We source interview questions from the top tech companies ranging from SQL problems, algorithms, machine learning concepts, product intuition, statistics, and more. Solve three questions a week given on Monday, Wednesday, and Friday and join the discussion online. Upgrade to the full course and learn how to ace interview questions with our in-depth solutions, take-home challenges, and community of data science candidates and professionals. Assume bin buckets intervals of one.
Try our data science interview question quiz to see where you fall on the distribution. Interview Query. Blog Login Premium. In-depth solutions from companies hiring data scientists. How It Works. Practice data science concepts We source interview questions from the top tech companies ranging from SQL problems, algorithms, machine learning concepts, product intuition, statistics, and more. Learn and solve data problems Solve three questions a week given on Monday, Wednesday, and Friday and join the discussion online.
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Where do you fall on the distribution? Start Learn more. Join hundreds of other data scientists. Get ready for your next interview with our community of data science mentors and professionals as we land our dream jobs. Join the community. Get insider details on the interview processes at various top tech companies. The Interview Query question bank definitely helped me see the commonalities in each question for different companies and cleared up how to efficiently spend my time studying.
Read Full Story. Jerry K. Sign up for updates. Free problems every week! Sign up! This popup does not exist. I'm sorry.There is an increased demand for careers in data science, data analytics, and programming, the need for a data analyst is higher. If we are interested in problem-solving, communicating knowledge with others, data analyst careers would be the best.
Most data analysts aim for large-scale data such as Hadoop. The basic skills required to be a data analyst are learning Scripting and statistical language, advanced excel, knowledge of SQL, need good presentation skills for reporting and visualizing data, database design data mining, cleaning. Their job is to collect the data and use it to help companies to make better business decisions. They work with large amounts of data like figures, facts, raw data, and numbers, need to see through the data and analyze it to give out the final predictions.
They typically use systems and calculation applications to figure out the numbers. The main goals of a data analyst are discovering useful information and supporting decision making in their business. The data analyst will gather and retrieve the data, process it to give out meaningful information to the outside user. Their work varies depending on the type of data they are given sales, social, inventory. They spend their time developing systems for collecting data and making their implementation into reports that will help their company in leading.
Now, if you are looking for a job that is related to Data Analyst then you need to prepare for the Data Analyst Interview Questions. It is true that every interview is different as per the different job profiles.
Here, we have prepared the important Data Analyst Interview Questions and Answers which will help you get success in your interview.
How Food Delivery Apps are leveraging Big Data Analytics?
In this Data Analyst Interview Questions article, we shall present 10 most important and frequently used Data Analyst interview questions. These questions will help students build their concepts around Data Analyst and help them ace the interview. Answer: A data analyst collects data from different sources and analyses the result using different Statistical techniques.
The main responsibilities are to generate insights from data and produce the result to the external clients. There is a huge opportunity in biotechnology and manufacturing industries. The human genome project is an example. Answer: Excel is used for a variety of purposes as generating summaries and presenting it in an interactive Excel dashboard for easy understanding. Cross-tabulation is done in excel by using a pivot table. Some of the problems faced by data analyst are.
Answer: During this process, the unwanted data are sorted out and all the possibilities of error are ruled out in order to improve the quality of the data. The best way to clean data is:. Answer: The difference is that understanding of computer science and analyzing the data with scale.
Data scientists need only basic concepts of statistics, and newly developed tools are more and more helpful for data scientists. The role of data scientists and data analyst are undefined and varied by own skill set and the industries. Data scientists can successfully grow into data analyst. Answer: Data Analyst is responsible for database design and security. They upgrade the database on a regular base so that it meets the demand of the market needs.
The procedures to analyze the data are:. The different variable techniques are. Answer: Missing values are replaced by the mean value of series in time series data.Food industry is adopting big data technology and big data analytics to stay competitive by understanding customer preferences and tastes.
