Data Analyst Interview Questions :
In my previous articles I have given the different kind of SQL interview questions with answers. In organization the user needs to play different roles like database admin,data analyst,data developer.In this article i will try to give different SQL Interview Questions with answers for Data Analyst. The role of Data Analyst is to analyse the data. In this article i will first explain some roles and responsibilities of data analyst and then i will give you 20 most important SQL Data Analyst Interview Questions.
Roles and Responsibilities of Data Analyst :
There are following roles and responsibilities for Data Analyst.The data analyst is responsible for the data
- 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 or 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 database by developing access system by determining user level of access
Data Analyst Interview Questions :
Question 1 :
What will be daily tasks of Data Analyst? (100% asked Data Analyst Interview Questions )
Answer :
1.The data analyst is responsible for the data analysis in the organization which are in to different databases.
2.Data Analysis Support : The data analyst is responsible for supporting the data for various systems.
3.Customer Interaction : The data analyst is responsible to interact with the customer and gives the different reports as per the customer requirements.
4.Data Issues: The data analyst is responsible to solve the data related issues
5.Maintaining the data : The data analyst will make sure that the data is in maintained form so that user will get the information of data quickly
6.The data analyst is responsible to analyze, identify and interpret trends or patterns in complex data sets.
7. The data analyst is responsible to prioritize business needs and work closely with management and information needs
8.Data mining : The data analyst is responsible to mine the data with using multiple complex sql queries.
9.Bridge between DBA and Customer: The data analyst is bridge between DBA and customer.If customer faces the issue related to data their first point of contact is data analyst.
10.Data Cleaning tasks: The data analyst is responsible for the quality of the data so that He/She needs to clean the data.
11.Data Consolidation: The data analyst is responsible to consolidate the data from multiple sources.
Question 2 :
State Different phases of data analytics ?(100% asked Data Analyst Interview Questions )
Answer :
There are following different stages used in data analytics :
- Problem definition: The exact problem and we need to take care of what exactly we want to achieve through this project
- Data exploration : Exploring the data from various sources is the second stage of any analytics project
- Data preparation : Preparing the test data for testing various scenarios in the project
- Modelling : This is heart of any data analytics project. The data modeling is nothing but the design of a database where user needs to convert snowflakes schema to star schema
- Validation of data : The data validation is most important step of any analytics project.User should do the data validation after proper implementation of project.
- Implementation and tracking : After successful implementation data analyst needs to track that the implemented project is working properly.
Question 3 :
What is mean by data mining?
Answer :
There are following different definitions of data mining :
Definition 1 :
Data Mining is the process used for the extraction of hidden predictive data from huge databases.
Definition 2 :
Data Mining is process of discovering the patterns in very large data sets involving the different methods like Machine Learning,statistics,different database systems.
Definition 3:
Data mining is defined as a process used to extract usable data from a larger set of any raw data which implies analyzing data patterns in large batches of data using one or more software.
Definition 4 :
The automated extraction of hidden data from a large amount of database is Data Mining.
Question 4 : What is data Cleansing?
Answer:
The data cleansing is related to data quality. The data analyst needs to make sure about the quality of the data. Data cleaning also referred as data cleansing, deals with identifying and removing errors and inconsistencies from data in order to enhance the quality of data.
Question 5 : What is basic difference between data mining and data warehousing?
Answer :
Data Warehousing :
Data warehousing is merely extracting data from different sources, cleaning the data and storing it in the warehouse.
Data Mining :
Where as data mining aims to examine or explore the data using queries. These queries can be fired on the data warehouse. Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc.
Example :
A data warehouse of a company stores all the relevant information of projects and employees. Using Data mining, one can use this data to generate different reports like profits generated etc.
Question 6 : State different steps in data cleansing activities.(80% asked Data Analyst Interview Questions )
Asnwer:
Step 1 : Sorting
The first step of data cleansing is sorting with different attributes as per Business requirement.
Step 2 : Large data sets :
For large data set data cleaning needs to be done step-wise. We need fragment the data and do the cleansing and again de-fragment that data.
Step 3 :Make use of Functions Scripts :
For data cleansing activities we need to use different functions and scripts for fast data processing.
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
Step 4 :Analyse the statastics :
Analyze the summary statistics for each column ( standard deviation, mean, number of missing values,)
Step 5 : Tracking of information
User needs to keep the track of the data cleansing activities
Step 6 :Schedule activities:
If there are repeated activities in data cleansing user can schedule the activities accordingly.
