how does our program approach them?

What does the data science domain interview round look like?

Here are some examples of domain-specific questions that have been asked in the interview:

Enroll?

Get closer to your dream Data Science job by registering for a pre-enrolment webinar with one of our founders.

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