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Head of Career Skills Development & Coaching

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Switchup: Transform Your Career

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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

Data Science

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Next Batch

12th June, 2022

Location

Live & online

Duration

4 months (apx. 10 hours/week)

Sayan Banerjee

Data Scientist II

Manika Kapoor

Senior Deep Learning Scientist

Siva Karthik Gade

Software Development Engineer

Sai Marapa Reddy

SWE, Machine Learning

Safir Merchant

SWE, Machine Learning

Mike Kane

Lead Data Engineer, Analytics

Akshay Lodha

Data Engineering & Analytics

Jaime Lichauco

Database Engineer

Anju Mercian

Data Engineering Consultant

Alokkumar Roy

Data Engineer

13,500+

Tech professionals trained

$1.267M

Highest offer received by an IK alum

53%

Average salary hike received by alums in 2021

Python Fundamentals

3 weeks

5 live classes

1

2

3

Note: Each week will cover some Data Science Python problems

2

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

3

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

4

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

5

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

Data Analysis with Python

2 weeks

7 live classes

1

2

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

3

- Random variables, distributions, PDF, and CDF
- Intriguing properties of normal distribution and related common interview questions
- The application of normal distribution in various industries/fields such as finance, trading, etc.
- Importance of normalization and standardization during data analysis
- Central Limit Theorem and its real-life applications
- Extensive coverage of distributions, including uniform distribution, binomial distribution, Poisson distribution, exponential, etc.
- Relationships among probability distributions such as approximating binomial distribution to the normal distribution under the certain circumstances
- 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

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

5

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

6

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

7

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

8

- Define Common Activation 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

- 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

Databases & SQL Programming

2 weeks

7 live classes

1

2

2

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

3

- Random variables, distributions, PDF, and CDF
- Intriguing properties of normal distribution and related common interview questions
- The application of normal distribution in various industries/fields such as finance, trading, etc.
- Importance of normalization and standardization during data analysis
- Central Limit Theorem and its real-life applications
- Extensive coverage of distributions, including uniform distribution, binomial distribution, Poisson distribution, exponential, etc.
- Relationships among probability distributions such as approximating binomial distribution to the normal distribution under the certain circumstances
- 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

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

5

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

6

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

7

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

8

- Define Common Activation 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

- 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

2

3

4

5

6

2

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

3

- Random variables, distributions, PDF, and CDF
- Intriguing properties of normal distribution and related common interview questions
- The application of normal distribution in various industries/fields such as finance, trading, etc.
- Importance of normalization and standardization during data analysis
- Central Limit Theorem and its real-life applications
- Extensive coverage of distributions, including uniform distribution, binomial distribution, Poisson distribution, exponential, etc.
- Relationships among probability distributions such as approximating binomial distribution to the normal distribution under the certain circumstances
- 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

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

5

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

6

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

7

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

8

- Define Common Activation 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

- 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

2

3

4

5

6

7

2

- Dealing with bias: Given an outcome, finding the probability of the coin being biased

3

- Random variables, distributions, PDF, and CDF
- Intriguing properties of normal distribution and related common interview questions
- The application of normal distribution in various industries/fields such as finance, trading, etc.
- Importance of normalization and standardization during data analysis
- Central Limit Theorem and its real-life applications
- Common FAANG+ interview questions on distributions:
- Suppose a fair coin is tossed 100 times. What is the probability there will be more than 60 heads?

4

- Hypothesis testing, develop null and alternative hypotheses
- How to find the confidence interval? What are Type-1 and Type-2 errors?
- One side vs. Two side testing. When to use when?
- Chi-square test and ANOVA (ANalysis Of VAriance)
- Learn how FAANG+ companies do A/B testing for their business
- Performance metrics: Answer business questions, such as opportunity estimation and gap analysis
- Application of AUC-ROC, Accuracy, Precision, Recall, F-score, etc.
- Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?

5

- Regression: Investigate the relationship between two variables
- Least Square Estimator vs. Maximum Likelihood Estimator: Under what conditions they are the same?
- Likelihood function: Measure how well observed data fits the assumed distribution
- Expectation-Maximization: Understand it through the example of Gaussian Mixture Models
- Maximum a posteriori (MAP) and how it’s different from Maximum Likelihood Estimation

6

- Defining the steps for data preprocessing with the help of intuitive examples
- Model training and the importance of training, validation, and test datasets
- Optimization techniques like Gradient Descent, SGD, and Adam Optimizer with challenging questions
- 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
- SVM kernel tricks and related interview questions
- Interview questions on kernel: Can it be used with KNN?
- Building a decision tree from scratch
- Overfitting and underfitting in the context of machine learning algorithms
- Bagging vs. Boosting

