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Switchup: Transform Your Career
Transition to AI/ML Data Science roles at Tier-1 companies
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Students enrolled: 165
Designed and taught by FAANG+ Data and Research Scientists to help you transform your career and land your dream job.
Data Science Engineers!
Get interview-ready with lessons by FAANG+ Data Scientists
Master core Data Science interview concepts
Sharpen coding skills relevant for DS interviews
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12th June, 2022
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Duration
4 months (apx. 10 hours/week)
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Sayan Banerjee
Data Scientist II
Manika Kapoor
Senior Deep Learning Scientist
Siva Karthik Gade
Software Development Engineer
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SWE, Machine Learning
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SWE, Machine Learning
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Lead Data Engineer, Analytics
Akshay Lodha
Data Engineering & Analytics
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Database Engineer
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AI/ML Data Science Course details and curriculum
PART 1: Mastering Data Science
Python Fundamentals
3 weeks
5 live classes
1
Python Fundamentals - I
2
Python Fundamentals - II
3
Python Fundamentals - III & Introduction to Shell Scripting, Git & Github
Note: Each week will cover some Data Science Python 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 Analysis with Python
2 weeks
7 live classes
1
Python for ML & DS- I
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
- Common FAANG+ 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
2
Python for ML & DS- II
Databases & SQL Programming
2 weeks
7 live classes
1
Intro to Databases & SQL Programming
2
Advanced SQL Database Modelling
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
- Common FAANG+ 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
Essential Math for DS & ML
6 weeks
7 live classes
1
Essentials of Probability
2
Probability Distributions
3
Essentials of Statistics
4
Calculus Primer
5
Linear Algebra and Regression - I
6
Linear Algebra and Regression - II
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
- Common FAANG+ 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
Predictive Analysis
7 weeks
7 live classes
1
EDA & Feature Engineering
2
Regression Algorithms
3
Classification Algorithms
4
Bagging & Boosting Techniques
5
Unsupervised Machine Learning - I
6
Unsupervised Machine Learning - II
7
ML Mini Projects
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
- Common FAANG+ 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
Deep Learning & Computer Vision
6 weeks
7 live classes
1
Intro to Neural Networks
2
DL Data structures and Frameworks
3
Neural Architectures
4
Computer Vision - I
5
Computer Vision - II
6
Deep Learning Mini Project
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
- Common FAANG+ 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
Natural Language Processing & Generative AI
4 weeks
7 live classes
1
Modern ML Architectures
2
Natural Language Processing - I
3
Natural Language Processing - II
4
Generative AI
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
- Common FAANG+ 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
Intro to Big Data & Spark
2 weeks
7 live classes
1
Big Data & Spark - I
2
Big Data & Spark - II
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
- Common FAANG+ 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
Advanced Statistics & Time Series Forecasting
5 weeks
7 live classes
1
Essentials of Statistics - II
2
Advanced Statistics
3
Time Series Forecasting - I
4
Time Series Forecasting - II
5
Visualization & Data Storytelling
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
- Common FAANG+ 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
Capstone Project
2 weeks
3 live classes
1
Capstone Project - I
2
Capstone Project - II
2
Behavioral Interview Prep
3
Offers and Negotiation
Support Period
6 Months
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
PART 2: Interview Prep
Data Structures and Algorithms Interview Prep
7 weeks
5 live classes
1
Sorting Algorithms
2
Binary Search
3
Arrays: Prefix Sum, Sliding Windows
4
Linked List
5
Recursion & Backtracking
6
Stacks and Queues
7
Trees
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 Interview Prep
5 weeks
5 live classes
1
Linear Regression, MLE, EM and MAP
2
SQL Programming
3
Intro to Machine Learning, Supervised Learning
4
Supervised and Unsupervised ML
5
Deep Learning and Time Series Analysis
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
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Best suited for
Software Engineers/Developers and Data Analyst professionals who want to make a career in the AI/ML Data Science domain
Recent college graduates/undergrads who want to become AIML data scientists
Anyone with basic or no understanding of Data Science and looking to master the data science domain (STEM background required)
Why choose this course?
Program by FAANG+ Data Scientists
360° course designed and taught by FAANG+ experts to help you become a AI/ML Data Scientist.
