Well, as we approach the description still holds true! The world needs more data scientists than there are available for hire. On the other hand, there's large number of people who are trying to get a break in the Data Science industry, including people with considerable experience in other functional domains such as marketing, finance, insurance, and software engineering.
You might have already invested in learning data science maybe even at a data science bootcampbut how confident are you for your next Data Science interview?
This blog is intended to give you a nice tour of the questions asked in a Data Science interview. After thorough research, we have compiled a list of actual data science interview questions that have been asked between at some of the largest recruiters in the data science industry — Amazon, Microsoft, Facebook, Google, Netflix, Expedia, etc. If you want to know more regarding the tips and tricks for acing the interviews, watch the data science interview AMA with some of our own Data Scientists.
To be able to perform well, one needs to have a good foundation in not one but multiple fields, and it reflects in the interview. We've divided the questions into 6 categories:.
We've also provided brief answers and key concepts for each question. Once you've gone through all the questions, you'll have a good understanding of how well you're prepared for your next data science interview! As one will expect, data science interviews focus heavily on questions that help the company test your concepts, applications, and experience on machine learning. Each question included in this category has been recently asked in one or more actual data science interviews at companies such as Amazon, Google, Microsoft, etc.
These questions will give you a good sense of what sub-topics appear more often than others. You should also pay close attention to the way these questions are phrased in an interview. Machine learning concepts are not the only area in which you'll be tested in the interview. Data pre-processing and data exploration are other areas where you can always expect a few questions.
We're grouping all such questions under this category. Data analysis is the process of evaluating data using analytical and statistical tools to discover useful insights. Once again, all these questions have been recently asked in one or more actual data science interviews at the companies listed above. As we've already mentioned, data science builds its foundation on statistics and probability concepts. Having a strong foundation in statistics and probability concepts is a requirement for data science, and these topics are always brought up in data science interviews.
Here is a list of statistics and probability questions that have been asked in actual data science interviews.Since then, the hype around data science has only grown. Recent reports have shown that demand for data scientists far exceeds the supply. However, the reality is most of these jobs are for those who already have experience. Data scientists come from all kinds of backgrounds, ranging from social sciences to traditional computer science backgrounds.
Many people also see data science as a chance to rebrand themselves which results in a huge influx of people looking to land their first role. To make matters more complicated, unlike software development positions which have more standardized interview processes, data science interviews can have huge variations. Airbnb recognized this and decided to split their data scientists into three paths : Algorithms, Inference and Analytics.
Coding challenges can range from a simple Fizzbuzz question to more complicated problems like building a time series forecasting model using messy data. These challenges will be timed ranging anywhere from 30mins to one week based on how complicated the questions are. Challenges can be hosted on sites such as HackerRankCoderByteand even internal company solutions.
Preparing for a Technical Interview: Algorithms, Data Structures, and Computer Science
This will typically consider both correctness as well as complexity i. They may also be looking for code readability, good design, or even a specific optimal solution. HR screens will consist of behavioral questions, asking you to explain certain parts of your resume, why you wanted to apply to this company and examples of when you may have had to deal with a particular situation in the workplace.
Occasionally you may be asked a couple of simple technical questions, perhaps a SQL or a basic computer science theory question. Remember, interviews are a two-way street so it would be in your best interest to identify any red flags before committing more time to the interviewing with this particular company. Calls such as these are typically conducted using platforms such as Coderpadwhich includes a code editor along with a way to run your code.
Occasionally you may be asked to write code in a Google doc. Thus you should be comfortable coding without any syntax highlighting or code completion. Questions at this stage can range in complexity from a simple SQL question solved with a windows function to problems involving Dynamic Programming.
Regardless of the difficulty, you should always ask clarifying questions before starting to code. Once you have a good understanding of the problem and expectations, start with a brute-force solution so that you have at least something to work with. After you have something working, start to optimize your solution and make your code more readable. Ask them about coding standards and processes, how the team handles work, and what their day to day looks like.Placements preparation is a very crucial task and the choice of a good guide can help you a lot to prepare for the same.
