Database Analyst Interview Preparation Guide

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IN is a conditional operator used in a WHERE clause and is shorthand for multiple OR conditional statements. It tests the expression that precedes it against a list of values that are passed in to the operator, which can either be comma-separated values or a subquery that returns a list of values. If the expression that precedes IN matches any of the elements in the list, the resulting value is TRUE, or 1; otherwise, the value is FALSE, or 0.

UNION will omit duplicate records, whereas UNION ALL will include duplicate records. UNION requires the server to do the additional work of removing any duplicates.

This question gets into how well candidates handle stressful situations. You're looking for a data analyst who can anticipate when a deadline is not going to work and who can find a solution. Past behavior is a good predictor of future behavior.

What to look for in an answer:

☛ Ability to see big picture
☛ Decisiveness and being proactive
☛ Answers that do not blame others

This query is a good way to get to know candidates as people. It can serve as an icebreaker at the beginning of an interview or, if it comes at the end, as a gentle way to bring your question portion to a close.

What to look for in an answer:

☛ Focused replies
☛ Personality
☛ Specifics

Typically done for performance reasons, to reduce the number of table joins. This is not a good idea in a transactional environment as there are inherent data integrity risks or performance risks due to excessive locking to maintain data integrity.

Questions related to the Unified Modeling Language (UML) or Entity-Relationship Diagrams (ERDs) may also be asked here.

In this step, the model provided by the client and the model developed by the data analyst are validated against each other to find out if the developed model will meet the business requirements.

SQL is short for Structured Query Language and is used to communicate with relational databases. It is the standard language used to retrieve, update, insert, and delete data when working with relational databases.

A primary key is a unique identifier for a particular record in a table. The primary key can’t be NULL. A primary key can be a single column or a combination of columns in a table. Each table can contain only one primary key.

I have advanced data analysis software experience. A few examples include creating PivotTables in Excel, producing databases from scratch in Access, and developing data mining algorithms in ELKI. Also in my previous role, I was tasked with upgrading the database to meet the demands of the market and the company to ensure it ran smoothly.

This is the final step of the data analysis process wherein the model is implemented in production and is tested for accuracy and efficiency.

Having a poor formatted data file. For instance, having CSV data with un-escaped newlines and commas in columns.
Having inconsistent and incomplete data can be frustrating.
Common Misspelling and Duplicate entries are a common data quality problem that most of the data analysts face.
Having different value representations and misclassified data.

Amongst the interview questions for data analyst, challenges faced is a sure-shot question put up by the interviewer. Here are a few challenges:

☛ Illegal values
☛ Duplicate entries
☛ Trying to identify data that is overlapping
☛ Regular misspelling
☛ Irregular value misrepresentation
Data analytics interview questions can come in various manners. There are data analytics questions for freshers and data analytics interview questions for experienced. Whichever ones apply to your present situation, make sure you are fully prepared.

A model does not hold any value if it cannot produce actionable results, an experienced data analyst will have a varying strategy based on the type of data being analysed. For example, if a customer complain was retweeted then should that data be included or not. Also, any sensitive data of the customer needs to be protected, so it is also advisable to consult with the stakeholder to ensure that you are following all the compliance regulations of the organization and disclosure laws, if any.

You can answer this question by stating that you would first consult with the stakeholder of the business to understand the objective of classifying this data. Then, you would use an iterative process by pulling new data samples and modifying the model accordingly and evaluating it for accuracy. You can mention that you would follow a basic process of mapping the data, creating an algorithm, mining the data, visualizing it and so on. However, you would accomplish this in multiple segments by considering the feedback from stakeholders to ensure that you develop an enriching model that can produce actionable results.

INSERT

Many interviewers pose questions that let them see an analyst's thought process without the aid of computers and data sets. After all, technology is only as good and reliable as the people behind it.

What to look for in an answer:

☛ Ability to identify variables/data segments
☛ Ability to communicate thought process
☛ Creativity

Aggregate functions perform calculations on a set of values and return a single value. The common aggregate functions are:

☛ COUNT (counts the number of rows in the table)
☛ SUM (returns the sum of all values of a numeric column)
☛ AVG (returns the average of all values of a numeric column)
☛ MIN (returns the lowest value of a numeric column)
☛ MAX (returns the highest value of a numeric column).

Aggregate functions are frequently used in combination with the GROUP BY statement.

There are two types of subqueries: correlated and uncorrelated.

An uncorrelated subquery is a independent query whose output is substituted into the main query. A correlated subquery, on the other hand, uses values from the outer query and therefore depends on the outer query. Such a subquery executes repeatedly, once for each row that is selected by the outer query.

The very first thing I would do is clearly define the problem or objective so I have a solid direction. Second, I would explore the data and become more familiar with it. This is extremely critical especially if I am working with a new set of data. Next, I would prepare the data for modeling. This entails data validation, detecting outliers, treating missing values, etc. With those steps completed, I would begin modeling the data until I discover the most significant or valuable results. Lastly, I would implement the model and track my results. As I'm sure you are aware, this process could vary slightly based upon the type of problem and the data and tools available.

From a given dataset for analysis, it is extremely important to sort the information required for data analysis. Data cleaning is a crucial step in the analysis process wherein data is inspected to find any anomalies, remove repetitive data, eliminate any incorrect information, etc. Data cleansing does not involve deleting any existing information from the database, it just enhances the quality of data so that it can be used for analysis.

Some of the best practices for data cleansing include –

☛ Developing a data quality plan to identify where maximum data quality errors occur so that you can assess the root cause and design the plan according to that.
☛ Follow a standard process of verifying the important data before it is entered into the database.
☛ Identify any duplicates and validate the accuracy of the data as this will save lot of time during analysis.
☛ Tracking all the cleaning operations performed on the data is very important so that you repeat or remove any operations as necessary.

Some of the best tools useful for data analytics are: KNIME, Tableau, OpenRefine, io, NodeXL, Solver, etc.

The DISTINCT keyword is used in the SELECT statement to eliminate repetition of identical data. It is also used in aggregate functions. When DISTINCT is used with only one column or expression, the query will strictly return the unique values for that particular column or expression. Similarly, when DISTINCT is used with multiple columns or expressions, the query will return only the unique combinations of those columns or expressions. Note that the DISTINCT keyword doesn’t ignore the NULL value when sifting through data.

This question lets you assess if candidates have the hard skills you need and can tell you what areas they might need training in. It is also another way to ensure basic competency.

What to look for in an answer:

☛ Software the job ad emphasized
☛ Experience with the software
☛ Ability to speak with familiarity

Data analysis deals with collecting, inspecting, cleansing, transforming and modelling data to glean valuable insights and support better decision making in an organization. The various steps involved in the data analysis process include –

Data analysts require inputs from the business owners and a collaborative environment to operationalize analytics. To create and deploy predictive models in production there should be an effective, efficient and repeatable process. Without taking feedback from the business owner, the model will just be a one-and-done model.

The best way to answer this question would be to say that you would first partition the data into 3 different sets Training, Testing and Validation. You would then show the results of the validation set to the business owner by eliminating biases from the first 2 sets. The input from the business owner or the client will give you an idea on whether you model predicts customer churn with accuracy and provides desired results.

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