Data Analyst Interview Questions

Data Analysis is an art of collecting and analyzing data so that the company can use the same to perfect their marketing, insurance, political and other business practices. These data analysts are highly trained professional and they perform the analysis by using various mathematical calculations and further determine how the data samples might best be applied to increase the profit of the business. One of the critical roles is an evaluation of risk.

As most companies are always looking to expand their businesses, or at least improve their business practices, data analysis is an essential and profitable field. Data Analyst seeks to understand the origin of data and any possible distortions through the use of technology. If one can identify trends and patterns of information and also has an excellent computer skill, then they can find their niche as a Data Analyst.

In this role, a person is asked to use their technical expertise to extrapolate data by using advanced computerized models. The job is professional and heavily influenced by mathematics and advanced algorithms. One can be at a Data cleaner, rooting out errors in data or can be employed on Initial Analysis, whereby the assessment of the quality of data is done. As the Main Data Analyst, one is asked to look at the meaning of data, and if they work on Final Analysis.

Latest Data Analyst interview questions

Here are a few data analyst interview questions that might be asked by the panel:

#1 Define Outlier?

It is a commonly used term by analysts, referred for a value that appears far away and diverges from an overall pattern in a sample. Outliers can be classified into two types;
  • Univariate
  • Multivariate

#2  Define KNN imputation method?

In KNN imputation, the missing attribute values are imputed by using the value of the attribute that is most similar to the quality whose values are missing. By using a distance function, the similarity of two attributes is determined.

#3 How can you define interquartile range as a data analyst?

The measure of the dispersion of data that is shown in a box plot referred to as the interquartile range. It is the difference between the upper and the lower quartile.

#4 How to deal with multi-source problems?

To deal with the multi-source problems one should:
  • Get involves in a restructuring of schemas, to accomplish schema integration.
  • And, Identify similar records and merge them into a single document containing all relevant attributes without redundancy.

#5 Define the essential steps required for data validation process?

Data Validation is performed in 2 different steps:

Data Screening: In this step various algorithms are used to screen the entire data to find any erroneous or questionable values.

Data Verification: In this step each suspect value is evaluated on a case by case basis, and a decision is made if the values have to be accepted as valid, rejected as invalid or if they have to be replaced with some redundant values.

#6 List the steps in an analytics project?

Steps included in an analytics project are;
  • Problem definition
  • Data exploration
  • Data preparation
  • Modeling
  • Validation of data
  • Implementation and tracking.

#7 List down some of the best tools that can be useful for data-analysis?

Some of the best tools that can be useful for data-analysis are:
  • Tableau
  • Rapid Miner
  • Open Refine
  • Google Search Operators
  • Solver
  • Node XL
  • IO
  • Wolfram Alpha’s
  • Google Fusion tables

#8 What missing patterns a data analyst observes?

The missing patterns that are generally observed are
  • Missing completely at random
  • Missing at random
  • Missing that depends on the missing value itself
  • Missing that depends on an unobserved input variable

#9 How can we differentiate between Data Mining and Data Analysis?

Here are a few considerable differences:
  • Data Mining: Data mining does not require any hypothesis and depends on clean and well-documented data. Results of data mining are not always easy to interpret. Its algorithms automatically develop equations.
  • Data Analysis: Whereas, Data analysis begins with a question or an assumption. Data analysis involves data cleaning. The work of the analysts is to interpret the results and convey the same to the stakeholders. Data analysts have to develop their equations based on the hypothesis.

#10 What will you do if a data is suspected or missing?

In case of suspected or missing data following steps should be taken;
  • Preparation of a validation report that gives information on all suspected data. Information like validation criteria that it failed and the date and time of occurrence should be taken care of.
  • Experience personnel should examine the suspicious data to determine their acceptability.
  • Invalid data should be assigned and replaced with a validation code.
  • To work on missing data best use of analysis strategy like deletion method, single imputation methods, model-based methods, etc. should be followed up.

#11 What steps can be used to work on a QA if a predictive model is developed for forecasting?

Here is a way to handle the QA process efficiently:
  • Firstly, partition the data into three different sets Training, Testing and Validation.
  • Secondly, show the results of the validation set to the business owner by eliminating biases from the first two sets. The input from the business owner or the client will give an idea of whether the model predicts customer churn with accuracy and provides desired results or not.
  • Data analysts require inputs from the business owners and a collaborative environment to operationalize analytics. To create and deploy predictive models in production there should be an effective, efficient and repeatable process. Without taking feedback from the business owner, the model will be a one-and-done model.

#12 What criteria can define a good data model?

To say a model is good, following points needs to be considered.
  • The developed model should have predictable performance.
  • It should be adaptable easily to any changes as per business requirements.
  • It should be scalable to any data change.
  • A model should be efficiently consumed for actionable results.

#13 How will you define logistic regression?

Logistic regression is a statistical method that analyze a dataset, in which there are one or more independent variables and it determine the outcome. It is measured with a dichotomous variable. The objective of logistic regression is to determine the suitable fitting model to describe the relationship between the dichotomous characteristic of interest and a set of independent variables. Logistic regression generates the coefficients of a formula to predict a logistic transformation of the probability of a presence of the characteristic of interest.

#14 Define collaborative filtering?

Collaborative filtering is a simple algorithm to create a recommendation system based on user behavioral data. The most critical components of collaborative filtering are users- items- interest. One of the examples of collaborative filtering is when you see a statement like “recommended for you” on online shopping sites that pop out based on your browsing history.

#15 List the data validations methods used by data analysts?

Usually, methods used by a data analyst for data validation are:
  • Data screening
  • Data verification
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