Answer for Error with the codes provided in the lectures

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Hi Lisa!

Thanks for reaching out and letting us know you encounter error code 1055 at this stage of the course.

We have addressed this issue later. Please refer to the following lecture and then retry running your queries.
https://365datascience.teachable.com/courses/360102/lectures/11680913 

Hope this helps.
Best,
Martin

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Answer for sql change and modify

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Hi Lisa!

Thanks for reaching out.

Sometimes the two options can be used as alternatives. In other situations, you can use only one or the other. For instance, as Vladimir suggested – With CHANGE you are able to change the name of a column while using MODIFY disallows that.

Throughout the course, we’ve used both to show that both can be used and because we did not want to deprive the course code from any of the two options.

That said, both are good to go in the NOT NULL Constraint lecture, as they can be interchangeable. Basically, the only difference is that you can alter a columns’ name when using CHANGE.

Hope this helps.
Best,
Martin

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Get Your Python Exercises

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Get Your Python Exercises 3

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Answer for DEFAULT constraint

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Hi Ashakah!

Thanks for reaching out.

Please make sure you don’t skip any lecture or exercise while taking this course. We gradually modify the contents of our data tables and it is essential that you don’t skip any steps.

That said, it seems that currently you don’t have a field called last_name in your customers table. Please make sure you’ve completed the exercise of the UNIQUE Constraint lecture – https://365datascience.teachable.com/courses/360102/lectures/5518488 (which is the lecture preceding the one you are referring to). 
In other words, please make sure to have created and altered the customers table by executing the following code:

CREATE TABLE customers (
customer_id INT AUTO_INCREMENT,
first_name VARCHAR(255),
last_name VARCHAR(255),
email_address VARCHAR(255),
number_of_complaints INT,
PRIMARY KEY (customer_id)
);

ALTER TABLE customers
ADD COLUMN gender ENUM('M', 'F') AFTER last_name;

After you can confirm that you have done that, please retry executing the code you’ve posted in your question.

Hope this helps but please feel free to get back to us should you need further assistance. Thank you.

Best,
Martin

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Answer for How To Import Data Into Python?

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Hi Lawal!

Thanks for reaching out.

Can you please support your question with the code you’ve executed, as well as with a screenshot containing the entire error message? Only then will we be able to provide a specific answer. Thank you.

Looking forward to your reply.
Best,
Martin

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Answer for Relationships between Tables in SQL

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Hi Gilles!

Thanks for reaching out.

Yes, a table can have only one primary key. And this key can be composed either by a single column (e.g. customer_id), or by several columns, in which case it will be called a composite primary key. In that case, the uniqueness of the key will not be defined by the value of a single column, but by the combination of the values of several columns. 

As an example of a composite primary key can be considered the combination of the playing card suit (Diamonds, Spades, Clubs, Hearts) and the playing card value (2, 3, 4, …, Jack, Queen, Kind, Ace). Neither the suit nor the value are sufficient to identify a unique value for the playing card. But in combination, the suit and the value can.

Hope this helps.
Best,
Martin

 

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Answer for SQL INSERT Statement

https://365datascience.com/dwqa-answer/answer-for-sql-insert-statement/ -

Hi Olanshile!
Thanks for reaching out.
We’ve answered this question here: https://365datascience.com/question/error-code-1452-cannot-add-or-update-a-child-row-a-foreign-key-constraint-fails-employees-titles-constraint-titles_ibfk_1-foreign-key-emp_no-references-employees-emp_no-on-delete-2/
But here’s the answer for your convenience.
Please stick to our general request to execute all code you see in the lectures and the exercises, in the given order. Doing this will prevent you from encountering some errors, such as this one – Error Code: 1452.
REASON FOR THE ERROR:
This error appears if you have already created another table, employees, where you have missed inserting data about the individual with id 999903. The relationship you have established between employees and dept_emp requires that you first insert a record in employees, and then insert a (related) record in dept_emp.
ON DELETE CASCADE means that if you remove record 999903 from employees, record 999903 will automatically be removed from dept_emp as well.
In brief, make sure the relationship between employees and dept_emp is valid and 999903 exists in employees so that you don’t get the same error the next time you try inserting 999903 in dept_emp.
SOLUTION: As explained in the article preceding this video, double-check if you’ve first inserted information about employee number 999903 in the employees table. Only then you should proceed with inserting information in the titles and dept_emp tables.