You must choose a guide as per your own preference.Five Data Science Project Ideas
Many people like reading articles whereas many people prefer reading books. GeeksforGeeks brings you a list of the most recommended books that you must read for placement preparation. A good book covers every specific detail of the topic it is listing. This is the must-have book for any higher level competitive exams and interview preparation. Needless to say why the book is generally referred to as the Bible for any general aptitude exams. Not only does it cover the theoretical part but also discusses many tricks and shortcuts to solve a particular question.
Introduction to Algorithms. Focussing from the engineering point of view towards solving problems related to Dynamic Programming, Greedy Algorithms, and Flow Networks, this is the must-have book before the placement starts.
The book also covers the mathematical aspects of approach towards a problem and brings many new exercises and problems for students studying Data Structure and Algorithm. Data Structures and Algorithms Made Easy. Every company, hiring Software Developers have a series of interview rounds focused on Data structures and Algorithms. Written in a very simple and lucid format, the book covers all the major topics of placement interviews, programming puzzles and an immense number of programs asked by the big tech giants like Google, Microsoft etc.
The book also covers many GATE topics to help the students to qualify the interview rounds and other exams related to data structure and algorithms. Cracking the Coding Interview. The book prepares the candidate to pen down the brilliant algorithms in the form of flawless codes that would just get the right attention from the major tech giants.
Introducing Data Science [PDF]
The book guides the readers on how to break the codes and algorithms into bits and pieces and uncover the hidden techniques to manage those broken shells back into one. Covering more than programming interview questions peeled off from the best interviews around the world, this is the must-have book for all the CS students. Apart from coding questions, this book also covers behavioral round questions and system design round questions.
The trick in solving the design patterns questions is to look through the history of software problem-solving techniques and to study them and apply. Scripted with some best and successful experiences in the field of system and software designing, the book takes the user through the journey of producing the best real-time software and multi-sensory learning experience.
The book is crafted with the latest research and cognitive science to make the learning easy and interesting. Search More Books:. You can also take part in our mock placement contest Sudo Placement which will start from 10th July and will be conducted weekly.
Register for Sudo Placement. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.
See your article appearing on the GeeksforGeeks main page and help other Geeks. Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below. Writing code in comment? Please use ide.Goodreads helps you keep track of books you want to read.
Want to Read saving…. Want to Read Currently Reading Read. Other editions. Enlarge cover. Error rating book. Refresh and try again. Open Preview See a Problem? Details if other :. Thanks for telling us about the problem. Return to Book Page. In collaboration with data scientists, industry experts and top counsellors, we have put together a list of general data science interview questions and answers to help you with your preparation in applying for data science jobs.
The book covers most of the models, algori In collaboration with data scientists, industry experts and top counsellors, we have put together a list of general data science interview questions and answers to help you with your preparation in applying for data science jobs. The book covers most of the models, algorithms, definition and concept used by a data scientist. The author himself is a data scientist in one of the leading analytics company.
The topics covered are mostly statistical as any company hiring a data scientist or a business analyst looks for these concepts in the interviewing candidate. A must book for anyone looking forward to make a career in data science. Get A Copy. Kindle Edition46 pages. More Details Friend Reviews. To see what your friends thought of this book, please sign up. Lists with This Book. This book is not yet featured on Listopia. Community Reviews. Showing From must-know technical questions, to role-specific approaches and answer tips, this extensive guide will help you launch a successful career in data science.
I just finished reading the guide and WOW! Fantastic answer guide! Very recommended!! United Kingdom. Super comprehensive! Thank you so much for each of the questions and answers!
This is the best career resource I've seen. South Africa. Iliya is the co-founder of Data Science.
He has won more than 90 national and international awards and competitions through the years. All rights reserved. Expert Advice. Helping people launch and advance a career in data science has been our passion and focus for over 5 years. With 28 dedicated courses in data science and overstudents trained, we know exactly what it takes to land the data science interview.
