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Data Scientist Interview
Questions

20 Questions and Answers by William Swansen

Updated January 4th, 2020 | William Swansen is an author, job search strategist and career advisor who assists individuals from all over the world.
Question 1 of 20
Data visualization is an important skill that will be used often when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.
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How to Answer
Data Science and analysis of complex data sets is a very technical discipline. However, the organization's stakeholders who use the results of the analysis need to be able to clearly understand what the data is telling them and be able to use it to improve their operations and help them make business decisions. You need to be able to present your work in a manner that is easy to understand and utilize. This is known as data visualization. The interviewer is seeking to understand how you organize and present the data to accomplish this objective.
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1.
Data visualization is an important skill that will be used often when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.
Data Science and analysis of complex data sets is a very technical discipline. However, the organization's stakeholders who use the results of the analysis need to be able to clearly understand what the data is telling them and be able to use it to improve their operations and help them make business decisions. You need to be able to present your work in a manner that is easy to understand and utilize. This is known as data visualization. The interviewer is seeking to understand how you organize and present the data to accomplish this objective.

William's Answer #1
"While the process of analyzing data is important, it is critical that the results of the analysis be useful to the stakeholders in the organization. It is important to understand what the stakeholder's objectives are when deciding how to present my results. One method I've found to be effective is to add graphs, pictures, and illustrations to my presentations and reports. Once, when presenting an analysis of customer usage trends for a product our organization sold, I incorporated images of the product and animated them to expand in size in relation to the growth in customer adoption, adding the statistics and actual growth numbers. The audience remarked about how clear this made the information."
William's Answer #2
"I am always cognizant of how my data analysis will be used by the stakeholders in our organization and seek to present my results in a manner that is relevant to the audience and in terms they can easily understand. I often use visual representations of the data along with the actual numbers to achieve this. Recently, when presenting a study on the implementation of process improvement and the results it generated, I used a photo of the actual production line, and animated it, increasing the speed of the line as the process was improved and the production times were reduced. I included the actual percentages of the time saved above the line so the management team could easily see the results which were achieved."
2.
Can you describe how Data Analysis is used by businesses and other organizations?
While this appears to be another Technical Question, it is actually more of a General Question. The interviewer is likely to ask this early in the interview to help establish a conversational tone for the interview and develop some avenues to follow up questions. As with any interview question, your answer should relate to the company's operations and how you believe they use data analytics to run their business. You can usually determine this from the information provided on their website and in the job posting.

William's Answer #1
"As a Data Scientist, I've come across many examples of how businesses use data analysis to improve the results of their operations. For example, eCommerce firms can use data analysis to understand customer behavior, reduce churn and better target their marketing. Financial organizations use it to evaluate investment opportunities and detect fraud. Healthcare companies employ data analysis to develop treatments for specific groups of patients."
William's Answer #2
"Data analytics is one of the most significant developments in making businesses and other organizations more efficient and effective. The insight data science provides helps virtually every organization to improve its operations through a better focus on outcomes and more targeted information used for intelligent decision making. Examples of this include search engines ranking pages depending on the specific interests of the user, and social media filtering information which the user is not interested in. Another use of data analytics is in robotics, which uses machine learning to handle new situations not previously encountered. Finally, businesses can extract information from large and unstructured sets of data which can then be used to develop products and target their marketing."
3.
What is Data Cleansing and why is it important in Data Analysis?
Technical questions like this one are straight forward ways for the interviewer to explore and confirm your technical competencies related to the position for which they are interviewing you. Your preparation for an interview should include researching and practicing technical questions, in addition to general and behavioral questions. Always answer technical questions succinctly, without embellishment or additional information.

William's Answer #1
"Data cleansing is the process of ensuring that data obtained from a wide variety of sources is suitable for analysis. It involves a high-level review of the data set, detection of any anomalies or inaccuracies, and correcting these to ensure the data is correct and accurate. It can also be used to eliminate components of the data that are irrelevant to the analysis being performed."
William's Answer #2
"It is always a good idea to cleans data before analyzing it. This involves reviewing the data for inaccuracies, irrelevant information or other items that will skew the analysis and result in conclusions that are incorrect or not usable. When performing a data cleansing operation, the Data Scientist looks for outliers or information that doesn't fit the pattern of the majority of the data. Inaccuracies are corrected and information not relevant to the analysis being performed is removed."
4.
A a Data Scientist, how do you employ statistics to analyze data and develop business recommendations?
Data Scientists use a variety of tools, statistics being one of the most used and commonly employed. An interviewer will ask this question early in the interview to set the stage, learn more about your skills and experience, and to guide you toward other, more specific questions. Keep this in mind when responding to this question, because it will provide you with the opportunity to move the interview in a direction that you are comfortable with and can easily address.

William's Answer #1
"Statistics is probably one of the strongest tools a Data Scientist has in their arsenal. It helps us to identify patterns, find hidden insights and quickly analyze large data sets. Statistics provide information about consumer behavior, interests, engagement and other aspects of the shopping and purchase process. They also allow for the quick development of models that validate assumptions and inferences."
William's Answer #2
"Of all the tools I use in the process of analyzing data, statistics is my favorite. This is the most mature methodology in the field of data science and there are a great many programs at our disposal. Statistical analysis is a straight forward way to identify trends, confirm a hypothesis, expose hidden insights and develop models business users need to make intelligent decisions. Statistics can be used to narrow the focus of an analysis and provide the users with the exact information they are looking for."
5.
Can you compare Sas, R and Python programming tools and describe their use in Data Analytics?
This is a Technical Question, which seeks to determine your technical capabilities and your knowledge of common tools used by Data Scientists. By specifying these tools, the interviewer is indicating that these are what their organization uses and expects you to be competent in. You should be able to compare these and state their purpose in analyzing data, even if you don't regularly use them.

William's Answer #1
"Sas, R, and Python are probably the most commonly used tools for data analytics. Sas has a wide array of functions, a user-friendly graphical interface, and strong reporting features. R's strength is that it is an open-sourced tool and is widely used in academic and research environments. Python is also an open-sourced product but is more widely used and supported. It is easy to learn and interfaces well with other tools. The best part about Python is its large portfolio of libraries and modules."
William's Answer #2
"While there are many data analytics tools available, Sas, R and Python are probably the most popular and widely used. Of these, I prefer Python. This is due to its large number of user-created libraries and modules, its ease of use, and its robustness in areas such as statistical operations and model building. R is also open-sourced but is more popular with the academic and scientific community. Sas is by far the most widely used data analytics tool and has an easy-to-use graphic interface and probably the strongest statistical functions. Sas' only drawback is its licensing cost, which can be prohibitive for smaller organizations."
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