Hope this helps.
Best,
Martin

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Answer for Sometimes I would like to validate my results. How can I do this in this example (Exists-Not Exists)

https://365datascience.com/dwqa-answer/answer-for-sometimes-i-would-like-to-validate-my-results-how-can-i-do-this-in-this-example-exists-not-exists-2/ -

Hi Mike!

Thanks for your reply.

Do you think that the following query delivers the output you are aiming for? Thank you.

SELECT 
e.first_name, e.last_name, t.title
FROM
employees e
JOIN
titles t ON t.emp_no = e.emp_no
WHERE
title = 'Assistant Engineer';

Looking forward to your answer.
Best,
Martin

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What Is an ARIMA Model?

https://365datascience.com/arima/ -

 

In our previous tutorial, we became familiar with the ARMA model.

But did you know that we can expand the ARMA model to handle non-stationary data?

Well, that’s exactly what we’re going to cover in this post – the intuition behind the ARIMA model, the notation that goes with it, and how it differs from the ARMA model.

Let’s get started, shall we?

What is an ARIMA model?

As usual, we’ll start with the notation. An ARIMA model has three orders – p, d, and q (ARIMA(p,d,q)). The “p” and “q” represent the autoregressive (AR) and moving average (MA) lags just like with the ARMA models. The “d” order is the integration order. It represents the number of times we need to integrate the time series to ensure stationarity, but more on that in just a bit.

Convention dictates that we always enter the three orders in the same way – “p” first, then “d” and finally – “q” (ARIMA(p,d,q)). Of course, that’s because “p” represents the AR components, “d” the Integrated ones and “q” the MA ones.

How is ARIMA related to ARMA?

Any model of the sort ARIMA (p, 0, q) is equivalent to an ARMA (p, q) model since we are not including any degree of changes. Of course, an ARIMA (0, 0, q) and an ARIMA (p, 0, 0) would also be the same as an MA(q) and an AR(p) respectively.

Now that we’re familiar with the notation and how the different types of models are connected, we can continue with the intuition.

How do ARIMA models work?

These integrated models account for the non-seasonal difference between periods to establish stationarity.

Hence, even the AR components in the model should be price differences, (ΔP) rather than prices (P). In a sense, we are “integrating” “d”-many times to construct a new time-series and then fitting said series into an ARMA (p, q) model.

What does a simple ARIMA (1,1,1) look like?

Okay, since now we know this, let’s have a look at the equation of a simple ARIMA model, with all orders equal to 1. Suppose P is the price variable we’re trying to model. Then, the simple ARIMA equation for P would look as follows:

ΔP_t =c+ϕ₁ ΔP_t-1 + θ₁ ϵ_t-1 +ϵ_t

Just like we did in the other tutorials on time series models, let’s go over all the moving parts of this equation and break it down, so we can understand it better.

For starters, P_t and P_t-1 represent the values in the current period and 1 period ago respectively.

Similarly, ϵ _t and ϵ _t-1 are the error terms for the same two periods. And, of course, c is just a baseline constant factor. The two parameters, ϕ ₁ and θ₁, express what parts of the value (P_t-1) and error (ϵ _t-1) last period are relevant in estimating the current one.

Finally, we have ΔP_t-1 . In math, physics, and science in general, we express the difference between two values as Δ (delta). Therefore, ΔP_t-1 is the difference between prices in period “t” and prices in the preceding period (ΔP_t = P_t-1-P_t). Therefore, ΔP is an entire time-series, which represents the disparity between prices of consecutive periods.

Here’s an easy way to think about ARIMA models.

Essentially, the entire ARIMA model is nothing more than an ARMA model for a newly generated time-series, which is stationary.

How do we determine the orders of an ARIMA model?

We saw that the ARMA doesn’t have any functions like the ACF or PACF which suggest what the optimal order for the different components is. We can say the same about the ARIMA. After all, it’s a more complex model based on ARMA. So, our best bet is to start simple, check if integrating once grants stationarity. If so, we can fit a simple ARIMA model and examine the ACF of the residual values to get a better feel about what orders to use.

