Common Data Science Interview Questions
Here are nine of the most frequently asked data science interview questions:
- Why do you want to work at this company as a data scientist?
- How did your previous work experiences prepare you for a role as a data scientist?
- How do you overcome any professional challenges?
- What tools and devices do you plan to use in your role as a data scientist?
- What is selection bias, and why do you need to avoid it?
- How do you organize big sets of data?
- Is having large amounts of data always preferable?
- What is root cause analysis?
- How do you usually identify outliers within a data set?
Which Book To Buy For Your Next Interview
After investigating each of these guides, it has become evident that theres no one perfect book that can effectively cover all the possible topics for a successful data science interview. Ultimately, in 2020, a guide book may not be the best way to learn and prepare.
Innately in data science exists the necessity of practicing SQL, Python, or R in an interpreter, as well as a general need for actively updating content foundations with the field rapidly changing over time with new technological processes. This simply isnt possible with a book, which is why we ultimately recommend seeking out other online resources for your needs.
Data Science Interviews Exposed
Written by a collective of data scientists, “Data Science Interviews Exposed” was one of the first data science interview guide books available on the market. In addition to the standard technical interview topics present in many similar texts, this book reviews job search procedures and standard screening interview processes.
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Some Final Words On Data Scientist Interview Questions
Interviewing for a data scientist position can be a bit scary at first. So, just in case youre still not as confident as you should be, as a final takeaway, remember the following:
- Listen carefully to everything that the interviewer mentions in the questions, emphasize on clear explanation, and on your thought process
- Even if your explanations arent perfect and you need some assistance from the interviewer, thats not a bad thing. In fact, it signals the interviewer youre open to receiving help, can handle feedback and would probably be a solid team-player
- Communication is key exude a positive attitude, demonstrate professionalism, and be confident in your abilities. Keep in mind your tone of voice and pacing, as well as your gestures. Your body language speaks volumes! That said, you can find more about the types of non-verbal communication and how to improve your body language in this Indeed article.
- Learn from the process if you havent been successful. Discuss the challenging data scientist interview questions you couldnt answer during the interview with a friend or colleague and try to find a solution. That will take off the edge and will make you feel more at ease the next time you encounter a similar problem.
Q5 What Is A Confusion Matrix
The confusion matrix is a 2X2 table that contains 4 outputs provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity, precision and recall are derived from it. Confusion Matrix
A data set used for performance evaluation is called a test data set. It should contain the correct labels and predicted labels.
The predicted labels will exactly the same if the performance of a binary classifier is perfect.
The predicted labels usually match with part of the observed labels in real-world scenarios.
A binary classifier predicts all data instances of a test data set as either positive or negative. This produces four outcomes-
True-positive Correct positive prediction
Basic measures derived from the confusion matrix-
Error Rate = /
Accuracy = /
Sensitivity = TP/P
Specificity = TN/N
Precision = TP/
F-Score = / where b is commonly 0.5, 1, 2.
Q6. Describe Markov chains?
The above figure represents a Markov chain model where each step has an output that depends on the current state only.
An example can be word recommendation. When we type a paragraph, the next word is suggested by the model which depends only on the previous word and not on anything before it. The Markov chain model is trained previously on a similar paragraph where the next word to a given word is stored for all the words in the training data. Based on this training data output, the next words are suggested.
Q9. What is the ROC curve?
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How Do You Overcome Any Professional Challenges
This question allows you to showcase your problem-solving and critical thinking skills in the workplace and within a team environment. Data scientists often handle complex problems, so your answer should demonstrate your ability to overcome obstacles and remain focused while finding solutions. Select a particular project or moment in which you overcame a challenge by using your skills to illustrate your potential with the company.
Example:In a team environment like this one, I feel its best to have an open discussion with my colleagues to discover ways in which we can overcome an issue. At my previous job, my team was responsible for analyzing a new subset of data for the marketing department. we were given the task of going through a large amount of data but there were no clear guidelines on what each team member was responsible for. I organized a meeting with all team members and our managers to clearly outline everyones tasks. As a result, we created an efficient system for delegating tasks when given new projects.
Q115 What Is A Boltzmann Machine
Boltzmann machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimise the weights and the quantity for the given problem. The learning algorithm is very slow in networks with many layers of feature detectors. Restricted Boltzmann Machines algorithm has a single layer of feature detectors which makes it faster than the rest.
Q116. What Is Dropout and Batch Normalization?
