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econolytics-blog · 7 years ago

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Top Data Analyst/Scientist Interview Questions and Answers

In this blog, we will cover the major data analyst/scientist interview questions and answers. The demand for data analysts is growing in today’s advancement in technology. When looking to hire a data analyst, here are the top data analyst/science interview and answers to ask during an interview.

1. What are the top responsibilities of a Data Analyst?

Each profession has its own unique way of handling responsibilities for the smooth running of tasks/processes of businesses or organizations.

Responsibilities of a data analyst may include;

Understanding the data structure and sources relevant to the business;

Being able to extract the data from these sources in a timely & efficient manner;

Identify, evaluate and implement services and tools from external sources to support the validation of data and cleansing;.

Develop and support various reporting processes of the business;

Perform an audit of data and resolve any business associated issues for clients;

Ensure database security by developing access system user levels;

Analyze, identify & interpret process trends or patterns primarily in complex data sets and trigger alerts for the business teams;

Evaluating historical data and making forecasts for growing the business;

Developing and validating predictive models to improve business processes and identify key growth strategies.

2. What are the key skills required for a data scientist?

Mathematics/Statistics Knowledge; A Data scientist should be able to work on statistical concepts seamlessly. Without a good hold on Statistics, a data scientist will not be able to understand basics such as cleaning and manipulating data.

Programming skills: Should be familiar with computer software and tools including; scripting language (Matlab, Python), Spreadsheet (Excel) and Statistical Language (SAS, R, SPSS), Querying Language (SQL, Hive, Pig). Other computer skills include; big data tools (Spark, Hive HQL), programming (JavaScript, XML), and so on.

Logical Deduction: This is a skill that comes with experience. The data scientist should be able to immediately identify anomalies and be able to draw out strategies from trends. Without this skill, a data scientist is not able to add value to the business.

Besides these skills, domain knowledge is increasingly becoming a requirement for a data scientist. Example: Credit Risk, supply chain management, etc.

Attention to details, decision making and problem-solving, communication skills, are some of the soft skills that a data scientist must develop.

3. Summarize the various steps in an analytics project

Defining the objective function;

Identifying key sources of data required for the analysis;

Data preparation & cleaning;

Data modelling

Model Validation

Implementation and tracking (deployment and monitoring the results)

4. Define Data Cleansing (data cleaning)

Refers to the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. Data cleansing also refers to identifying incomplete, incorrect, inconsistent or irrelevant parts of the record set, table, or database data and then replacing, modifying, or deleting the dirty data. In model development, data cleaning also means identifying anomalies in the data that cannot be represented consistently by one model. Example: For income estimation models, very high values of income that are not consistent with the data should be either removed or capped to a maximum limit. The aim is to enhance the quality of data.

5. What are the best practices for data cleaning?

Best practices for data cleaning includes;

Understanding the range (Min./Max.), mean, median and plotting a normal curve;

Identifying outliers in the data and treating them;

Missing value treatment;

6. Explain what is logistic regression?

Logistic regression is a statistical method for examining a dataset consisting of one or more independent variables that define an outcome.

7. Give some of the best tools useful for data analysis

Solver

NodeXL

KNIME

R Programming

SAS

Weka

Apache Spark

Orange

Talend

RapidMiner

OpenRefine

Tableau

Google Search Operators

Google Fusion Tables

Wolfram Alpha’s

Pentaho

8. What is the difference between data mining and data profiling?

Data profiling is the process of analyzing the data available from an existing information source like a database and collecting statistics or informative summaries about that data. It may be information on various attributes like discrete value, value range etc.

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It can be focusing on cluster analysis, dependencies, sequence discovery, detection of unused records and others.

9. What are some common problems faced by data analyst?

Problems include;

Data storage and quality

Identifying overlapping data

Common misspelling

Duplicate data entries

Varying value representations

Missing values

Illegal values

Security and privacy of data

10. What are Hadoop and MapReduce?

It’s the name of the programming framework developed by Apache for processing large data set, for an application in a distributed computing environment.

You can read more about Hadoop and Map Reduce here

Read Full Article Here: https://www.econolytics.in/blog/data-analyst-scientist-interview-questions-answers/

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