Peculiarities of integrated models

It’s important to note that we lose d-many observations when we deal with integrated values. This comes from the fact that there is no “previous” period, where we wish to integrate the very first day of the dataset. Simply put, we can’t find the difference between the first element and the one preceding it, because it doesn’t exist.

Similarly, if we integrate two times, we lose two observations, one for each integration. Even though we’d have an integrated difference in prices for the second day of the dataset (ΔP₂ = P ₁ – P₂), wouldn’t have one for the first (ΔP ₁= P₀– P₁), to compare it with. Therefore, we’d also have a missing value for the second day of the time-series, after integrating twice (Δ²P₂= ΔP₁– ΔP₂).

Simply put, for any integration we lose a single observation, so we should be aware of this when making our analysis. This is important because having empty values prevents the certain Python functions from compiling.

If you want to learn more about implementing ARIMA models in Python, or how the model selection process works, make sure to check out our step-by-step Python tutorials.

If you’re new to Python, and you’re enthusiastic to learn more, this super comprehensive article on learning Python programming will guide you all the way from the installation, through Python IDEs, Libraries, and frameworks, to the best Python career paths and job outlook.

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How to Become a Successful Data Scientist - 3 Experts Share Their Advice

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Becoming a successful data scientist is easier said than done. Especially if you’re a newcomer making their first adventurous steps into the field.

Sure, there are tons of super-helpful resources about the technical side of data science (which we utterly adore from the bottom of our geeky hearts). However, there’s another hefty challenge every aspiring data scientist needs to overcome. The challenge is the lack of clear understanding of what you should expect.

This is why we reached out to 3 outstanding data scientists with very different backgrounds, industries, and expertise, and asked them for some practical advice. And, in the spirit of sharing expert opinion and wisdom with the data science community, they were totally happy to lift the veil for you on the following:

• what’s missing in data science college education;


• the top challenges you’ll have to face in order to become a successful data scientist (and how to overcome them);


• the most valuable advice that will help you become a successful data scientists if you are just getting started or transferring from a completely different field.

We’re sure their remarkable insights will inform and inspire you, no matter where you are in your data science career preparation and development.

So, here’s what our data scientists have to say.

On what’s missing in data science college education…

Edouard Harris, CEO and Co-founder of SharpestMinds

“Generally speaking, good software engineering practice is lacking in schools. Sometimes, you work as a team in undergrad but the team comes together for the purpose of building one project, showing it to your Professor, and then walking away with a grade. Whereas for real software projects that really contribute to the world, you really need to write code in a way that would be understandable.

That’s the biggest hiring principle that’s missing at universities and is necessary in the workforce. And in grad school, we missed out on this, too. I went to grad school where you’re writing code for yourself, you’re not writing code for other people to read. Even when you publish your code, you don’t really care that other people are going to read it. You just want to get it published.”

Mayank Kejriwal, Research Assistant Professor at USC

“There are three things I think that colleges can do to keep its candidates attractive and viable in a job market that will tighten at some point, and is already under assault from alternative routes. First, require more technical acumen in-class assignments and projects to ensure students are applying what they’re learning, rather than engaging in a ‘check the box’-style engagement. Second, actively start prepping students for internships and interviews that are so necessary today for good full-time positions upon graduation. And third, make a networking and communications class compulsory for the students.”

Rosaria Silipo, Principal Data Scientist at KNIME

“Math and algorithms can be studied and learned. Attitude is harder to acknowledge and to change. I’ve worked with many junior data scientists so far. They’ve just completed a university degree or a specialization course in data science, and they think they are done and have nothing more to learn.”

On the challenges you need to overcome as a beginner to become a successful data scientist…

Edouard Harris, CEO and Co-founder of SharpestMinds

“Well, there are different types of beginner data scientists. But I’m going to imagine someone fresh out of school.

So, for someone fresh out of school, one of the challenges they face is writing code that will be read by a lot of people.

A second important one is being able to talk about your results with a business-side person. This kind of collaboration between business and tech doesn’t really happen in university. This is how the real world works, and yet, we’re not trained for this.

So, the ability to build an interface constructively with the other side and understand the problem of the business that is either losing you money or not making you enough of money is crucial.