7

- Defining recommendation systems through examples from video streaming and online shopping
- Drawbacks of item-based recommenders and why to use matrix factorization
- Singular Value Decomposition and other alternatives for SVD
- Explain clustering by describing Gene Expression and Image Segmentation
- Graphically depicting the K-Means Algorithm and how to choose the value of K
- Understand DBSCAN Graphically depicting the K-Means Algorithm and how to choose the value of K
- An algorithm and its parameters in detail and when it is preferred
- Interview questions based on the preference of K-Means and DBSCAN Algorithm
- Explore PCA and how to use it for Dimensionality Reduction
- Learn to compute principal components iteratively and by using eigenvalues and eigenvectors

8

- Define Common Activation Functions and the advantages of using them
- 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?
- Bidirectional RNN (BiRNN) and Stacked RNN
- Advantages of using BiRNN
- How to go from Naive RNN to Long short-term memory (LSTM)
- LSTM architecture: Forget Gate, Input Gate, Intermediate Cells, and Update Cell
- Interview-relevant strategies: What is the interviewer expecting when they ask about LSTM basics?

9

- Understand trends, seasonality, cyclic, and irregularity in time series data
- 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

2

3

4

5

6

2

- Dealing with bias: Given an outcome, finding the probability of the coin being biased

3

- Random variables, distributions, PDF, and CDF
- Intriguing properties of normal distribution and related common interview questions
- The application of normal distribution in various industries/fields such as finance, trading, etc.
- Importance of normalization and standardization during data analysis
- Central Limit Theorem and its real-life applications
- Common FAANG+ interview questions on distributions:
- Suppose a fair coin is tossed 100 times. What is the probability there will be more than 60 heads?

4

- Hypothesis testing, develop null and alternative hypotheses
- How to find the confidence interval? What are Type-1 and Type-2 errors?
- One side vs. Two side testing. When to use when?
- Chi-square test and ANOVA (ANalysis Of VAriance)
- Learn how FAANG+ companies do A/B testing for their business
- Performance metrics: Answer business questions, such as opportunity estimation and gap analysis
- Application of AUC-ROC, Accuracy, Precision, Recall, F-score, etc.
- Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?

5

- Regression: Investigate the relationship between two variables
- Least Square Estimator vs. Maximum Likelihood Estimator: Under what conditions they are the same?
- Likelihood function: Measure how well observed data fits the assumed distribution
- Expectation-Maximization: Understand it through the example of Gaussian Mixture Models
- Maximum a posteriori (MAP) and how it’s different from Maximum Likelihood Estimation

6

- Defining the steps for data preprocessing with the help of intuitive examples
- Model training and the importance of training, validation, and test datasets
- Optimization techniques like Gradient Descent, SGD, and Adam Optimizer with challenging questions
- 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
- SVM kernel tricks and related interview questions
- Interview questions on kernel: Can it be used with KNN?
- Building a decision tree from scratch
- Overfitting and underfitting in the context of machine learning algorithms
- Bagging vs. Boosting

7

- Defining recommendation systems through examples from video streaming and online shopping
- Drawbacks of item-based recommenders and why to use matrix factorization
- Singular Value Decomposition and other alternatives for SVD
- Explain clustering by describing Gene Expression and Image Segmentation
- Graphically depicting the K-Means Algorithm and how to choose the value of K
- Understand DBSCAN Graphically depicting the K-Means Algorithm and how to choose the value of K
- An algorithm and its parameters in detail and when it is preferred
- Interview questions based on the preference of K-Means and DBSCAN Algorithm
- Explore PCA and how to use it for Dimensionality Reduction
- Learn to compute principal components iteratively and by using eigenvalues and eigenvectors

8

- Define Common Activation Functions and the advantages of using them
- 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?
- Bidirectional RNN (BiRNN) and Stacked RNN
- Advantages of using BiRNN
- How to go from Naive RNN to Long short-term memory (LSTM)
- LSTM architecture: Forget Gate, Input Gate, Intermediate Cells, and Update Cell
- Interview-relevant strategies: What is the interviewer expecting when they ask about LSTM basics?

9

- Understand trends, seasonality, cyclic, and irregularity in time series data
- 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

2

3

4

2

- Dealing with bias: Given an outcome, finding the probability of the coin being biased

3

- Random variables, distributions, PDF, and CDF
- Intriguing properties of normal distribution and related common interview questions
- The application of normal distribution in various industries/fields such as finance, trading, etc.
- Importance of normalization and standardization during data analysis
- Central Limit Theorem and its real-life applications
- Common FAANG+ interview questions on distributions:
- Suppose a fair coin is tossed 100 times. What is the probability there will be more than 60 heads?

4

- Hypothesis testing, develop null and alternative hypotheses
- How to find the confidence interval? What are Type-1 and Type-2 errors?
- One side vs. Two side testing. When to use when?
- Chi-square test and ANOVA (ANalysis Of VAriance)
- Learn how FAANG+ companies do A/B testing for their business
- Performance metrics: Answer business questions, such as opportunity estimation and gap analysis
- Application of AUC-ROC, Accuracy, Precision, Recall, F-score, etc.
- Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?