Continuous feedback and learning
Technical coaching, homework assistance, solutions discussion, and individual sessions
Capstone project
Exposure to real-life data science and machine learning projects
Interview prep modules
Dedicated interview prep classes focused on helping you get 100% interview-ready
Mock interviews with FAANG+ Data Scientists
Live interview practice in real-life simulated environments with FAANG and top-tier interviewers
Career skills development
Resume building, LinkedIn profile optimization, personal branding, and live behavioral workshops
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Meet your instructors
Our highly experienced instructors are active hiring managers and employees at FAANG+ companies and know exactly what it takes to ace tech and managerial interviews.
Andrew Treadway
Research Data Scientist
9+ years experience
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Omkar Deshpande
Head of Curriculum
15+ years experience
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Nick Camilleri
Head of Career Skills Devp. and Coaching
10+ years experience
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Qiuping Xu
Principal Scientist
9+ years experience
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Abdul Salama
Manager, AI Platform
12+ years experience
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Bharath Srikanth
Senior Data Scientist
7+ years experience
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Bhuvan Venkatesh
Data Scientist
5+ years experience
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Built for working professionals
The program schedule is designed to fit into your work and life schedule, with live classes on weekends and coaching sessions in the evenings.
Sunday
4-hour Live Classes in the morning
Consists of introduction to fundamentals, interview-relevant topics and case studies
Assignment review session
Solve questions and case studies based on the assignment shared with you
Thursday
2-hour session in the evening to discuss assignments and problem solutions
Once a week
1-hour technical coaching session to discuss any additional doubts
Practice and track progress on UpLevel
UpLevel will be your all-in-one learning platform to get you FAANG-ready, with 10,000+ interview questions, timed tests, videos, mock interviews suite, and more.
Mock interviews suite
On-demand timed tests
In-browser online judge
10,000 interview questions
100,000 hours of video explanations
Class schedules & activity alerts
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Real-time progress update
11 programming languages
Get upto 15 mock interviews with hiring managers
What makes our mock Interviews the best:
Hiring managers from Tier-1 companies like Google & Apple
Interview with the best. No one will prepare you better!
Domain-specific Interviews
Practice for your target domain - Data Science
Detailed personalized feedback
Identify and work on your improvement areas
Transparent, non-anonymous interviews
Get the most realistic experience possible
More about mock interviews
Career impact
Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.
Akshay Lodha
Data Engineering & Analytics
Placed at:
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The experience with IK was phenomenal, it was totally worth it. After so many years I was interviewing and IK helped me a lot in orienting myself and to get into the rhythm. Had a transition from Goldman Sachs to Facebook. IK mentors guided me and told me not to worry about the preparation part and to focus on upskilling myself. That really made a huge difference.
Sayan Banerjee
Data Scientist II
Placed at:
Interview Kickstart has a very nice community. Everybody is very knowledgeable. Omkar is amazing. The way that he explains things is so great that it also taught me how to explain steps in problem solving well. Interview Kickstart's way of identifying patterns in a problem is the most effective part for me. Lastly, with the community, it enabled me to have a network of people that I can see different perspectives, and approaches to reaching solutions.
Siva Karthik Gade
Software Development Engineer
Placed at:
IK offers high-quality study material, knowledgeable and patient instructors working at industry-leading companies, well-paced live classes + tests + review sessions, always available technical + career coaches, mock interview support from the best interviewers in the respective fields. IK brings together people with same the ambition (on their platform, UPLEVEL) to guide and inspire each other
Nadha Gafur
Machine Learning Engineer
Placed at:
It has been a great learning experience. The structure is really good and the materials as well. The lectures and live class pre-reading material is very informative and engaging.
Sai MarapaReddy
Software Engineer
Placed at:
I completed IK’s program and got offers from a couple of FAANG companies. Why you should take this course: It is well tested and the focus is more on the concepts/templates rather than approaching one problem at a time. You will meet peers who have similar aspirations. You can make groups and help yourselves.
Safir Merchant
Machine Learning Software Engineer
Placed at:
I liked the course that IK provided a lot. IK provided all the knowledge on a variety of topics that helped me prepare for coding interviews. The mock interviews were really great. Landing a job at my desired company has been a great pleasure.
How to enroll for the AI/ML Data Science Course?
Learn more about Interview Kickstart and the AI/ML Data Science course by joining the free webinar hosted by Ryan Valles, co-founder of Interview Kickstart.
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A Free Guide to Kickstart Your Data Science Career at FAANG+
From the interview process and career path to interview questions and salary details — learn everything you need to know about Data Science careers at top tech companies.