And the third thing is, often, in very large companies, there are a bunch of cultural norms that an outside person is not familiar with, like, what’s the first thing you do in a meeting at that company? Basically, all of these are a dealing-with-people type of challenges. So, once you’ve got the technical training, you should reframe your mind, so that you can deal with other human beings constructively to build a productive enterprise, together. And that’s hard. It’s very hard to get large groups of people all rowing together in the same way. And it’s even harder when you’re just joining and you have no idea of the cultural norms and the consequences of mistakes that are easy to make early on.”

Mayank Kejriwal, Research Assistant Professor at USC

“Technical skills are only one required set of skills necessary for succeeding as a data scientist.

Without communication skills, it will be difficult for the data scientist to rise up in the ranks and hold their own in leadership positions.”

Rosaria Silipo, Principal Data Scientist at KNIME

“The first big challenge is in acknowledging that this job requires (and will keep requiring) continuous learning. There is no data scientist who knows it all. Or at least I have never met any. We all specialize in some specific techniques, data domains, or business cases. And even in what we know best, new optimization techniques, new loss functions, and new approaches often appear, and we need to learn them again. The university courses and the specialization courses all give us the capability of learning new techniques and applications quickly, but a big part of our job consists of continuous learning.

Another necessary change in attitude is about task organization.

A junior data scientist often thinks that their job is just to train machine learning models, which “automagically” generate fantastic insights and leave the customers in awe. Well, it is not that simple. Actually, training and applying one or more machine learning models is the easiest part. There are libraries in any tool exactly to do that. One node or one line of code will probably do the trick. The main challenge in a project comes before and after you train the model. Before that, you need to prepare the data so that they are clean and describe the problem accurately and informatively, making the model training much easier. After you train the model, you need to optimize it, as well as interpret and communicate the results.

All of these tasks are an integral part of a successful data science project and part of the data scientist job.

In addition, the data scientist can help to clean the data, for example. Not only manually but by creating and proposing more efficient automatic AI-based solutions. I insist on that because bad data produce bad results, no matter how smart the machine learning algorithm. So, cleaning data or presenting results is also an important part of the successful data scientist job. Finally, no AI-generated insight is as admirable if it can’t be communicated properly.

I know most of us come from a science or computer programming background and might not be well-versed with words. However, communication of the final results is as important as the solution itself. Many data science solutions fail during the deployment phase, and one of the most common causes is the inability of the data scientists to effectively communicate the power of the achieved results (see blog post: “The Deployment Pain”). One of the skills junior data scientists often lack is communication, both in speaking and writing, and they will need to learn it and master it if they want their data science solutions to be successful.”

On the most important things to remember if you’re just starting out (or transferring into data science from a completely different field)…

Edouard Harris, CEO and Co-founder of SharpestMinds

“It depends a lot on the career, but the broadest useful advice is: leverage your domain knowledge.

Essentially, you want to tell a story and also create a narrative about yourself. And the narrative you create about yourself when you transition is not “Oh, I’m changing everything about myself.” It’s more like, “No. I’m moving away to even further increase the value of the experience that I already have.” And too many people who want to make that transition are in a way embarrassed. But that’s the wrong way to look at it. You have to look at it as “I used to be this and I was awesome at it. This makes me even more awesome. Because now I have this amazing skill set and knowledge and also all of these tools available to me that make me even more of that.”

And one last thing. The only certain way to fail is to give up. And I’ve seen people that I’m sure are definitely getting a job. But for various reasons they don’t believe in themselves. And, sometimes life happens when you have to just make ends meet – that’s the way life is… But usually, success is just one notch above the level where you’re discouraged enough to give up. So, yes, there’s a wall but you can break through the wall if you push further.”

Mayank Kejriwal, Research Assistant Professor at USC

“No matter where you go or what you do (with very little exception), you’re going to be dealing with people. People interact with technology in different ways and need technology for different things. You can learn the most valuable lessons only by talking to different people about their needs. As technologists, we tend to stake our claims on solutions rather than problems. And we’re still very driven by features and automation. But the most valuable lesson I learned is to start from the problem and ask myself some cold, hard questions. What is the simplest possible solution to this problem and why isn’t it enough? Is it possible that I’m biased towards solution X or Y because I want X or Y to succeed as opposed to just solving the problem in the most efficient way possible?”