5

- Regression: Investigate the relationship between two variables
- Least Square Estimator vs. Maximum Likelihood Estimator: Under what conditions they are the same?
- Likelihood function: Measure how well observed data fits the assumed distribution
- Expectation-Maximization: Understand it through the example of Gaussian Mixture Models
- Maximum a posteriori (MAP) and how it’s different from Maximum Likelihood Estimation

6

- Defining the steps for data preprocessing with the help of intuitive examples
- Model training and the importance of training, validation, and test datasets
- Optimization techniques like Gradient Descent, SGD, and Adam Optimizer with challenging questions
- 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
- SVM kernel tricks and related interview questions
- Interview questions on kernel: Can it be used with KNN?
- Building a decision tree from scratch
- Overfitting and underfitting in the context of machine learning algorithms
- Bagging vs. Boosting

7

- Defining recommendation systems through examples from video streaming and online shopping
- Drawbacks of item-based recommenders and why to use matrix factorization
- Singular Value Decomposition and other alternatives for SVD
- Explain clustering by describing Gene Expression and Image Segmentation
- Graphically depicting the K-Means Algorithm and how to choose the value of K
- Understand DBSCAN Graphically depicting the K-Means Algorithm and how to choose the value of K
- An algorithm and its parameters in detail and when it is preferred
- Interview questions based on the preference of K-Means and DBSCAN Algorithm
- Explore PCA and how to use it for Dimensionality Reduction
- Learn to compute principal components iteratively and by using eigenvalues and eigenvectors

8

- Define Common Activation Functions and the advantages of using them
- 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?
- Bidirectional RNN (BiRNN) and Stacked RNN
- Advantages of using BiRNN
- How to go from Naive RNN to Long short-term memory (LSTM)
- LSTM architecture: Forget Gate, Input Gate, Intermediate Cells, and Update Cell
- Interview-relevant strategies: What is the interviewer expecting when they ask about LSTM basics?

9

- Understand trends, seasonality, cyclic, and irregularity in time series data
- 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

2

2

- Dealing with bias: Given an outcome, finding the probability of the coin being biased

3

- Random variables, distributions, PDF, and CDF
- Intriguing properties of normal distribution and related common interview questions
- The application of normal distribution in various industries/fields such as finance, trading, etc.
- Importance of normalization and standardization during data analysis
- Central Limit Theorem and its real-life applications
- Common FAANG+ interview questions on distributions:
- Suppose a fair coin is tossed 100 times. What is the probability there will be more than 60 heads?

4

- Hypothesis testing, develop null and alternative hypotheses
- How to find the confidence interval? What are Type-1 and Type-2 errors?
- One side vs. Two side testing. When to use when?
- Chi-square test and ANOVA (ANalysis Of VAriance)
- Learn how FAANG+ companies do A/B testing for their business
- Performance metrics: Answer business questions, such as opportunity estimation and gap analysis
- Application of AUC-ROC, Accuracy, Precision, Recall, F-score, etc.
- Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?

5

- Regression: Investigate the relationship between two variables
- Least Square Estimator vs. Maximum Likelihood Estimator: Under what conditions they are the same?
- Likelihood function: Measure how well observed data fits the assumed distribution
- Expectation-Maximization: Understand it through the example of Gaussian Mixture Models
- Maximum a posteriori (MAP) and how it’s different from Maximum Likelihood Estimation

6

- Defining the steps for data preprocessing with the help of intuitive examples
- Model training and the importance of training, validation, and test datasets
- Optimization techniques like Gradient Descent, SGD, and Adam Optimizer with challenging questions
- 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
- SVM kernel tricks and related interview questions
- Interview questions on kernel: Can it be used with KNN?
- Building a decision tree from scratch
- Overfitting and underfitting in the context of machine learning algorithms
- Bagging vs. Boosting

7

- Defining recommendation systems through examples from video streaming and online shopping
- Drawbacks of item-based recommenders and why to use matrix factorization
- Singular Value Decomposition and other alternatives for SVD
- Explain clustering by describing Gene Expression and Image Segmentation
- Graphically depicting the K-Means Algorithm and how to choose the value of K
- Understand DBSCAN Graphically depicting the K-Means Algorithm and how to choose the value of K
- An algorithm and its parameters in detail and when it is preferred
- Interview questions based on the preference of K-Means and DBSCAN Algorithm
- Explore PCA and how to use it for Dimensionality Reduction
- Learn to compute principal components iteratively and by using eigenvalues and eigenvectors

8

- Define Common Activation Functions and the advantages of using them
- 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?
- Bidirectional RNN (BiRNN) and Stacked RNN
- Advantages of using BiRNN
- How to go from Naive RNN to Long short-term memory (LSTM)
- LSTM architecture: Forget Gate, Input Gate, Intermediate Cells, and Update Cell
- Interview-relevant strategies: What is the interviewer expecting when they ask about LSTM basics?

