If you're looking for Data Analyst Interview Questions for Experienced or Freshers, you are at right place. There are lot of opportunities from many reputed companies in the world. According to research Data Science Market expected to reach $128.21 Billion with 36.5% CAGR forecast to 2022. So, You still have opportunity to move ahead in your career in Data Analytics. Mindmajix offers Advanced Data Analyst Interview Questions 2018 that helps you in cracking your interview & acquire dream career as Data Analyst.
Q: What are the primary responsibilities of a data analyst?
The primary responsibilities of a data analyst are as follows:
1. A data analyst is responsible for all data related information and the analysis is needed for the staff and the customers.
2. Very useful at the time of audit
3. Very capable of using statistical techniques and provide suggestions based on the data
4. Focused on improving the business process and always strive for process optimization
5. Work with the raw data and provide meaningful reports for the managers
6. They are responsible for acquiring data from different primary and secondary sources so that they can harvest one common database.
Q: What are the prerequisites for an individual to become a data analyst?
The following are the prerequisites for an individual to become a data analyst:
1. Should have a good understanding of business objects and reporting packages.
3. Should be well versed with data mining, segmentation techniques
4. Should be experienced in analyzing a large amount of data, EXCEL.
Q: What are the different steps available in an analytical project, list them out?
The various steps involved in analytics project are :
1. Definition of the problem
2. Exploring the data
3. Preparing the data
4. Data modelling
5. Validation of the data
6. Tracking and implementation
Q: Explain what does data cleansing mean?
So data cleansing is also called as data cleaning. During this process, the inconsistency that is identified are sorted out and all the possible errors are also taken care during this process. All of these steps focus towards improving the data quality
Q: Explain what is logistic regression?
The logistic regression is nothing but one of the regression models that is used for data analysis purposes. This type of regression method is called a statistical method where one of the data elements is an independent variable which ultimately helps you with the outcome.
Q: List out the popular tools which are used for data analysis?
They are various tools that are available in data analysis, they are as follows:
2. Google search operators
3. Google Fusion Tables
Q: Explain what is data mining?
The data mining is a process where it focuses on cluster analysis. It is considered as a process of analyzing large data sets and out of which they will be able to identify unique patterns and also help the user to understand and establish a relationship to solve any obstacles through analyzing data.
Data mining is also used to predict future trends within organizations
Q: What are the four stages of data mining?
The four stages of data mining are as follows:
1. Data sources
2. Data exploration or Data gathering
4. Deploying models
Q: Explain what is Data Profiling?
The data profiling is nothing but a process of validating or examining the data that is already available in an existing data source, so the data source can be an existing database or it can be a file.
The main use of this is to understand and take an executive decision whether the data that is available is readily used for other purposes.
Q: What is the day to day challenges that actually affect data analyst big time?
The list of common problems that most of the time data analyst actually oversee is nothing but:
1. Common misspelling
2. Duplicate entries
3. Overlapping data
4. Missing values
Q: What is the name of the framework which was completely developed by Apache for large data sets which can be processed for an application? All of this processing is happening in a distributed computing environment?
Hadoop and MapReduce is the programming framework which was completely developed by Apache where large sets of data for an application is been processed under a distributed computing environment.
Related Article: Advantages of Hadoop MapReduce Programming
Q: What are the two data validation methods that are actually used by data analysts?
The two data validation methods that are actually used by the data analysts are:
** Data screening
** Data verification
Q: Explain what is collaborative filtering with an example?
Well, Collaborative filtering is nothing but a process or an algorithm that actually helps the user with a recommendation based responses to the user based on analyzing user behavioral data.The important components of collaborative filtering are as follows:
For example: if we have to explain collaborative filtering then we can consider our browsing history pattern. Based on our browsing interest pattern we will be getting “recommended products for you” ad while you are browsing online shopping sites.
So next time when you see some of your browsed products are shown as ad’s remember that is Collaborative Filtering process
Q: Explain what is Map Reduce?
The Map-Reduce is nothing but a programming model where it is associated with process implementation and also analyzing large chunks of data sets parallelly. Using this programming model large data sets are segregated into small chunks of data sets which are analyzed parallelly to yield the outcome.
Q: Explain what does clustering mean?
The clustering is defined as a process of grouping a definite set of objects based on certain predefined parameters. This is one of the value-added data analysis technique that is used industry-wide while processing a large set of data.
Q: What are the applications that are based on clustering algorithm?
The applications that are based on clustering algorithm is listed below:
3. Mathematical analysis
4. Statistical analysis
Q: What are the properties of clustering algorithm?
The properties of the clustering algorithm are as follows:
3. Hard and Soft
Q: Explain what is imputation process? What are the different types of imputation techniques that are available?
The Imputation process is nothing but a process of replacing missing data elements with a substituted values. They are two types of imputation techniques that are available for use:
>> Single Imputation
>> Multiple Imputation
Q: What is the standard for having a good data model?
The criteria or standard for having a good data model is as follows:
1. Should be in a form where it can be consumed easily
2. Even if it is larger datasets, the model should be scalable
3. Should have a predictable performance
4. A good model is always available for adapting changes
Q: List out the tools that are used in Big data analysis?
The list of tools that are used in Big data is as follows:
Q: What is the good to have skills for an individual to be a value-added data analyst to the organization?
The following are good to have skills for an individual which will be a value ad for the data analyst, they are following:
1. Predictive Analysis: This is a major game changer within process improvisation
2. Presentation Skills: This is vital for an individual to make sure that they are able to showcase a face to their data analysis. This can be done by using some of the reporting tools
3. Database knowledge: This is essential because it is widely used in day to day operational tasks for the data analyst.
Q: What is the best way to deal or to tackle multi-source problems?
The best way to deal with multi-source problems is:
1. Restructure the schemas that are available
2. Identify the similar records and make sure that they are combined into a single record
Q: What is data screening in data validation process?
The data screening is a process where the entire set of data is actually processed by using various algorithms to see whether we have any questionable data. This type of values is handled externally and thoroughly examined.
Q: List out few best practices that are followed when it comes to data cleansing?
The best practices that are followed when it comes to data cleansing is as follows:
1. Need to define and follow a standard verification process of evaluating the data even before getting it into the database.
2. Identification and handling duplicate values so that data accuracy is always maintained
3. There is a need to develop a quality data plan which actually focuses on identifying any possible errors and also learn from the mistakes and constantly improving the plan.
Q: Explain few important aspects of Data analysis?
The data analysis is nothing but an in-depth study the entire data set that is available in the database.
1. First and foremost step for data analysis starts with questions and assumptions.
2. It also involves in identifying troublesome records which need to be cleaned.
3. Convey the same information to the stakeholders so that they can understand the outcome of the data analysis.
4. Study based on different regression models will help them to state an expected output.
Q: Explain in detail what is meant by the K-mean algorithm?
The K-mean algorithm is one of the famous partitioning methods. Within this, the objects belong to a specific k group.
Within the k-mean algorithm:
1. The clusters are actually a sphere. So all the data points within the cluster are actually centered in the cluster.
2. The spread or the variance of the cluster is pretty much similar.
Q: Explain the concept of Hierarchical clustering algorithm?
The Hierarchical clustering algorithm is nothing but a process where it actually combines and divides the existing groups. So based on this a hierarchical structure the groups are assigned to a specific order and structure.
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