Data Analysis is an art of collecting and analyzing data so that the company can use the same to perfect their marketing, insurance, political and other business practices. These data analysts are highly trained professional and they perform the analysis by using various mathematical calculations and further determine how the data samples might best be applied to increase the profit of the business. One of the critical roles is an evaluation of risk.
As most companies are always looking to expand their businesses, or at least improve their business practices, data analysis is an essential and profitable field. Data Analyst seeks to understand the origin of data and any possible distortions through the use of technology. If one can identify trends and patterns of information and also has an excellent computer skill, then they can find their niche as a Data Analyst.
In this role, a person is asked to use their technical expertise to extrapolate data by using advanced computerized models. The job is professional and heavily influenced by mathematics and advanced algorithms. One can be at a Data cleaner, rooting out errors in data or can be employed on Initial Analysis, whereby the assessment of the quality of data is done. As the Main Data Analyst, one is asked to look at the meaning of data, and if they work on Final Analysis.
Latest Data Analyst interview questions
Here are a few data analyst interview questions that might be asked by the panel:
11. Define Outlier?
It is a commonly used term by analysts, referred for a value that appears far away and diverges from an overall pattern in a sample. Outliers can be classified into two types;
12. Define collaborative filtering?
Collaborative filtering is a simple algorithm to create a recommendation system based on user behavioral data. The most critical components of collaborative filtering are users- items- interest. One of the examples of collaborative filtering is when you see a statement like “recommended for you” on online shopping sites that pop out based on your browsing history.
13. What will you do if a data is suspected or missing?
In case of suspected or missing data following steps should be taken;
- Preparation of a validation report that gives information on all suspected data. Information like validation criteria that it failed and the date and time of occurrence should be taken care of.
- 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 best use of analysis strategy like deletion method, single imputation methods, model-based methods, etc. should be followed up.
17. Define the essential steps required for data validation process?
Data Validation is performed in 2 different steps:
Data Screening: In this step various algorithms are used to screen the entire data to find any erroneous or questionable values.
Data Verification: In this step each suspect value is evaluated on a case by case basis, and a decision is made if the values have to be accepted as valid, rejected as invalid or if they have to be replaced with some redundant values.