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Interview Strategy and Success
Interview Questions
Career Path
Salary and Levels at FAANG
Frequently Asked Questions
Data Science Interview Process Outline
The typical Data Science interview process at FAANG and other Tier-1 companies looks like:
- One coding round: Easy to medium Leetcode problems or Python-based Data Manipulation and Wrangling questions. SQL is often a part of these rounds.
- Behavioral round: Open-ended questions to gauge if you're a "good fit” for the company.
- 3-4 on-site rounds:
- One problem-solving/discussion round
- One take-home assignment round
- 1-2 domain rounds
What to Expect at Data Scientist Interviews
1
One coding round
- Easy to medium Leet code problems or Python-based Data Manipulation and Wrangling questions. SQL is often a part of these rounds.
- The SQL round is pretty standard across all the FAANG+ companies. You’ll be asked to solve problems using common clauses such as JOINS, WHERE, and GROUP BY.
- Google tends to focus more on Statistical coding, some Data Analysis, and SQL since the company handles vast data sets.
2
One problem-solving/discussion round
- Inclined towards discussing your work experience, past projects, and problem-solving with a mix of statistics, coding, probability, and some quantitative aptitude questions.
- Facebook (Meta) focuses more on real-world data problems. So, prepare accordingly and provide concise answers when asked to elaborate on statistical terms.
3
One take-home assignment round
- Some companies give a dataset and inference-based questions to judge problem-approach/deduction skills as part of take-home assignments. The usual deadline is 24-48 hours.
- For the take-home assignment given by Apple, you’ll only be provided with three days. It will probably be a Machine Learning problem, and you’ll have to develop a model and give a prediction using the dataset.
4
1-2 domain rounds
- This round demands a deep dive into Data Science fundamentals. Interview questions in these rounds typically focus on designing experiments to meet certain business goals, A/B testing, and ML algorithms.
- You’ll need to be clear about how you frame the problem, the metrics you use, A/B testing, technical trade-offs, and so on, along with the required data analysis.
5
One behavioral round
- You can expect Data Science interview questions on your job experience and discussions on past projects along with open-ended questions to gauge if you're a "good fit.”
- When applying at Google, ensure that you have an answer for “Why Google?”. Such questions are asked at all FAANG+ companies.
- Thoroughly research each company's leadership principles and develop answers in the form of a story based on those characteristics.
IK’s Data Science Course is built to help you crack every stage of the interview. Read Why You Should Choose the Data Science Interview Course by Interview Kickstart to learn more.
Data Science Interview Questions
Data Science interview questions are based on various topics. You can answer them if you identify the common fundamentals.
Try answering these Data Science interview questions:
1
Data Science Interview Questions on Coding
Write a code that takes a number from the user and outputs all Fibonacci numbers less than the user input.
Given: The CDF of a distribution. Find: The mean.
Given: Two numbers a, b ;a<b. Find: Output of f(a,b) = g(a) + g(a+1) +g(a+2) +…+ g(b-2) + g(b-1) + g(b), where g(x) is defined as all Fibonacci numbers less than x.
Given: A number X. Find: The smallest sum of two factors (a, b) of X.
Given: Person A decides to go on a skydiving trip. Based on his research, the probability of a glitch resulting in death is 0.001. Find: The probability of death if A goes on 500 skydives.
2
Domain-specific Data Science Interview Questions
How do you define the ROC curve?
What is meant by the true positive rate and false-positive rate?
What are the steps involved in making a decision tree?
Given a data set consisting of variables with more than 30 percent missing values, how will you deal with them?
Define dimensionality reduction and what are its advantages?
Explain how you would calculate eigenvalues and eigenvectors of the following 3x3 matrix.
How to deal with unbalanced binary classification?
What is the difference between normalization and standardization?
Why does data cleaning play a vital role in the analysis?
3
Data Science Interview Questions on Behavioral Skills
Walk us through a project you’re very proud of.
Have you ever used data science to inform a business decision?
How well do you communicate technical concepts to non-technical team members?
How have you used data to elevate the experience of a customer or stakeholder?
Describe when you had to clean and organize a big data set.
If you want to go over some more Data Science interview questions, read:
Data Science Career
The Data Scientist career paths have been booming, and this trend is expected to continue in the upcoming years. Our Data Science Interview Course can help you gain the required skills to land the best job offers in top tech companies.
