Data Science COURSE

Nail Your Next Data Science Interview

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Students enrolled: 165

Designed and taught by FAANG+ Data Scientists to help you land your dream job in FAANG+ and Tier-1 companies. In a span of 15 weeks, we will cover everything you need to learn to nail your next Data Science interview.

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Course Overview

Start Learning

Get all the information about the course and pricing in our live webinar with Q&A.

Students who chose to uplevel with IK got placed at

Sayan Banerjee

Data Scientist II

Siva Karthik Gade

Software Development Engineer

Alokkumar Roy

Data Engineer

Sai Marapa Reddy

SWE, Machine Learning

Vinayak Prabhu

SWE, Machine Learning

Mike Kane

Lead Data Engineer, Analytics

Akshay Lodha

Data Engineering & Analytics

Jaime Lichauco

Database Engineer

Anju Mercian

Data Engineering Consultant

21,000+

Tech professionals trained

$1.28M

Highest offer received by an IK alum

53%

Average salary hike received by alums

Data Science Course details and curriculum

Data structures and Algorithms

Sorting

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

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

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

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

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

SQL Programming (interview-focused concepts and questions)

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

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

Distributions

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

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?

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

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

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

Deep Learning

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

Time Series Analysis

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

Career Skills

Interview Strategy and Success

Behavioral Interview Prep

Offers and Negotiation

Support Period

15 mock interviews

Take classes you missed/retake classes/tests

1:1 technical/career coaching

Interview strategy and salary negotiation support

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Best suited for

Why choose this course?

Program designed by FAANG+ leads

Covering data structures, algorithms, interview-relevant topics, and career coaching

Individualized teaching and 1:1 help

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

Mock interviews with Silicon Valley engineers

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

Personalized feedback

Constructive, structured, and actionable insights for improved interview performance

Career skills development

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

50% Money-Back Guarantee*

If you do well in our course but still don't land a domain-relevant job within the post-program support period, we'll refund 50% of the tuition you paid for the course.*

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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.

Qiuping Xu

Principal Scientist

9+ years experience

A typical week at Interview Kickstart

This is how our structured and organized program assists learners in uplevel their careers while keeping their current jobs. Our learners devote 10 to 12 hours per week to this course.

Thu

Foundation content

Get high-quality videos and course material for next week’s topic

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

Sun

Online live sessions

Attend 4-hour sessions hosted by Lead Data Scientists and Research Scientists from FAANG+ companies

Discuss open-ended questions and problem-solving strategies

Get pro tips to solve challenging interview problems

Mon-Wed

Practice problems & assignments

Practice the concepts taught in live sessions to solve assignment questions

Live doubt-solving with FAANG+ Data Science instructors

Learn about the hiring process and interview experiences at FAANG+ companies from the instructors

Every day

1:1 access to instructors

Personalized coaching from FAANG+ DS instructors

Individualized and detailed attention to your questions

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

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 - Machine Learning

Detailed personalized feedback

Identify and work on your improvement areas

Transparent, non-anonymous interviews

Get the most realistic experience possible

More about mock interviews

1. Flexible schedule

Pick timings convenient to you

4. Technical and behavioral interviews

Uplevel your technical and behavioral interview skills

2. Remote interview experience

Mirrors the current format of remote interviews

5. Level-specific interviews

Because an L4 at Google can be quite different from an E7 at Meta

3. Feedback documentation

All the feedback you’ve ever wanted, recorded and documented

6. Interviewer of your choice

Choose based on domain

Career impact

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

Sadhana Upadhyay

Works at:

The data structure course is very well-structured with detailed explanations, especially the videos by Mr. Omkar Deshpande. Previously, data structures and algorithms were challenging topics for me, but now I feel much more comfortable tackling problems using the patterns I learned in class

Jack Mengel

Works at:

Valuable knowledge

I learned many valuable skills that has helped me both in my job as well as in interviewing for new jobs. I will benefit from the program for a long time as I have a much deeper understanding of statistical concepts as well as data structures and various algorithms.

Soumya Majumder

I was a part of the Data Science Masterclass Program at Interview Kickstart.Very in-depth explanation. Gets you the intuition to solve the solutions. I especially like the mock interviews that IK is providing where you can overcome the fear of interviews and get to know what the interviewers are expecting. Every idea is to be considered as a pattern, which then is how the course generates absolute value. Will definitely encourage anyone of any stream to join and enrich yourselves.

A Free Guide to Kickstart Your Back-End Engineering Career at FAANG+

From the interview process and career path to interview questions and salary details — learn everything you need to know about Back-End Engineering careers at top tech companies.

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.

One problem-solving/discussion round
One take-home assignment round
1-2 domain rounds

Behavioral round: Open-ended questions to gauge if you’re a “good fit” for the company.

What to Expect at Data Scientist Interviews

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.

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.

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.