People have pretty high expectations for food especially when they are ordering it from a restaurant. The mind-set of people is tuned in such a way - that they want each and every bite of the food to be perfect in taste whether it is the cheesy pizza, their favourite Ham, frozen lasagne or the fries. Restaurants and food delivery apps are revolutionizing the food industry in novel and surprising ways to make sure that the food tastes the same every time and is always on time.
Here are some examples on how restaurants and food delivery apps are achieving an increment in revenue by adding big data analytics on their menu to understand customer tastes and preferences. With the growth of online food delivery industry, the investments in the food delivery business have increased exponentially - from 25 million dollars in ; 46 million dollars in ; million dollars in and it was expected to touch 1 billion dollars by end of Every food delivery chain, restaurant, grocery store, and cafeteria- all businesses in the food industry generate data in the form of customer orders, delivery location, GPS, tweets, images, reviews, blogs, updates, etc.
The data generated relates to average wait time, experience with the delivery, taste of the food, menu availability, loyalty card points and product inventory levels. As mobile trend moves rapidly, food delivery businesses are now combining all the unstructured big data with transactional and sales data for tendency and sentiment analysis — to leverage mobile app analytics that can help them build brand image and affinity towards customers, thereby increasing sales.
With 6 million registered customers in 21, stores across 62 countries, 87, possible drinking combinations and serving 4 billion cups annually - Starbucks, the popular coffee maker is grinding petabytes of data to leverage big data analytics for refined customer experience. Starbucks collects data on - when and what customers order to provide them with personalized offers on their favourite coffee. As people become more health conscious about what they eat there is huge demand for fresh and unprocessed items on restaurant menus.
McDonalds, the biggest fast food chain is having a tough time rendering a healthy food experience to its diners.
Infor the first time ever, since its inception in the US, McDonalds has closed more stores than it has opened. The only thing that stops growth is relevancy to the customer. McDonald's management team is keenly focused on acting more quickly to better address today's consumer needs, expectations and the competitive marketplace. There are many food delivery startups struggling hard with big data to gain competitive edge in the battle of the food delivery apps.
Chicago-based GrubHub is making big moves and has entered the on demand food delivery wars by tapping into big data. When the dining halls are closed and the fridge is empty-GrubHub has a special place in the heart of hungry diners. With 5 million active diners, 30, take out restaurants across cities, open 24x7 and filling an average oforders everyday —GrubHub is using analytics to get insights into the American Stomach to fulfil every food desire.
GrubHub uses data about millions of orders made on their platform to help hungry diners choose the right dish from the right restaurant. So next time on a date-do order a Pizza. Men and Women order Pizza at the same rates besides fries and soda. GrubHub is leveraging the results of data analysis based on demographics, gender, age, seasonality and ethnicity to provide its hungry diners with lip-smacking food that they are likely to enjoy on-the-go.
GrubHub uses big data to identify patterns in takeout ordering, find the differences between gender specific tastes.
This analysis helps restaurants make best use of the menu by providing recommendations and suggestions to diners on what kind of food they are likely to enjoy. Having delivered 3 million meals till date, within 20 to 40 minutes-Munchery is shaking up the food industry by tapping into the data to deliver tasty and quality chef-made gourmet take-out food to its customers.
Munchery does not feature the same menu in two cities. Each chef at Munchery specializes in a different cuisine and they have the menu or cuisine that is popular in that city. Munchery website has menus uploaded on their website so that customers can leave feedback about a particular menu similar to Amazon product review. Munchery leverages this data for analysis to identify what menu items need improvement or which items should be eliminated from the list.
Big data at Munchery helps them refine their delivery menu right from the dishes, ingredients and flavours to best suit the needs of their customers. Munchery uses Desk.
Learn Hadoop Online to gorge out the world of big data analytics in the food industry. The secret ingredient behind the quick deliveries is the big data analytics used to estimate how many customers will order a particular menu item, when and from where. The wow factor as to why people love Sprig is because they proactively position their supplies through big data analysis, to make sure that the delivery time is low. Data science team at Sprig does heuristic predictive analysis to ensure that customers enjoy delicious food in short span of time.
