Our curriculum

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

2.Presorting

• Extensions of Merge Sort, Quick Sort, Heap Sort

• Common sorting-related coding interview problems

3.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

4.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

5.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

6.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

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 insights 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 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

• Common interview questions on distributions:

  • Say you have X1 ~ Uniform(0, 1) and X2 ~ Uniform(0, 1). What is the expected value of the minimum of X1 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 Functions and the advantages of using them

• Understand neural networks with emphasis on 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, finding 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