Are you preparing for a SQL developer interview?
Then read on as we give you the 7 crucial concepts in SQL that you must know thoroughly to help you sail through the interview.
Getting to know SQL
Structured Query Language or SQL is one of the most common languages for organizing and extracting data that is stored in relational databases. This language is a mainstay for most of the people working with data since most of the databases are managed relationally, thus making this language indispensable. SQL is used by data analysts to query tables of data and derive insights from it. It is used by data scientists to load data into their models. Similarly, data engineers and database administrators use SQL to ensure that everyone in their organization has easy and intuitive access to the data they need.
Interviews always depend on your knowledge and experience. However, there are some important concepts in SQL which you must cover. These topics will help you in basic as well as advanced SQL interview questions.
Tables are one of the most basic concepts in SQL that you need to understand as you may find SQL interview questions based on this. For instance- What do you mean by tables in SQL? This is one of the most common question asked by the interviewers, hence knowing what SQL table means is important. A table is a unique set of data with a consistent number of columns or typed data attributes. Each table should have a primary key i.e. a column that uniquely identifies a row.
SQL interview questions based on relationships in SQL are also frequently asked. For instance- What are relationships? Relationships are the links or relations between entities that have something to do with each other. So when two tables are joined, one is always considered as the ‘parent’ in the relationship and the other one as a ‘child’. Relationships and tables are the basic knowledge of SQL that an aspiring SQL developer should have.
Once you understand the basic knowledge of SQL tables and relations, you will be ready to build an understanding of what relationship means. To begin with, you need to understand the modality or the Ordinality of the relationships which specify whether relationship from the parent table to the child table is mandatory or optional.
The next important concept is cardinality or multiplicity of relationships. The SQL interview questions are often based around relationships in SQL to understand if the candidate has the basic knowledge of SQL. Cardinality is either one-to-one or one-to-many or many-to-many.
Both Ordinality and cardinality only scratch the surface of a database structure. Once you are clear with these concepts you can move on to more advanced concepts in SQL such as normalization and identifying relationships. For instance – one of the frequently asked SQL interview questions is – What is normalization and what are the advantages of it? Or explain the different types of normalization?
The concept of index also needs to be learnt thoroughly as one or two SQL interview questions are often based on this. For instance- What is an Index? Or explain the different types of index?
An index is a performance tuning method that allows faster retrieval of records from the table. It basically creates an entry for each value thus making it faster to retrieve data.
DROP, DELETE and TRUNCATE statements
One of the top SQL interview questions is- Explain the difference between TRUNCATE and DELETE statements? Or what is TRUNCATE, DROP and DELETE statements?
DELETE is a Data Manipulation Language or DML command whereas TRUNCATE is a Data Definition Language or DDL command. DELETE statement is used to delete rows from a table whereas to delete all rows from the table and to make the space free, TRUNCATE command is used. The DROP command is used to remove an object from the database.
To understand this concept you should know different subsets of SQL. This is explained in various SQL tutorials that are available online.
Query and Subquery
A query is a request for information or data from a database table or combination of tables whereas subquery is a query within another query.
One should know the concept of query and subquery in SQL very well as it is one of the frequently asked SQL interview questions. For instance, from a simple question like- What is a query? Or what is a subquery? What are its types? To questions which may ask you to write an SQL query for a given data can be asked in an interview.
Hope you will find the above information useful.
A step-by-step approach to answering any question in a technical interview
As anyone job-hunting knows, the most stressful part of the whole process is almost certainly the dreaded job interview! If you are pursuing a career in analytics, then the interview process can present its own unique set of trials and tribulations. But as with anything in life, the best thing you can do is to be prepared.
This article will help you with preparation - we are going to explain what to expect from an analytics interview and how you can best prepare.
What Can I Expect From a Job Interview for a Career in Analytics?
For most careers in analytics, companies expect you to be able to code well or at least know the syntax well enough that it’s not a barrier for you day-to-day. Therefore, while these skills will generally be put to the test, it’s not the only skill interviewers will focus on. In addition to the technical portion (i.e., the coding portion), you will likely need to solve a “use case”, which is a problem that they have experienced, a hypothetical problem, or one they are actively trying to solve.