What more does a hungry stomach need than food that is hot and tastes good and shows up in just 20 minutes? Nothing apparently.These pages are meant to provide helpful information about how to get a software engineering, product manager, data science, and designer job at DoorDash. Being prepared and knowledgeable is a key to every step of the hiring process. You can tab through each part of the guide to see information that can be helpful to your stage from office location for those trying to figure out if a company has a presence in your city of choice to real world interview questions.
We hope you find these helpful and if you have content that you think we should add or think we got anything wrong, please email us at corrections pathrise.
Stage 3: Onsite interview. The onsite is 5 hours long and includes behavioral questions and technical interview questions about coding, architecture, and past projects. The interview process for a data scientist can take weeks Stage 1: Phone screen with recruiter Stage 2: Phone interview with hiring manager Stage 3: Take home dataset analysis with SQL Stage 4: Onsite interview. The interview process for a product manager can take weeks Stage 1: Phone screen with recruiter Stage 2: Take home assignment to come up with product recommendations for DD Stage 3: Assignment review with Director of Product Stage 4: On-site interview, which includes presentation of assignment.
Mission At DoorDash, we re working to empower local communities and in turn, creating new ways for people to earn, work, and thrive. We believe in delivering good by connecting people and possibility. Vision Ultimately, our vision is to build the local, on-demand Fedex. We are a logistics company more so than a food company.
We help small businesses grow, we give underemployed people meaningful work, and we offer affordable convenience to consumers. We're tackling some of the most difficult logistical challenges that come with on-demand delivery both in engineering and in operations. Get hired. About this guide These pages are meant to provide helpful information about how to get a software engineering, product manager, data science, and designer job at DoorDash.
Get Hired. What we know. DoorDash is a unicorn tech company They attempt to keep the culture casual and friendly, but it is difficult as the company continues to grow Some employees feel like there has been a lot of instability in the company goals Good perks, like free food and flexible work schedules. Describe one past experience on your resume What 3 things do you look for the most in a company? Have you used our service?
Tell me about your current job. Why DoorDash? What are you looking for in your next role? Tell me about a difficult project that you've worked on and walk me through how you solved it. Design web backend which given a stock, finds the best price start, end date from past history using stock API from Quandl, Yahoo, Intrinio Sort an array of integers, given the max value will be N.
Design a database for a simplified Twitter What problems would come if we scaled? Find intersecting intervals for 2 lists of overlapping intervals. Given an array of integers, a start point aand an end point, print out all the broken intervals. How do you test your features? Code a stack data structure How much do you value diversity? What project s have you worked on that demonstrate your skills? Provide a set of recommendations on how to improve our business or product based on the attached dataset.
Please state your hypothesis, describe how you would structure your experiment, list your success metrics, and describe the implementation.As a result of their growth, they need to grow their data science team to help scale their business. DoorDash is aware of the importance of data and the need for a high-energy, confident, and well-experienced data scientist. Additionally after that, any company with marketplace effects. Analytics Data Scientist: This team focus on experimental analysis with emphasis on building dashboards and doing the analysis that supports specific business goals.
Machine Learning Engineers: This team focus on building the bulk of the infrastructure for deploying models. Data Science Machine Learning: This team sits right in the middle of the former two.
They build models that focus on business impact. Their focus is on experimental analysis, building recommendation systems and features, building pipelines for recommendations, designing marketing attribution and segmentation, and building sales models. Although these three teams are separate and work independently, in some cases they work very cross-collaboratively. The application process on DoorDash is not too different from the application processes of most tech companies.
The process starts with:. A data scientist will ask a few questions on how you crafted the solution and go through your thought process. After applying for the job, you will get a phone interview with a recruiter. This initial phone call interview by a recruiter usually last for 30 minutes. You will be asked a few questions about projects and background related to data science.