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.

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.

Data Science Interview Questions

Data Science interview questions can be based on a variety of topics. You can answer them if you identify common fundamentals.

Try answering these Data Science interview questions:

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.

Domain-specific Data Science Interview Questions

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 list its advantages.

Explain how you would calculate eigenvalues and eigenvectors of the following 3×3 matrix.

How to deal with unbalanced binary classification?

Differentiate between normalization and standardization.

Python or R – Which one would you prefer for text analytics?

Why does data cleaning play a vital role in the analysis?

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.

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

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:

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 follow a 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

Data Scientist’s Job Responsibilities

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 Manager

5+ years

Manage small teams; strong project management skills; strong interpersonal and people management skills; management experience.

Senior Data Manager

8 - 10 years

Manage large teams; excellent interpersonal and peoplemanagement skills; lead large projects; strong technical skills; deepbusiness acumen; create business impact; manage resources anddevelop talent.

Top Skills Needed to Become a Data Scientist

Data Mining and Data Wrangling

Machine Learning and Artificial Intelligence

Python, R Java, C++, Scala

SQL, Pig, Hive

Programming and 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 Science Engineers 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 at Amazon

Average compensation by level

Level name

Total

Base

Stock (/yr)

Bonus

$175K

$132K

$26K

$21K

$227K

$150K

$57K

$27K

$336K$315K

$160K

$140K

$19K

$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 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 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 five years of experience

L6 (Staff Data Scientist): Requires 5-8 years of experience

L7 (Senior Staff Data Scientist): Requires over eight years of experience

L8-L11: Executive roles; only employees with considerable experience within Google are eligible for these positions

Data Scientist at Google

Average compensation by level

Level name

Total

Base

Stock (/yr)

Bonus

$158K

$119K

$32K

$14K

$233K

$150K

$58K

$26K

$307K

$181K

$96K

$32K

$548K

$228K

$257K

$51K

FAQs on Data Science Interview Course

What does a Data Scientist do?

A Data Scientist is a technically skilled, result-oriented individual who is data-driven and adept at developing complex quantitative algorithms to sort and synthesize large amounts of data and drive strategy in their organization.

Is IK’s Data Science Interview Course just for professionals working as Data Scientists in non-FAANG+ companies?

This course is not just for Data Scientists working in companies other than FAANG. If you have worked as an Analyst, Applied Scientist, Research Scientist, or Statistician in your previous organizations and have some exposure to Machine Learning and Statistics; then this course is for you.

Why is system design not covered in IK’s Data Science Interview Course?

Based on our research and conversations with the instructors/hiring managers at FAANG+ companies, the system design round is not part of the interview process for data science roles such as business data scientist or product data scientist. In certain roles like applied scientist and research scientist you might require to have understanding of the system design. If you plan to target a role like a machine learning engineer, in that case, system design is an integral part of the interview process, and for that, you can look out for our machine learning program.

What skills are required to become a Data Scientist?

Programming in at least one language – Python, R or MATLAB
Strong Statistical knowledge like understanding of causal impact and inference, A/B testing, hypothesis testing, etc.
A good understanding of machine learning algorithms
Ability to collaborate with various teams on a wide variety of problems
Experience working with databases

What qualifications are required to become a Data Scientist?

To become a Data Scientist, you should have a Bachelor’s or Master’s degree in Computer Science, Software Engineering, Mathematics or a related field. Bachelor’s degree for an entry-level position and a Master’s degree for higher-level Data Scientist positions is required.

I am working as a Business Intelligence Analyst. Can this course help me to target roles such as Data Scientist?

If you are familiar with basic statistics and have some experience working with Machine Learning models, you can take this course to prepare for positions such as Product Analyst, Data Scientist, etc.

How to enroll for the Data Science Interview Course?

Learn more about Interview Kickstart and the Data Science course by joining the free webinar hosted by Ryan Valles, co-founder of Interview Kickstart.

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Course Overview

Start Learning

Students who chose to uplevel with IK got placed at

21,000+

Tech professionals trained

$1.28M

Highest offer received by an IK alum

53%

Average salary hike received by alums

Data Science Course details and curriculum

Sorting

Recursion

Trees

Graphs

Dynamic Programming

SQL Programming (interview-focused concepts and questions)

Probability

Distributions

Data Science Design: A/B testing

Regression, MLE, EM, and MAP

Supervised Machine Learning

Unsupervised Machine Learning

Deep Learning

Time Series Analysis

Interview Strategy and Success

Behavioral Interview Prep

Offers and Negotiation

15 mock interviews

Take classes you missed/retake classes/tests

1:1 technical/career coaching

Interview strategy and salary negotiation support

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Why choose this course?

Program designed by FAANG+ leads

Individualized teaching and 1:1 help

Mock interviews with Silicon Valley engineers

Personalized feedback

Career skills development

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