They are testing you not only for your solution to the problem but they expect you to walk them through how you got there.
Steps to Success
1. Focus on Methodology Not on the Code
It is important to note here that they aren’t just looking for your solution. They want to see your approach to the problem and that your technical foundation related to the subject matter is strong. Even with the wrong solution, they could be impressed if you walk them through how you got there.
You need to show them that you understand the methodology and the underlying assumptions that you need to make to reach the solution. Therefore, you need to walk them through the assumptions that you made, and why you made them. For example, what are you assuming about the population of users?
You also must think about and explain the math that underlies your methodology. Think about what could affect the metrics that you are working with in this situation, and communicate that you understand what would cause these changes.
If you can’t see it already, communication is the key variable that will run through all of this advice. In explaining your methodology, you need to show a full grasp of the situation. Explain what you assume about the problem, and what you assume it will take to reach a solution.
2. Be Detail Oriented On The Code But Only When Asked
In a job interview problem, you will often be presented with a piece of code, and be expected to analyze it or correct the mistakes which may solve the problem. This is where it is extremely important to show that you are detail oriented. Before this part, however, you’re most likely focusing on methodology and approach to the question, so refer to tip #1 above first.
You are expected to walk the interviewer through each part of this problem. Look at the syntax and explain to the interviewer what each block of code is achieving. From here, you will be able to come up with a “big picture” of what this code is achieving, and understand what could be added (or removed) to reach a proper solution.
Once you have properly explained the entirety of the code, as well as your approach to the solution, walk the interviewer through what you believe that solution could be.
As you can see, the solution was important, but how you got there was equally important. An interviewer will be much more willing to forgive mistakes if they can see your thought process and see that you are mostly on the right track, with a solid understanding of the methodology involved.
3. Think About Edge Cases
In coding, it is always important to understand the edge cases, and a job interview is no different.
Think about situations where you think the code could break, and communicate that to your interviewer. It is especially helpful if you can relate these edge cases to specific scenarios that they would actually encounter in their business. This is a great opportunity to show not only your coding knowledge, but your understanding of their business.
Then, once you have identified these potential edge cases, suggest ways that you could account for them so that the problems don’t occur. A solution is always easier to reach once you have identified the potential problems clearly. This is your chance to show your interviewer that you are always thinking about potential problem areas, and able to solve them as well.
4. Don’t Accept the Obvious!
In any problem that is presented in an interview, always remember to not accept the obvious answer! If it were obvious, it probably wouldn’t be given to you as a question in a job interview.
That’s why it’s so important to consider the advice above. Consider every detail presented, look for holes in the code, and consider real business edge cases. By communicating all of this, you will likely be able to identify where the problem lies, and from there you can build a solution.
Remember, this is a complex problem that needs solving, otherwise they wouldn’t be showing it to you. If you are struggling at first, just take your time and walk the interviewer through it, they want to see your thought process anyways.
We can’t tell you exactly what problem you will encounter in your job interview. But by considering all the advice above, you can develop a reliable strategy to solving any problem you may encounter.
If you are interviewing for an analytics position that involves coding, the coding aspect should be almost second-nature by that point. The interviewer is more interested in how you break down the problem, how you identify the areas that need work, and how you work toward a solution. They also want to see that you know their business, which means considering specific edge cases and relevant factors that might be relevant to the competitive environment in which they operate.
So there you have it, take your time and be thorough, but most importantly communicate your thought process the entire way. And if you want some extra practice on your coding, check out my article here on the best niche platforms to learn SQL and Python! Good luck!
SQL, python, R, or Tableau? With so many tools to choose from, which ones do I need to know?
When it comes to the world of analytics, you probably wouldn’t be surprised to learn that it can get quite complicated. One thing that is typical of most analytical jobs is that you will likely need to learn how to code, which generally requires learning a programming/scripting language.