Rosaria Silipo, Principal Data Scientist at KNIME

“Learn the math behind the algorithms, not just how to apply them in a script.

Throughout your career, you will need to learn new tools. However, learning a new tool will be easier if you have a solid grasp of the math behind your data processing techniques.

Acknowledge that in this field you will never stop learning. It is the good and the bad thing of working as a data scientist. On one hand, your brain will never stop acquiring new concepts; on the other, you’ll need to invest time to acquire new concepts.

Take every new project as a great chance to learn something new… From a book, a colleague, experience, or something else. You never know where the next piece of new knowledge will come from!”

In Conclusion – The Path to Become a Successful Data Scientist

Data science is challenging. But it’s also a super rewarding field to start a career in! And sometimes, to become a successful data scientist, all you need is a little bit of extra motivation to keep you going. So, whenever the data science learning curve gets a bit steeper, we hope you’ll refer to this article for a daily dose of data science inspiration.

Special thanks to:

Edouard Harris

Edouard Harris is a successful data scientist and the co-founder of a company called SharpestMinds – a machine learning mentorship program based on income share. He was a physicist for about 10 years before he transferred into data science.

Mayank Kejriwal

Mayank Kejriwal is a research assistant professor at the University of Southern California’s Department of Industrial and Systems Engineering. He is also a research lead at the USC Information Sciences Institute (ISI) and works on projects for the initiative AI for Social Good.

Rosaria Silipo

Rosaria Silipo is currently a Principal data scientist at KNIME. She earned her doctorate degree in biomedical engineering in 1996. Rosaria has been working in the field of data analytics for 25 years. She’s also the author of the “Practicing Data Science” ebook and the KNIME e-learning course on data science.

Ready to take the next step towards becoming a successful data scientist?

Check out the complete Data Science Program today. If you still aren’t sure you want to turn your interest in data science into a solid career, we also offer a free preview version of the Data Science Program. You’ll receive 12 hours of beginner to advanced content for free. It’s a great way to see if the program is right for you.

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SQL Interview Questions

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SQL Interview Questions

SQL is one of the most popular coding languages today and its domain is relational database management systems. And with the extremely fast growth of data in the world today, it is not a secret that companies from all over the globe are looking to hiring the best specialists in this area. So, imagine you are at an interview for your ideal job and advanced professionals are sitting in front of you, interested in how you would perform. Such a meeting will be crucial for both sides. However, there’s no reason to freak out! To reduce the stress, here are our top tips to answering 10 frequently encountered SQL interview questions.

What is SQL?

SQL is an acronym for Structured Query Language. It is a programming language specifically designed for working with databases. Of course, some may argue and say it is not exactly a programming language since it has not been created with the idea of using features of procedural languages such as conditional statements or “for” loops. These people will insist on calling SQL a coding language because it is only about executing commands for querying, creating, inserting, updating, and deleting data in a database.

Nevertheless, it is more important to know what the domain of SQL is. But don’t rush to tell that to the interviewers, as this might be your next question! And in our exemplary excerpt with SQL interview questions, that’s exactly the case!

What is a Database? What is a DBMS?

A database, implying an electronic database, is data stored on a computer and organized in a way that makes it easy to access and manipulate. The software tool that allows the user to interact with the data stored in the database is called a database management system – DBMS.

You could wrap up the two questions by saying there are two types of database management systems – relational and non-relational. SQL is a language, designed only for working with relational DBMSs.

It is normal that the interviewers start with two fundamental questions that you feel at ease with. Thus you can relax and get ready to proceed with some more challenging ones.

More on the SQL language and database management systems you can read in our tutorial Why You Should Learn SQL.

What is the difference between DDL, DML, DCL, and TCL?

First of all, what do these acronyms mean?

“L” stands for “Language” in all of them. And this must help you remember that these are the four categories in which the SQL commands have been separated into.

DDL stands for Data Definition Language and includes commands which allow you to CREATE, DROP, ALTER, and TRUNCATE data structures. DML, instead, involves commands for manipulating information. It actually means “Data Manipulation Language”, and regards the possibility to SELECT, INSERT, UPDATE, and DELETE data. If you are using SQL in the sphere of data science or business intelligence, it is this part of the language you will most use at work.