1
Data Science Career Roadmap
A Data Scientist’s career path features two main career tracks:
- Individual Contributor roles
- Management roles
Data Scientist Career Path — Individual Contributor (IC) Roles
Individual contributors in the Data Scientist’s career path work on core data science tasks such as programming, creating models, coding, solving complex problems, and getting hands-on with the technical aspects of data science jobs.
Advanced or deep technical or hard skills are key to developing an IC Data Scientist career path.
Typically, the Data Scientist career path for an individual contributor (IC) follows this progression:
Data Scientist 1 → Data Scientist 2 → Senior Data Scientist → Staff Data Scientist → Sr. Staff Data Scientist → Principal Data Scientist
2
Data Scientist Career Path — Management Roles
A managerial role in a Data Scientist’s career path involves management tasks such as leadership, building relationships, conflict resolution, etc.
For software engineering managerial roles, a conceptual understanding of the technologies used is sufficient to perform managerial tasks. In contrast, Data Science Managers must have a working knowledge of the technologies used.
Communication, leadership, collaboration, and other soft skills are essential for developing a Data Scientist career path in a management role.
Typically, the Data Scientist career path in management follows this progression:
Data Scientist 1 → Data Scientist 2 → Senior Data Scientist → Data Science Manager → Sr. Data Science Manager
To understand the career trajectory of a Data Scientist better, read:
3
Qualifications Required to Become a Data Scientist
Depending on where you are in your Data Scientist career path, you will need the following educational degrees:
- Bachelor’s/Master’s degree in Computer Science, Software Engineering, or a related field; Bachelor’s degree for an entry-level position and a Master’s degree for higher-level Data Scientist positions
- Ph.D. in a relevant field is preferable and often a prerequisite for advanced or research and development positions.
You can also obtain professional certifications in the skills needed to pursue a career in Data Science. Some of the top Data Science certifications customized for Software Engineers and Software Developers to uplevel your Data Scientist career path are:
Tensorflow Developer Certification
Google Professional Data Engineer Certification
Amazon AWS Big Data Certification
Microsoft Certified Azure AI Fundamentals
SAS Certified AI & Machine Learning Professional
4
Job Roles and Responsibilities of a Data Scientist
Based on the experience and job profile, the different job responsibilities of Data Scientists have been put together in the table given below:
Data Scientist’s Job Responsibilities by Role
Role
Experience Required
Job Responsibilities
Junior-level Data Scientist
Internship/independent projects
Develop experience working on existing code, programs, models to enhance efficiency, effectiveness, quality, and outcomes.
Mid-level Data Scientist
2+ years
Create and implement basic models and make presentations for feedback; develop technical expertise, and learn all about operations and various facets of data science projects.
Senior Data Scientist
5+ years
Strong technical competence in data science projects; lead projects; good business sense, communication, interpersonal skills; create operational impact; perform at scale, deepen technical expertise, widen interpersonal skill set.
Senior Principal/Staff Scientist
8 - 10 years
Advanced conceptual and practical technical expertise; provide technical direction and at scale; create business organizational impact; deep business acumen; identify business opportunities and enable teams to solve complex problems.
Data Science Manager
5+ years
Manage small teams; strong project management skills; strong interpersonal and people management skills; management experience.
Senior Data Science Manager
8 - 10 years
Manage large teams; excellent interpersonal and people management skills; lead large projects; strong technical skills; deep business acumen; create business impact; manage resources and develop talent.
5
Top Skills Needed to Become a Data Scientist
Data Mining and Data Wrangling
Machine Learning and Artificial Intelligence
Python, R, C++
SQL, Pig, Hive
Predictive Modeling
Math and Statistics — Linear Algebra, Bayes Theorem, Geometry, Multivariable Calculus, Probability, Discrete Math, and Graph Theory
Tableau, Excel, Microsoft Power BI, Qlikview, and other Business Intelligence and Data Visualization tools
Hadoop, Apache Spark, Apache Kafka, TensorFlow, Pandas, Matplolib, Scikit-Learn, Spark MLib, Numpy, AWS Deep Learning AMI, and other data frameworks
Wondering how to list these skills on your resume? Read How to Create an Impressive Data Scientist Resume.
Data Scientist Salary and Levels at FAANG+ Companies
The average Data Scientist's salary range is between $105,750 and $180,250 per year. However, total compensation varies considerably depending on the company, location, employee value, years of experience, and core skills.
We've listed the Data Scientist salary ranges for various FAANG+ companies below to give you a better idea of how they differ by level:
Facebook Data Scientist Salary
The different levels of Data Scientists at Facebook are:
IC3 (Associate Data Scientist): This is typically the level at which fresher Data Scientists or Software Engineers are hired.