9

- Understand trends, seasonality, cyclic, and irregularity in time series data
- 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

2

3

4

5

2

- Dealing with bias: Given an outcome, finding the probability of the coin being biased

3

- Random variables, distributions, PDF, and CDF
- Intriguing properties of normal distribution and related common interview questions
- The application of normal distribution in various industries/fields such as finance, trading, etc.
- Importance of normalization and standardization during data analysis
- Central Limit Theorem and its real-life applications
- Common FAANG+ interview questions on distributions:
- Suppose a fair coin is tossed 100 times. What is the probability there will be more than 60 heads?

4

- Hypothesis testing, develop null and alternative hypotheses
- How to find the confidence interval? What are Type-1 and Type-2 errors?
- One side vs. Two side testing. When to use when?
- Chi-square test and ANOVA (ANalysis Of VAriance)
- Learn how FAANG+ companies do A/B testing for their business
- Performance metrics: Answer business questions, such as opportunity estimation and gap analysis
- Application of AUC-ROC, Accuracy, Precision, Recall, F-score, etc.
- Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?

5

- Regression: Investigate the relationship between two variables
- Least Square Estimator vs. Maximum Likelihood Estimator: Under what conditions they are the same?
- Likelihood function: Measure how well observed data fits the assumed distribution
- Expectation-Maximization: Understand it through the example of Gaussian Mixture Models
- Maximum a posteriori (MAP) and how it’s different from Maximum Likelihood Estimation

6

- Defining the steps for data preprocessing with the help of intuitive examples
- Model training and the importance of training, validation, and test datasets
- Optimization techniques like Gradient Descent, SGD, and Adam Optimizer with challenging questions
- 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
- SVM kernel tricks and related interview questions
- Interview questions on kernel: Can it be used with KNN?
- Building a decision tree from scratch
- Overfitting and underfitting in the context of machine learning algorithms
- Bagging vs. Boosting

7

- Defining recommendation systems through examples from video streaming and online shopping
- Drawbacks of item-based recommenders and why to use matrix factorization
- Singular Value Decomposition and other alternatives for SVD
- Explain clustering by describing Gene Expression and Image Segmentation
- Graphically depicting the K-Means Algorithm and how to choose the value of K
- Understand DBSCAN Graphically depicting the K-Means Algorithm and how to choose the value of K
- An algorithm and its parameters in detail and when it is preferred
- Interview questions based on the preference of K-Means and DBSCAN Algorithm
- Explore PCA and how to use it for Dimensionality Reduction
- Learn to compute principal components iteratively and by using eigenvalues and eigenvectors

8

- Define Common Activation Functions and the advantages of using them
- 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?
- Bidirectional RNN (BiRNN) and Stacked RNN
- Advantages of using BiRNN
- How to go from Naive RNN to Long short-term memory (LSTM)
- LSTM architecture: Forget Gate, Input Gate, Intermediate Cells, and Update Cell
- Interview-relevant strategies: What is the interviewer expecting when they ask about LSTM basics?

9

- Understand trends, seasonality, cyclic, and irregularity in time series data
- 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

2

2

3

Support Period

6 Months

1

2

3

4

Data Structures and Algorithms Interview Prep

7 weeks

5 live classes

1

2

3

4

5

6

7

2

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

3

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

4

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

5

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

Data Science Interview Prep

5 weeks

5 live classes

1

2

3

4

5

2

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

3

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

4

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

5

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

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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)

360° course designed and taught by FAANG+ experts to help you become a AI/ML Data Scientist.

Technical coaching, homework assistance, solutions discussion, and individual sessions

Exposure to real-life data science and machine learning projects

Dedicated interview prep classes focused on helping you get 100% interview-ready

Live interview practice in real-life simulated environments with FAANG and top-tier interviewers

Resume building, LinkedIn profile optimization, personal branding, and live behavioral workshops

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The program schedule is designed to fit into your work and life schedule, with live classes on weekends and coaching sessions in the evenings.

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

2-hour session in the evening to discuss assignments and problem solutions

1-hour technical coaching session to discuss any additional doubts

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

Real-time progress update

11 programming languages

What makes our mock Interviews the best:

Interview with the best. No one will prepare you better!

Practice for your target domain - Data Science

Identify and work on your improvement areas

Get the most realistic experience possible

More about mock interviews

Our engineers land high-paying and rewarding offers from the biggest tech companies, including **Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.**

Data Engineering & Analytics

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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.

Data Scientist II

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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

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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.

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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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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

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

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 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.

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

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

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

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?

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.

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