Data Analytics (DA) is being used by many businesses nowadays to make effective policies and plans that lead to business growth. It is basically the process of examining data sets to draw meaningful conclusions about the information contained in them. Mostly, specialized systems and software are used in drawing these conclusions. In the recent times, data analytics has proved to be a highly-effective technique for the businesses to propel their growth.
The experts in the field of data analytics are called Data Analysts. These professionals are quite in demand these days in almost all large and medium scale organizations. We have listed below some of the most important Data Analyst interview questions and answers, which would prove very helpful for the candidates looking for a job in this domain:
Q1. What is involved in a typical data analysis?
Typical data analysis involves the collection and organization of data. After that, finding correlations are found between the analyzed data and the remainder of the company’s and industry’s data. It also involves problem spotting ability and then, initiating relevant preventive measures or solving the problem in a creative manner.
Q2. What is required to become a data analyst?
Q3. Mention what is the responsibility of a Data Analyst?
The role of a data analyst is quite vast in scope and it involves many things. The major responsibilities of a data analyst entail documenting the types and structure of the business data (logical modeling); analyzing and mining business data in order to identify patterns and correlations among the various data points; mapping and tracing data from one system to another with the purpose of solving a given business or system problem; designing and creating data reports and reporting tools to facilitate decision making in the organization; and, performing a statistical analysis of business data.
Q4. What has been your most difficult analysis to date?
My most challenging analysis till date has been the prediction sales that I made during the recession period and estimating financial losses for the upcoming quarter. Interpreting the information is a seamless process. However, it is a daunting job to predict future trends when the market is not stable and fluctuating frequently. Generally, I analyze and report on data that has already occurred. But during the recession time, I had to research how the fragile economic conditions impacted varying income groups and then, draw an inference on the purchasing power of each group.
Q5. How will you explain Data Cleansing?
Data Cleansing, also called data scrubbing, is the process of altering or removing data from a database that is incomplete, incorrect, improperly formatted, or duplicate data. The purpose of all these activities is to ensure that there is no junk or redundant data in the database and that whatever data is present over there is meaningful and accurate. There are many ways to perform data cleansing in various software and data storage architectures. It can be performed interactively with the aid of data wrangling tools, or as batch processing through scripting.
Q6. Tell us some of the best practices for data cleaning.
Some of the best practices for data cleaning include,
- Sort the data at first; negative numbers, strings that start with obscure symbols, and other outliers will often appear at the top of your dataset after a thorough sorting.
- Focus your attention on the summary statistics for each column; they will help zero in quickly on the most frequent problems.
- When you are dealing with a large and messy dataset, attaining 100% cleanliness is next to impossible. Therefore, the practical approach in such a case is recognizing the most common issues and resolving them first to enhance the overall quality of the dataset.
- Another good approach to deal with a large dataset is to pickup up a random sample and work on it; it will improve your iteration speed as well. When you gain confidence on a particular technique, you may repeat it with the entire dataset.
- Sampling technique can also be employed to test data quality. For example: if you are dealing with a dataset having 250 random rows, there should not be any formatting issues with more than 5 rows.
- Tools such as OpenRefine, which was earlier called Google Refine, can spare you the headache of dealing with big data by performing a lot of simple cleanings (e.g. string canonicalizations) with minimal user input.
- Get mastery of using regular expression.
- You may create a set of utility functions, scripts, and tools, to handle common cleansing tasks.
- It is a good practice to keep track of every cleansing operation conducted by you so that you can modify, repeat, or remove operations as and when required. It is advisable to use a tool that automatically keeps track of your actions.
Q7. List the best tools that can be useful for data-analysis?
The best tools for data analysis are: OpenRefine, RapidMiner, Tableau, KNIME, Google Search Operators, NodeXL, Solver, Google Fusion tables, and Wolfram Alpha’s.
Q8. What is the difference between data mining and data profiling?
The difference between data mining and data profiling is:
Data mining is the computing process of sorting through large data sets, in order to identify patterns and establish relationships for the purpose of data analysis and thereupon problem solving. Data mining tools prove very helpful for business organizations in predicting the future trends.
Data profiling is basically a data examining process aimed at achieving various purposes like determining the accuracy and completeness of data. This process closely examines a database, or other similar data sources, in order to reveal the erroneous areas in data organization; data quality is improved by the deployment of data profiling technique.
Q9. What are the most common problems faced by data analysts?
Some of the most common problems faced by data analyst are:
- Duplicate entries
- Missing values
- Common misspelling
- Varying value representations
- Illegal values
- Identifying overlapping data
Q10. Explain what should be done with suspected or missing data?
First of all, you should prepare a validation report that provides information about all the suspected data that you have. The report should contain information like the information about the validation criteria that a particular data item or items failed to, with the date and time of the occurrence. Experienced personnel should examine the suspicious data to determine its acceptability. Invalid data should be assigned a validation code. To deal with the missing data, you should use the best analysis strategies like single imputation method, deletion method, model-based methods, etc.
Q11. Explain what is an Outlier?
Outlier is a frequently used term by data analysts. It refers to a value that appears far away and diverges from an overall pattern in a sample. There are basically two types of outliers, viz., univariate and multivariate. A univariate outlier is a data point that consists of an extreme value on one variable, while a multivariate outlier is a combination of unusual scores on minimum two variables.
Q12. What do you mean by logistic regression?
Logistic regression, also called logit regression, is a statistical method for examining a dataset in which there are one or more independent variables, which define an outcome.
Q13. How do you define Big Data?
Big Data is a frequently heard term these days. It basically refers to a very large volume of data, both structured as well as unstructured, that inundates a business on a day-to-day basis. Although big data does not equate to a particular volume of data, it generally signifies a data volume as large as a terabyte, petabyte, or even exabyte, captured over a period of time. It’s not the sheer volume of this data that matter, what really matters is how an organization handles it and what it makes out of it. Big data prove very helpful in taking important business decisions and making effective business plans for the future, provided this data is analyzed properly to draw meaningful insights out of it.
Q14. What is Apache Hive?
Apache Hive is an open-source data warehouse system for querying and analyzing large datasets stored in Hadoop files. There are three main functions performed by Hive: data summarization, query and analysis. Hive supports an SQL like query language called HiveQL, which transparently converts queries into MapReduce and Spark jobs, which can run in Hadoop. Besides this, HiveQL supports custom MapReduce scripts to be plugged into queries. Moreover, Hive enables data serialization/deserialization and enhances flexibility in schema design by including a system catalog referred to as Hive-Metastore.
Q15. What should be the criteria for a good data model?
The criteria for a good data model include:
- It should be easily consumable
- Major data changes in it should be easily scalable
- It should provide predictable performance
- It should be capable of adapting to changes in requirements. Read More