IC4: Those hired at this level should have 3-5 years of industry experience. However, new grads can also be hired at this level, provided they can demonstrate skill and expertise.
IC5: Data Scientists hired at IC5 have at least 6-9 years of industry experience as they are required to lead complex projects on their own. Also considered the “terminal” level before a Data Scientist moves into the management domain as IC5 onwards, they perform more managerial responsibilities.
IC6: Most Data Scientists working at this level have almost 9+ years of experience.
IC7 and IC8: These levels require more than 10 years of experience.
Data Scientist Salary at Facebook
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
IC3
$168K
$127K
$29K
$14K
IC4
$222K
$155K
$48K
$19K
IC5
$302K
$184K
$90K
$29K
IC6
$404K
$218K
$142K
$44K
Amazon Data Scientist Salary
Amazon has its own Data Scientist job levels. They are:
L4: Entry-level Data Scientists with less than four years of experience pursuing advanced degrees. They need to be skilled in at least one scripting language and familiar with SQL.
L5: Mid-level Data Scientists have four to seven years of experience and may also have the title of Data Scientist II. At this level, Data Scientists usually have a Master’s degree with a good knowledge of coding.
L6: This level is for Data Scientists who have advanced degrees like Ph.Ds in Machine Learning, Natural Language Processing, etc., based on their area of specialization. The level includes several managerial positions as well.
L7: This level is for Principal Data Scientists with 10+ years of experience. These employees have several management responsibilities and essentially run the teams.
Data Scientist Salary at Amazon
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
L4
$175K
$132K
$26K
$21K
L5
$227K
$150K
$57K
$27K
L6
$315K
$160K
$140K
$19K
L7
$638K
$185K
$419K
$42K
Apple Data Scientist Salary
On average, the Apple Data Scientist’s salary is $170,871 per year in the US. It can range from $94k to $257k, depending upon your experience, location, skill sets, and many other factors.
Data Scientist Salary at Apple
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
ICT3
$207K
$149K
$41K
$17K
ICT4
$289K
$175K
$96K
$20K
ICT5
$395K
$220K
$145K
$33K
Netflix Data Scientist Salary
Unlike other companies such as Amazon and Apple, Netflix doesn’t have job levels. The company is known for hiring only senior professionals, like, Senior Data Scientists. However, even in this position, salary tends to differ.
Based on your experience and accomplishments, the Netflix Data Scientist salary ranges from $200,000 to $400,000. On average, a Senior Data Scientist at the company earns around $322,272 per year.
However, Netflix does offer a few opportunities for entry-level positions where the Data Scientist can earn around $127,000.
Data Scientist Salary at Netflix
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
Sr. SW. Engineer
$305K
$275K
$14K
$13K
Google Data Scientist Salary
With a user base spanning hundreds of millions, you can imagine how valuable Data Scientists must be to Google. The company employs almost 140,000 people globally, divided into teams; almost each of these teams has a Data Scientist.
There are nine different job levels at Google:
L3 (Data Scientist II): An entry-level position with 0-1 year of experience
L4 (Data Scientist III): Requires 2-5 years of experience
L5 (Senior Data Scientist): Requires over 5 years of experience
L6 (Staff Data Scientist): Requires over 8 years of experience
L7 (Senior Staff Data Scientist): Requires over 10 years of experience
L8-L11: Executive roles; only employees with considerable experience within Google are eligible for these positions
Data Scientist Salary at Google
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
L3
$158K
$119K
$32K
$14K
L4
$233K
$150K
$58K
$26K
L5
$307K
$181K
$96K
$32K
L6
$548K
$228K
$257K
$51K
FAQs on Data Science Interview Course
1
What does a Data Scientist do?
2
Is IK's Data Science Interview Course designed only for Data Scientists working in non-FAANG companies?
3
Why is system design not covered in IK’s Data Science Interview Course?
4
What skills are required to become a Data Scientist?
5
What qualifications are required to become a Data Scientist?
6
I am working as a Business Intelligence Analyst. Can this course help me to target roles such as Data Scientist?
How to enroll for the Data Science Interview Course?
Learn more about Interview Kickstart and the Data Science Interview course by joining the free webinar hosted by Ryan Valles, co-founder of Interview Kickstart.
Next webinar starts in
00
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Already preparing or want a sneak peek? Try the DS Interview Prep 7-day email course
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