Question 7 : Explain different tools used for data analysis.(100% askedData Analyst Interview Questions )
Answer:
The following tools are very useful in data analysis :
- Tableu
- OBIEE
- RapidMiner
- OpenRefine
- KNIME
- Google Search Operators
- Solver
- NodeXL
- io
- Wolfram Alpha’s
- Google Fusion tables
Question 8 :What are various features of Data Mining?
Answer:
Following are different features of data mining :
• Automatic pattern predictions based on trend and behaviour analysis.
• Prediction based on likely outcomes.
• Creation of decision-oriented information.
• Focus on large data sets and databases for analysis.
• Clustering based on finding and visually documented groups of facts not previously known.
Question 9 :Explain data purging?(100% asked Data Analyst Interview Questions )
Answer:
The process of cleaning junk data is termed as data purging. Purging data would mean getting rid of unnecessary NULL values of columns. This usually happens when the size of the database gets too large.
Data Purging is most important activity for database management systems. The junk data will grab the database memory and it will slows down the performance of the database. So frequent purging gives the fast performance of data.
Question 10 :What is OLAP ? Explain with example.
Answer:
OLAP is technology used in many Business Intelligence applications which includes complex analytical calculations.OLAP is used for complex calculations,Trends Analysis,sophisticated data modeling.OLAP database is stored in multidimensional database model.OLAP system contains less number of transactions but complex calculations like aggregation- Sum,count,average,min,max e.t.c.
The Aggregated data in OLAP system must be in months,quarters,years,weeks e.t.c. The key purpose to use OLAP system is to reduce the query response time and increase the effectiveness of reporting.If these aggregated calculations are already stored in repository and if user wants fast access of data then user can use OLAP system.OLAP database stores aggregated historical data in multidimensional schema.
Real Example :
If Company head wants information of Resources salary in year 2000.
In spite of using the transactional system we will use OLAP system here where aggregated data of year 2000 for Resources is already present.
Question 11: What will be common issues faced by data analyst?
Answer:
The most common issues faced by data analyst is ;
- Common misspelling
- Duplicate entries
- Missing values
- Illegal values
- Varying value representations
- Identifying overlapping data
Question 12 : What are different data validation methods used by data analyst?
Answer:
Usually, methods used by data analyst for data validation are
- Data screening
- Data verification
Question 13 :Explain different usages of data mining.
Answer :
Following are some usages of data mining :
1.Fast Business Decisions :
Data mining helps analysts in making faster business decisions which increases revenue with lower costs.
2.Find Patterns:
Data mining helps to understand, explore and identify patterns of data.
3.Process automation:
Data mining automates process of finding predictive information in large databases.
4.Hidden Pattern Finding :
Helps to identify previously hidden patterns.
Question 14 :
Tell different industries where data analysis is frequently used?
Answer:
Following are different industries where data analysis is frequently used :
1. Marketing
2. Advertising
3. Services
4. Artificial Intelligence
5. Government intelligence
Question 15 : Explain different skills where the data analysis needs to be used.
Answer:
The data analyst should know the following things :
- Database knowledge
- Database management
- Data blending
- Querying
- Data manipulation
- Predictive Analytics
- Basic descriptive statistics
- Predictive modeling
- Advanced analytics
- Big Data Knowledge
- Big data analytics
- Unstructured data analysis
- Machine learning
- Presentation skill
- Data visualization
- Insight presentation
- Report design
Question 16 : What are different tools used for Big data.
Answer:
- Hadoop
- Hive
- Pig
- Flume
- Mahout
- Sqoop
Question 17 :What are different stages of data mining ?(100% asked Data Analyst Interview Questions )
Answer :
There are following different stages of data mining :
a. Business understanding
b. Data understanding
c. Data preparation
d. Modeling
e. Evaluation
f. Deployment
Question 18 : 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, outliers detection
- Imputation techniques, etc.
- Simplex algorithm
- Mathematical optimization
Question 19 : How data warehouse and data mining work together?
Answer:
Following points gives you idea about the data warehouse and data mining relationship :
Data mining
1.Extracting useful information for large amounts of data, for the purpose of finding various methods for business intelligence. This is the process of data mining
2.Prediction of future is done by using data mining. Data warehousing is the source for data mining.
Data warehousing:
1.Extracting data from various resources, transforming into required form is done in data warehousing. Later this data is loaded into data warehouse.
2.Historical data is stored using data warehousing. Business analysis is done by business users.
Questiion 20 : What are different stages in data mining?
Answer:
Following are different types of data mining :
a. Data cleaning
b. Integration
c. Selection
d. Data transformation
e. Data mining
f. Pattern evaluation
g. Knowledge representation
The data analyst is responsible for analysis of data and data cleaning. Hope you like this article on Data Analyst Interview Questions.If you like this article or if you have any issues and concerns with the same kindly comment it in comment section.