The Square Data Scientist Interview
Want a preview of the DoorDash take-home challenge? Need a take-home challenge review? We have both at Interview Query. The analytics take home-home challenge is divided into two segments.
This first part involves analysis on data set using a case study data provide. The data science machine learning take-home challenge is also two parts. The first part requires building a model to predict delivery duration while the second part is to create an application that can serve the model from part 1.
Ace your data science interview
At this point, you will be asked a series of questions about the techniques used. The interviewer is just trying to get a grasp of your thought process and understanding why you made certain decisions. The on-site interview lasts for about 5 hours with a lunch break in-between. You will be introduced to the data scientist team along with other team members that work closely together. During the on-site interview, you may be given a real-life DoorDash problem to work on and present to the interview panel as various team members pair program with you.
Depending on the type of data science role, expect it to be heavy on either analytics or building a machine learning model. The Data Science Role at DoorDash DoorDash is aware of the importance of data and the need for a high-energy, confident, and well-experienced data scientist. Analyzing the data using quantitative analysis to provide insights on how to best help business and product leaders understand user behaviors, marketplace dynamics, and market trends. Forecast the supply of available dashers as well as incoming delivery demand.
Build models for next-generation pricing and pay algorithms. Predict preparation time for over 50, merchant partners.Square, Inc. Founded in and based in San Francisco, California, the company develops and markets hardware and software payment products that combine merchant services and mobile payments into a single, easy-to-use platform.
Out of their many robust products and services, people are most likely to be familiar with the Square iPad point-of-sale system used in many businesses across the country. Square generates billions of monthly transactional data which forms the basis of analysis at the company. The data science role at Square varies greatly depending on the unique goals and priorities of each team and cuts across a wide range of data science and analytics concepts.
Thus, the tools and skills required may also range from basic analytics to write code to deploy machine learning systems. The term data science at Square encompasses a wide scope of fields related to data science. More specifically, the data scientist role at Square may include one or more of the following team-specific responsibilities:.
Square follows a similar hiring process to other big tech companies, with the exception of not administering a take-home challenge. The interview process starts with an initial phone call from HR to discuss past relevant experience and expertise. After passing the initial screen, candidates proceed to a technical interview one or two in some cases with a hiring manager and data scientist.
If successful, an onsite interview will then be scheduled. This interview consists of four to five one-on-one interview rounds with several team members and managers. The initial interview is a minute non-technical phone screen with HR or a hiring manager. The interviewer will ask about your past relevant projects as a data scientist to determine if you are a good fit for the team. This interview is collaborative and it is done via coderpad. Candidates must be ready to write code on a whiteboard in both SQL and Python along with a pair programming exercise.
The Data Science Role at Square The data science role at Square varies greatly depending on the unique goals and priorities of each team and cuts across a wide range of data science and analytics concepts. Other relevant qualifications include: An advanced degree M. Experience deploying machine learning e.
Proficiency in any of the following scripting languages Python, Java, etc. Experience with building complex, scalable ETLs for a variety of different business and product use cases. Technical expertise in building personalization, ranking or recommendation systems that scale, with a fundamental understanding of machine learning algorithms and statistics.
Data Science Teams at Square The term data science at Square encompasses a wide scope of fields related to data science. Growth Data Science Team: Leverage data and automation to help Square solve impactful business, marketing, and growth problems such as lifetime value forecasting, churn prediction, attribution modeling, causal inference, and more.
Customer Support Automation Cash App : Build models that anticipate customer issues and deliver proactive in-app suggestions, use NLP to contextualize inquiries and respond instantly with relevant content, develop prioritization algorithms that improve efficiency, and apply the latest research to automate conversations with customers. Risk Cash App : Build machine learning models that detect fraudulent activity in real-time, develop new product features that drive down risk losses, experiment with state-of-the-art algorithms to decrease false positives, use any and every dataset at your disposal including 3rd party data to engineer new features for risk models, verify customer documents using OCR, and use biometric and device signals to detect malicious logins and account takeovers.
