Introduction To Data Analytics Interview Questions and Answer
So you have finally found your dream job in Data Analytics but are wondering how to crack the 2019 Data Analytics interview and what could be the probable Data Analytics Interview Questions. Every Data Analytics interview is different and the scope of a job is different too. Keeping this in mind we have designed the most common Data Analytics Interview Questions and answers to help you get success in your Data Analytics interview.
Below is the Top 2019 Data Analytics Interview Questions that are mostly asked in an interview
1. What is the difference between Data Mining and Data Analysis?
Answer:
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2. Mention what are the various steps in an analytics project?
Answer:
Data analytics deals with collecting, cleansing, transforming and modeling data to gain valuable insights and support better decision making in an organization. Steps involved in the data analysis process are as follows –
Data Exploration – Having explored the business problem, a data analyst has to analyze the root cause of the problem.
Data Preparation – In this step of the data analysis process we find data anomalies like missing values within the data.
Data Modelling – The modeling step begins after the data has been prepared. Modeling is an iterative process wherein the model is run repeatedly for improvements. Data modeling ensures that the best possible result for a business problem.
Validation – In this step, the model provided by the client and the model developed by the data analyst are validated against each other to find out if the developed model will meet the business requirements.
Implementation of the Model and Tracking – In this final step of the data analysis model implementation is being done and after that tracking is done to ensure that model is implemented correctly or not?
3.What is the responsibility of a Data Analyst?
Answer:
•Resolve business associated issues for clients and perform data audit operations.
•Interpret data using statistical techniques.
•Identify areas for improvement opportunities.
•Analyze, identify and interpret trends or patterns in complex data sets.
•Acquire data from primary or secondary data sources.
•Maintain databases/data systems.
•Locate and correct code problems using performance indicators.
•Securing database by developing access system.
4.What Are Hash Table Collisions? How Is It Avoided?
Answer:
A hash table collision happens when two different keys hash to the same value. There are many techniques to avoid hash table collision, here we list out two:
Separate Chaining: It uses the data structure that hashes to the same slot to store multiple items.
Open addressing: It searches for other slots using a second function and store item in the first empty slot.
5. List of some best tools that can be useful for data-analysis?
Answer:
•Tableau
•RapidMiner
•OpenRefine
•KNIME
•Google Search Operators
•Solver
•NodeXL
•io
•Wolfram Alpha’s
•Google Fusion Tables
6.What is the difference between data mining and data profiling?
Answer:
The difference between data mining and data profiling is as follows –
•Data profiling: It targets the instant analysis of individual attributes like price vary, distinct price and their frequency, an incidence of null values, data type, length, etc.
•Data mining: It focuses on dependencies, sequence discovery, relation holding between several attributes, cluster analysis, detection of unusual records etc.
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7. Explain K-mean Algorithm and Hierarchical Clustering Algorithm?
Answer:
K-Mean Algorithm – K mean is a famous partitioning method. In the K-mean algorithm, the clusters are spherical i.e. the data points in a cluster are centered on that cluster. Also, the variance of the clusters is similar i.e. each data point belongs to the closest cluster
Hierarchical Clustering Algorithm – Hierarchical clustering algorithm combines and divides existing groups and creating a hierarchical structure for them to show the order in which groups are divided.
8.What is data cleansing? Mention few best practices that you need to follow while doing data cleansing?
Answer:
From a given dataset, it is extremely important to sort the information required for data analysis. Data cleaning is a crucial step wherein data is inspected to find any anomalies, remove repetitive and incorrect information, etc. Data cleansing does not involve removing any existing information from the database, it just enhances the data quality so that it can be used for analysis.
Some of the best practices for data cleansing include –
•Developing a data quality plan to identify where maximum data quality errors occur so that you can assess the root cause and plan according to that.
•Follow a customary method of substantiating the necessary information before it’s entered into the information.
•Identify any duplicates data and validate the accuracy of the data as this will save a lot of time during analysis.
•Tracking all the improvement operations performed on the information is incredibly necessary in order that you repeat or take away any operations as necessary.
9.What are some of the statistical methods that are useful for data-analyst?
Answer:
Statistical methods that are useful for data scientist are
•Bayesian method
•Markov process
•Spatial and cluster processes
•Rank statistics, percentile, outlier’s detection
•Imputation techniques, etc.
•Simplex algorithm
•Mathematical optimization
10. Explain what is imputation? List out different types of imputation techniques? Which imputation method is more favorable?
Answer:
During imputation, we have a tendency to replace missing information with substituted values. The kinds of imputation techniques involve are –
•Single Imputation: Single imputation denotes that the missing value is replaced by a value. In this method, the sample size is retrieved.
•Hot-deck imputation: A missing value is imputed from a randomly selected similar record by using punch card
•Cold deck imputation: It works same as hot-deck imputation, but a little more advanced and chooses donors from other datasets
•Mean imputation: It involves replacing missing value with the predicted values of other variables.
•Regression imputation: It involves replacing missing value with the predicted values of a certain value depending on other variables.
•Stochastic regression: It is same as regression imputation, however, it adds the common regression variance to the regression imputation
•Multiple Imputation: Unlike single imputation, multiple imputations estimates the values multiple times
Although single imputation is widely used, it does not reflect the uncertainty created by missing data at random. So, multiple imputations are more favorable than single imputation in case of data missing at random.
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