Which Programming Language is Best?
If you are getting into analytics, and considering it as a career, it’s not long before you can become pretty overwhelmed with all the technical platforms and languages you might need to learn to start your career. Therefore, if you are considering a career in analytics, one of the first questions you will probably have is — what coding languages do I need to absolutely learn? And which languages are “nice to haves”.
In this article, we’ll give you a rundown of our recommendations for the top programming languages to learn for a career in analytics. These are the languages that recruiters most often look for, and your best bet if you are trying to break into the world of analytics whether it be data science or business analytics. Let’s get started by outlining my top picks.
SQL (a must know)
SQL is a scripting language that is used for accessing data within databases. Databases are powerful tools for storing large amounts of data, and SQL is what is used to access and pull out that data, to manipulate the data, or to clean it up and reorganize it.
Basically, data that is accessed by SQL is stored in a relational database. Each kind of data is stored in a table. A table has columns and rows to represent different properties about different things. With SQL, you can access these tables, find information that is relevant, compare information, or even manipulate it. Of course, all of these commands go deeper than the span of this article, but just know that this is an essential tool for many careers in analytics.
One other important consideration with SQL is that different companies use different types of databases. For example, you have HIVE, MySQL, postgres, and many others, all of which have different nuances to their syntax. The good news is that if you have a good grasp of SQL in general, you should have no problem adapting to the differences in these databases.
There are many great online SQL resources. For example, if you're looking for a guide to teach you SQL from scratch, I like Mode Analytics. If you already know SQL (even if you're just a beginner) and are looking for real-world practice problems, Strata Scratch, provides over 500+ SQL practice problems taken from real interviews from companies.
Python or R (a must know, if you're going into a career in data science)
Two very popular programming languages for data science and analytics jobs are Python and R. These are very adaptable languages, and as such can serve similar purposes, which may make it tough to decide between the two. Depending on which you are familiar with, both can be quite helpful, but it is important to be aware of the differences depending on which specific area of analytics you want to go into.
R is primarily used in research and has developed to be very useful for the purposes of statistics. As such, it is widely used by data scientists and statisticians for a variety of features related to statistics and data analysis. There is basically an option for almost any type of data analysis you want to do. R stores its data in a wide variety of ways (tables, matrices, vectors, etc.) which allow for objects such as regressions, coordinates, and more.
Python is more of an all-purpose programming language. It is a very large language and as such it has libraries to perform almost all the tasks that R can. Python is also a very powerful tool for machine learning and artificial intelligence, with libraries built specifically to perform these tasks.
I like to use python over R because of python’s great automation libraries and functions.Of course, all of this may sound very complicated to a beginner. So just know that if you are considering a career in data science or analytics, Python and R can both be extremely helpful. They are both open-source languages with large and growing communities supporting them.
Datacamp.com provides great resources for both R and python.
BI Tools like Tableau (a nice to have)
Business Intelligence tools (or BI Tools) are types of software that basically help you visualize your data. These platforms help you visualize and identify trends, to understand patterns, and develop implications based on those patterns. These tools essentially take the outputs from SQL and or python/R and adds an interactive graphics component to help you serve up insights to your stakeholders and business partners.
One of the most popular BI Tools is Tableau. Tableau helps you to understand key business data points and make insights based on that data. It can connect to almost any data sources, including Salesforce, Google Analytics, and SQL databases. It presents all its information in a handy interactive dashboard, which also allows you to control and generate new information and insights.
So there you have it, our top choices of coding languages to learn if you are considering getting into a career in analytics. Of course, analytics and data science are very broad fields. For this reason, before you go all-in on a certain programming language, consider more specifically which part of analytics and data science you are most interested. Do some research on the types of roles you really want to pursue, and then identify which of the programming languages above would be most valuable.
The languages above all have extremely powerful capabilities within data science and analytics. All would be quite valuable for a career in analytics. No matter which direction you choose, knowing any of these languages would certainly open a lot of doors.
Resources To Jump Start A Career In Analytics
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