Data Scientist has been named "best job" for four consecutive years by Glassdoor and Harvard Business Review. With this guide, you'll be fully prepared to embark on a new career as a Data Scientist.
Co-founder, Data Science. Ned is the co-founder of Data Science. Ned has rich experience in financial advisory, and has worked for renowned international companies, such as Pwc ItalyCoca-Cola United Kingdomand Infineon Technologies Germany. He is also the author of numerous career resources helping aspiring professionals reach their highest goals. Ellie is a Computational Biologist, with expertise in the fields of algorithms and data structures, phylogenetics, and population genetics.
She has a solid academic background in Bioinformatics with publications on constructing Phylogenetic Networks and Trees. Elitsa is one of the authors of the course Customer Analytics in Python in the data science Program and is currently creating the upcoming Data Visualization Course. Python, R and ML Instructor. Python and SQL Instructor. Technical questions, how-to-answer guides, best practices, example replies, and more are covered in this comprehensive guide.
The best way to do that is by building small projects. Building projects is an effective strategy for the following two reasons:.
Recent data shows that Python is still the leading language for data science and machine learning. This is a perfect reference to keep close by for those frequent data manipulation tasks using Pandas. Think Python reviews everything from the basics of data structures and functions, to more advanced topics such as classes and inheritance. Every few chapters this book ties together key concepts with case studies. This is a great way to reinforce learning new concepts. Action Step : Work through the case study in Chapter 13 on data structure selection.
If you want to make yourself marketable to employers and stay current with your data science skills, you should have a good handle on R. A recent poll of the data science community indicated that It covers the basics for new R users, such as data cleaningbut also gets into more advanced topics as well.
This book is a great general R reference from Hadley Wickham and Garret Grolemund, two of the top developers in the R community. Action Step : Use this chapter to perform an exploratory analysis. You can explore this housing dataset and document your findings using an Rmarkdown notebook. Make sure you put your project on your github page and link to it from the projects section on your linkedin profile.
If you really want to set yourself apart as an R user and impress employers, Advanced R is a great resource. It covers everything from the foundations, including data structures, object oriented programming, and debugging, to functional programming and performance code.
One R user was able to achieve a performance speed up of over X using Rcpp. Introduction to Statistical Learning is one of the best introductory textbooks for machine learning. The effect that TV vs online ad spending has on sales is a perfect application of linear models for interpretability. Action Step : Use chapter 4 on Classification to implement a logistic regression model. Use this credit card dataset to predict defaults. If you want to accelerate your machine learning career, you need to have a strong grasp on both fundamentals, and advanced topics.
The Elements of Statistical Learning is the perfect resource for bringing your machine learning skills to the next level. Use this housing dataset to predict housing prices. Use the Scikit-Learn implementation of linear regression with all of the features, and then use Ridge Regression and the Lasso to select the most important features.
A great way to gain a deep, lasting understanding of machine learning topics is to implement them from scratch. This book provides extensive theory on the algorithms to help you. This is a great book developed from various Stanford courses on large scale data mining and network analysis. Large companies like Google receive hundreds of millions or more search queries per day, so they are especially interested in mining very large datasets.
Complete exercise 5.In collaboration with data scientists, industry experts and top counsellors, we have put together a list of general data science interview questions and answers to help you with your preparation in applying for data science jobs. The book covers most of the models, algorithms, definition and concept used by a data scientist.
The author himself is a data scientist in one of the leading analytics company. The topics covered are mostly statistical as any company hiring a data scientist or a business analyst looks for these concepts in the interviewing candidate.
A must book for anyone looking forward to make a career in data science. Read more Read less. Customers who bought this item also bought. Page 1 of 1 Start over Page 1 of 1. Cracking The Machine Learning Interview. Nitin Suri. Vishwanathan Narayanan. Python Interview Questions. Machine Learning using Python. Manaranjan Pradhan. What other items do customers buy after viewing this item? Customer reviews. How are ratings calculated?
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