Compliance Team: Build and automate actionable reporting, define KPIs, build ETLs, and build dashboards for key compliance processes to improve the overall compliance infrastructure and platforms. Embedded Product: Leverage engineering, analytics, and machine learning to empower data-driven decision making in the full life cycle of product development while working cross-functionally across many different team organizations The Interview Process Square follows a similar hiring process to other big tech companies, with the exception of not administering a take-home challenge.
Example Questions: Can you give me the top 5 records and the maximum date for each? Explain what metrics we should use to evaluate a binary classification model? The Square Data Scientist onsite interview will most likely consist of: A coding and algorithms interview involving pair programming A data exploration interview also involving lots of coding A machine learning interview that involves white-boarding and explaining some of the fundamentals of machine learning concepts A statistics interview that involves testing your knowledge of basic statistical concepts An analytics interview which involves metrics definitions and applications A culture fit interview with a product manager to determine if you are a good fit for the team and the company as a whole.
Notes and Tips Remember, the interview process aims to assess how you can apply analytics and machine learning concepts to solve business problems, develop new features, and improve the customer experience. Since you are likely going to be white-boarding, it may be useful to practice coding on a whiteboard before the interview.
Also, Square uses a shared coding environment for most of its technical interviews as it allows the interviewer to assess your technical ability at a glance while simultaneously getting a feel for your specific process.
If you are new to pair programming interviews, we recommend reading up on the concept. It can be incredibly helpful to practice communicating your thought process and coding decisions out loud while writing code.Data cleaning also referred as data cleansing, deals with identifying and removing errors and inconsistencies from data in order to enhance the quality of data.
Logistic regression is a statistical method for examining a dataset in which there are one or more independent variables that defines an outcome. Data profiling: It targets on the instance analysis of individual attributes. It gives information on various attributes like value range, discrete value and their frequency, occurrence of null values, data type, length, etc. Data mining: It focuses on cluster analysis, detection of unusual records, dependencies, sequence discovery, relation holding between several attributes, etc.
Hadoop and MapReduce is the programming framework developed by Apache for processing large data set for an application in a distributed computing environment. In KNN imputation, the missing attribute values are imputed by using the attributes value that are most similar to the attribute whose values are missing. By using a distance function, the similarity of two attributes is determined. There are two types of Outliers. Hierarchical clustering algorithm combines and divides existing groups, creating a hierarchical structure that showcase the order in which groups are divided or merged.
K mean is a famous partitioning method. Collaborative filtering is a simple algorithm to create a recommendation system based on user behavioral data. KPI : It stands for Key Performance Indicator, it is a metric that consists of any combination of spreadsheets, reports or charts about business process.
Design of experiments : It is the initial process used to split your data, sample and set up of a data for statistical analysis.
Map-reduce is a framework to process large data sets, splitting them into subsets, processing each subset on a different server and then blending results obtained on each.
Clustering is a classification method that is applied to data. Clustering algorithm divides a data set into natural groups or clusters.
Time series analysis can be done in two domains, frequency domain and the time domain. A correlogram analysis is the common form of spatial analysis in geography. It consists of a series of estimated autocorrelation coefficients calculated for a different spatial relationship.
About this guide
In computing, a hash table is a map of keys to values. It is a data structure used to implement an associative array. It uses a hash function to compute an index into an array of slots, from which desired value can be fetched.
A hash table collision happens when two different keys hash to the same value. It searches for other slots using a second function and store item in first empty slot that is found. List out different types of imputation techniques?
During imputation we replace missing data with substituted values. Although single imputation is widely used, it does not reflect the uncertainty created by missing data at random. An n-gram is a contiguous sequence of n items from a given sequence of text or speech. It is a type of probabilistic language model for predicting the next item in such a sequence in the form of a n Your email address will not be published.
Everything else is great though!