Dropout is a technique of dropping out hidden and visible units of a network randomly to prevent overfitting of data . It doubles the number of iterations needed to converge the network.
Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one.
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Data Science Interview Questions And Answers
Preparing for an interview is not easythere is significant uncertainty regarding the data science interview questions you will be asked. No matter how much work experience or what data science certificate you have, an interviewer can throw you off with a set of questions that you didnt expect.
During a data science interview, the interviewer will ask questions spanning a wide range of topics, requiring both strong technical knowledge and solid communication skills from the interviewee. Your statistics, programming, and data modeling skills will be put to the test through a variety of questions and question styles that are intentionally designed to keep you on your feet and force you to demonstrate how you operate under pressure.
Preparation is the key to success when pursuing a career in data science, and that includes the interview process.
This guide contains all of the data science interview questions you should expect when interviewing for a position as a data scientist.
We previously created a free data science interview guide, yet we still felt we had more to explore. So we curated this list of real questions asked in a data science interview. From this list of data science interview questions, an interviewee should be able to prepare for the tough questions, learn what answers will positively resonate with an employer, and develop the confidence to ace the interview.
Q103 How Does An Lstm Network Work
Long-Short-Term Memory is a special kind of recurrent neural network capable of learning long-term dependencies, remembering information for long periods as its default behaviour. There are three steps in an LSTM network:
- Step 1: The network decides what to forget and what to remember.
- Step 2: It selectively updates cell state values.
- Step 3: The network decides what part of the current state makes it to the output.
Tips For Data Science Case Interview
To learn how to answer data science case study interview questions, you need to practice. You may think you know everything you need to know because you have a high-powered degree. But the data science case study is a highly specialized process. Resting on your laurels is a surefire way to be outshined by your competitors and wind up looking unprepared. Consider case interview coaching.
Management Consulted provides expert training in every facet of the interview process, including the case study. Our proven techniques will help you refine the case interview skills you already have and develop new ones. If you cant afford coaching, arrange to practice with friends and peers, assuming they have some degree of expertise. Have everyone generate their own insights and presentations from the same case interview practice cases, then present to one another. Seeing how other people work with the same material will help you increase your creativity. But be careful about taking too much advice from someone who may not be an expert on the case study data science interview.
TELL A STORY!
LEARN TO EMPATHIZE!
Data Scientist Interview Questions And Answers 2021
The 365 Team
Data Scientist Interview Questions – Why You Should Practice Them?
Landing an awesome data scientist job isnt just a luck of the draw. Above all, its a matter of preparation. But even if youre an aspiring data scientist whos super dedicated to the task, you might find yourself struggling in the process. Why? The reasons are two-fold:
First, the data scientist interview format can vary greatly depending on the company you apply at.
Second, data scientist interview questions cover a wide scope of multidisciplinary topics. That means you can never be quite sure what challenges the interviewer might send your way.
So, we pulled our data science brains together, got in touch with recent hires and interviewers, and compiled a punchy interview guide. First, well discuss the best possible preparation in terms of data science skills and qualifications. Then, well list the data scientist interview questions youre most likely to get . Finally, well let you in on the specifics of the data scientist interview process in 3 major companies.
Q107 What Is Exploding Gradients
While training an RNN, if you see exponentially growing error gradients which accumulate and result in very large updates to neural network model weights during training, theyre known as exploding gradients. At an extreme, the values of weights can become so large as to overflow and result in NaN values.
This has the effect of your model is unstable and unable to learn from your training data.
Tell Me About An Experience Working On A Multi
A Data Scientist collaborates with a wide variety of people in technical and non-technical roles. It is not uncommon for a Data Scientist to work with developers, designers, product specialists, data analysts, sales and marketing teams, and top-level executives, not to mention clients. So in your answer to this question, you need to illustrate that you’re a team player who relishes the opportunity to meet and collaborate with people across an organization. Choose an example of a situation where you reported to the highest-level people in a company to show not only that you are comfortable communicating with anyone, but also to show how valuable your data-driven insights have been in the past.
- Can you tell me about a time when you demonstrated leadership capabilities on the job?
- How do you go about resolving conflict?
- How do you prefer to build rapport with others?
- Talk about a successful presentation you gave and why you think it went well.
- How would you explain a complicated technical problem to a colleague/client with less technical understanding?
- Describe a time when you had to be careful talking about sensitive information. How did you do it?
- Rate your communication skills on a scale of 1 to 10. Give examples of experiences that demonstrate the rating is accurate.
