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Tuesday, June 25, 2019

20 Top Data Analyst Interview Questions and Answers {Updated}

Data Analyst Interview Questions and Answers for experienced PDF, Read commonly asked Data Analyst Job Interview Questions with Answers PDF for Freshers.

Read Data Analyst Interview Questions and Answers

What is the responsibility of a Data Analyst?
Responsibility of a Data analyst include,
  • Provide support to all data analysis and coordinate with customers and staffs
  • Resolve business associated issues for clients and performing audit on data
  • Analyze results and interpret data using statistical techniques and provide ongoing reports
  • Prioritize business needs and work closely with management and information needs
  • Identify new process areas for improvement opportunities
  • Analyze, identify and interpret trends or patterns in complex data sets
  • Acquire data from primary or secondary data sources and maintain databases/data systems
  • Filter and “clean” data, and review computer reports
  • Determine performance indicators to locate and correct code problems
  • Securing the database by developing access system by determining user level of access.

What is required to become a data analyst?
To become a data analyst,
  • Robust knowledge on reporting packages (Business Objects), programming language (XML, Javascript, or ETL frameworks), databases (SQL, SQLite, etc.)
  • Strong skills with the ability to analyze, organize, collect and disseminate big data with accuracy
  • Technical knowledge in database design, data models, data mining and segmentation techniques
  • Strong knowledge of statistical packages for analyzing large datasets (SAS, Excel, SPSS, etc.)

What are the various steps in an analytics project?
Various steps in an analytics project include
  • Problem definition
  • Data exploration
  • Data preparation
  • Modeling
  • Validation of data
  • Implementation and tracking

What is data cleansing?
Data cleaning also referred to as data cleansing, deals with identifying and removing errors and inconsistencies from data in order to enhance the quality of data.

List out some of the best practices for data cleaning?
Some of the best practices for data cleaning includes,
  • Sort data by different attributes
  • For large datasets cleanse it stepwise and improve the data with each step until you achieve a good data quality
  • For large datasets, break them into small data. Working with fewer data will increase your iteration speed
  • To handle common cleansing task create a set of utility functions/tools/scripts. It might include, remapping values based on a CSV file or SQL database or, regex search-and-replace, blanking out all values that don’t match a regex
  • If you have an issue with data cleanliness, arrange them by estimated frequency and attack the most common problems
  • Analyze the summary statistics for each column ( standard deviation, mean, number of missing values,)
  • Keep track of every date cleaning operation, so you can alter changes or remove operations if required

What is logistic regression?
Logistic regression is a statistical method for examining a dataset in which there are one or more independent variables that define an outcome.

List of some best tools that can be useful for data-analysis?
  • Tableau
  • RapidMiner
  • OpenRefine
  • Google Search Operators
  • Solver
  • NodeXL
  • io
  • Wolfram Alpha’s
  • Google Fusion Tables

What is the difference between data mining and data profiling?
The difference between data mining and data profiling is that
Data profiling: It targets the instance analysis of individual attributes. It gives information on various attributes like value range, discrete value, and their frequency, occurrence of null values, data type, length, etc.
Data mining: It focuses on cluster analysis, detection of unusual records, dependencies, sequence discovery, relation holding between several attributes, etc.

List out some common problems faced by data analyst?
Some of the common problems faced by data analyst are
  • Common misspelling
  • Duplicate entries
  • Missing values
  • Illegal values
  • Varying value representations
  • Identifying overlapping data

Mention the name of the programming framework developed by Google for processing large data set for an application in a distributed computing environment?
Hive is the programming framework developed by Google for processing large data set for an application in a distributed computing environment.

What are the missing patterns that are generally observed?
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 the unobserved input variable

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

What are the data validation methods used by data analyst?
Usually, methods used by data analyst for data validation are
  • Data screening
  • Data verification

What should be done with suspected or missing data?
  • Prepare a validation report that gives information on all suspected data. It should give information like validation criteria that it failed and the date and time of occurrence
  • 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 use the best analysis strategy like deletion method, single imputation methods, model-based methods, etc.

How to deal with the multi-source problems?
To deal with the multi-source problems,
  • Restructuring of schemas to accomplish a schema integration
  • Identify similar records and merge them into a single record containing all relevant attributes without redundancy

What is an Outlier?
The outlier is a commonly used term by analysts referred for a value that appears far away and diverges from an overall pattern in a sample. There are two types of Outliers
  • Univariate
  • Multivariate

What is Hierarchical Clustering Algorithm?
Hierarchical clustering algorithm combines and divides existing groups, creating a hierarchical structure that showcases the order in which groups are divided or merged.

What is the K-mean Algorithm?
K means is a famous partitioning method.  Objects are classified as belonging to one of K groups, k has chosen a priori.
In the K-mean algorithm,
  • The clusters are spherical: the data points in a cluster are centered around that cluster
  • The variance/spread of the clusters is similar: Each data point belongs to the closest cluster

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