Data Scientist Mock Interview
Practice 30 Data Scientist interview questions covering machine learning, statistical modeling, and technical problem-solving.
Question 19 of 30
What is Data Cleansing and why is it important in Data Analysis?
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William Swansen has worked in the employment assistance realm since 2007. He is an author, job search strategist, and career advisor who helps individuals worldwide and in various professions to find their ideal careers.
"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."
William Swansen has worked in the employment assistance realm since 2007. He is an author, job search strategist, and career advisor who helps individuals worldwide and in various professions to find their ideal careers.
Technical questions like this are straightforward 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 Swansen has worked in the employment assistance realm since 2007. He is an author, job search strategist, and career advisor who helps individuals worldwide and in various professions to find their ideal careers.
"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."
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As the name suggests, data cleansing of the process of cleaning the data so that it is in the form on which the data can be analyzed.
Data Cleaning involves a high-level review of the data set and looks for the missing and inconsistent data, outliers, detect noise and inaccuracies, and correcting them to make sure the data is accurate. For me, data cleansing is the most crucial step in the data analysis and I spend lots of my data analysis time at this step because it is the foundation of the whole analysis. As I read somewhere and also believe that "garbage data gives garbage result, Good data gives good results.'
Marcie's Feedback
This is an excellent response because it is on point and extremely thorough. The interviewer will appreciate that you recognize the importance of data cleansing and that without it the results of the analysis won't be accurate. Great job!
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