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Q79 How Will You Define The Number Of Clusters In A Clustering Algorithm
Though the Clustering Algorithm is not specified, this question is mostly in reference to K-Means clustering where K defines the number of clusters. The objective of clustering is to group similar entities in a way that the entities within a group are similar to each other but the groups are different from each other.
For example, the following image shows three different groups.
Within Sum of squares is generally used to explain the homogeneity within a cluster. If you plot WSS for a range of number of clusters, you will get the plot shown below.
The Graph is generally known as Elbow Curve.
Red circled a point in above graph i.e. Number of Cluster =6is the point after which you dont see any decrement in WSS.
This point is known as the bending point and taken as K in K Means.
This is the widely used approach but few data scientists also use Hierarchical clustering first to create dendrograms and identify the distinct groups from there.
Q77 What Are The Various Steps Involved In An Analytics Project
The following are the various steps involved in an analytics project:
Understand the Business problem
Explore the data and become familiar with it.
Prepare the data for modelling by detecting outliers, treating missing values, transforming variables, etc.
After data preparation, start running the model, analyze the result and tweak the approach. This is an iterative step until the best possible outcome is achieved.
Validate the model using a new data set.
Start implementing the model and track the result to analyze the performance of the model over the period of time.
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Data Science Teams At Deloitte
The data science and analytics team at Deloitte works with other teams to unlock business opportunities through applied data decision-making generated from client data. Given the cross-functional aspect of the role data scientists play, job duties and responsibilities can span from business or machine learning analytics to predictive modeling.
Analytics and Cognitive: Focus on leveraging the power of data analytics, mathematical techniques, and predictive modeling to uncover hidden relationships from vast troves of data. Work with clients to implement large-scale data ecosystems, including data management, governance, and the integration of structured and unstructured data to generate insights towards leveraging cloud-based platforms.
Government and Public Services : Leverage sales and pipeline analytics of Deloittes Customer Relationship Management system to support and improve GPS sales. Apply natural language processing and machine learning models to facilitate research and gather/design processes to mine various business developments to identify ways of improving growth rates.
DevOps: Collaborate with data engineers to build and maintain cutting-edge AI solutions that provide clients with real-time customer insights. Collaborate with the data science team to deliver production-level grade pipelines, including building ETL jobs to ingest data into a database, curating and cataloging metadata about ETL datasets, and implementing data model solutions.
Framework For Tackling Product Questions
There’s a couple different frameworks out there tackling product interview questions. At Interview Query we have combined these frameworks with an additional focus on metrics and applied data that employers are expecting from data scientist candidates.
While product managers cover a decent amount of this in their interviews, data scientists can focus less on the end user experience and more on larger behavioral changes from their users. Therefore a good general framework should be:
1. Clarifying the Question
What are the product goals? What’s the background context? We almost always start at an information disadvantage. So what questions do we need to ask to bridge the gap?
2. Make Assumptions
Make some assumptions about the problem to narrow the scope. State what you’ll explore in your analysis and what you won’t.
3. Analyze User Flows
Examine exactly how the product works. How does a user get to a certain feature? How does a user use a certain feature? What kinds of different users are there?
4. Define Hypothesis
Start hypothesizing situations to explore that would help understand the root cause of the issue.
5. Draw Metrics to Support your Hypothesis
Use metrics as an example to further illustrate how it could prove or disprove your hypothesis.
6. Tie Your Analysis to the Product Goals
Finally tie your analysis back to the product goals. Give some sort of summary statement that can prioritize which ones matter and what the next steps are.
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What Are The Most Important Tools And Technical Skills For A Data Scientist
Data science is a highly technical field and you will want to show the hiring manager that you’re adept with all of the latest industry-standard tools, software, and programming languages. Out of the various statistical programming languages used in data science, R and Python are most commonly used by Data Scientists. Both can be used for statistical functions such as creating a nonlinear or linear model, regression analysis, statistical tests, data mining, and more. Another important data science tool is RStudio Server, while Jupyter Notebook is often used for statistical modeling, data visualizations, machine learning functions, etc. Of course, there are a number of dedicated data visualization tools used extensively by Data Scientists, including Tableau, PowerBI, Bokeh, Plotly, and Infogram. Data Scientists also need plenty of experience using SQL and Excel.
Your answer should also mention any specific tools or technical competencies demanded by the job you’re interviewing for. Review the job description and if there are any tools or programs you haven’t used, it might be worth becoming familiar with before your interview.