DCL, Data Control Language, consists of commands that are typically used by database administrators. This category allows the programmer to GRANT and REVOKE rights delineating how much control you can have over the information in the database.

Similarly, TCL, which is the Transaction Control Language, also contains commands applied by database administrators. They ensure the transactions occurring within the database will happen in such a way that minimalizes the danger of suffering from data loss.

What is the point of using a foreign key constraint?

After you go through the fundamental SQL interview questions, you are likely to be asked something more specific. Therefore, your next task won’t be about explaining what SQL constraints and keys mean in general, although you must be very familiar with the concept. You will rather be given the chance to demonstrate your ability to elaborate on a specific type of an SQL constraint – the foreign key constraint.

The foreign key constraint comprises a set of rules, or limits, that will ensure that the values in the child and parent tables match. Technically, this means that the foreign key constraint will maintain the referential integrity within the database.

If you want to dig deeper into this subject, here we explain primary, foreign, and unique keys in more detail

Define and provide an example of using an inner join.

It’s not all about theory. Using a hands-on approach to handling realistic tasks is often times way more important. That’s why you’ll have to deal with practical SQL interview questions, too.

Obviously, you must be aware that joins are one of the most frequently used tools in SQL, regardless of your job role. Particularly if you are working in the sphere of business intelligence, your work will be centred around understanding SQL joins in depth.

So, an SQL join is a tool that allows you to construct a relationship between objects in your database. Consequently, a join shows a result set containing fields derived from two or more tables.

For instance, assume that in one table you have data about the customer ID and fields related to the sales a customer has made, and in the other, you have data about the customer ID and their private information, such as first and last name and email address. Therefore, an inner join allows you to obtain an output containing information from both tables only for the customer IDs found in the two tables that match. Provided that you set the customer ID field to be a matching column, of course.

Using the previous example, explain how to use a left join.

SQL joins is such an important topic that it could lead to a follow-up question. It is good to provide a sharp answer in this case.

You could say “Unlike an inner join, a left join will ensure that we extract information from both tables for all customer IDs we see in the left table. The customer IDs that match between the two tables could contain data from the right table as well, while the IDs that are only found in the left table will display null values in the place of the columns from the right table.

To expand your knowledge on this topic, check out this article

What is the difference between MySQL and PostgreSQL? How about between PL/SQL and SQL?

Now, this is a tricky one.

Basically, the reason for encountering an SQL interview question like this is that the interviewer wants to understand the extent you are acquainted with the fact that SQL has a few versions, each carrying specific characteristics.

You could say that MySQL and PostgreSQL are just two versions of the Structured Query Language. Since you’ve just been asked about joins, you could mention that PostgreSQL supports outer joins, while MySQL doesn’t – you’ll need to use UNION or UNION ALL to emulate an outer join in MySQL. And thus, you could perhaps impress the interviewers with additional knowledge in this subject.

 

PL/SQL is not a version of SQL, though, and that’s the tricky part of the question. PL/SQL is a complete procedural programming language and its scope of application is different. It is not strictly related to relational databases.

What is this query about?

SELECT 

    emp_no, AVG(salary)

FROM

    salaries

GROUP BY emp_no

HAVING AVG(salary) > 120000

ORDER BY emp_no;

The version of SQL in which this query has been written is MySQL, but you won’t really need to mention that. Even if you don’t recognize the version, then common sense, the keywords you see, and the names of the fields should convince you this query is about extracting the average salary obtained by employees only when the salary value is larger than 120,000 dollars.

And don’t be surprised if after you provide your answer, the interviewer asks: “And the database won’t throw an error?”. Read the query carefully before you reply. It is much better to double-check and be sure that in this situation, everything is correct.

More on the differences between using WHERE or HAVING you can find in this tutorial

The following two tables are part of the database you are working with. Write a query that will display the salaries received by the last contract of a given employee as a result. Limit the number of obtained records to 1,000.

SELECT 

    s1.emp_no, s1.from_date, s1.salary

FROM

    salaries s1

WHERE

    s.from_date = (SELECT 

            MAX(s2.from_date)

        FROM

            salaries s2

        WHERE

            s2.emp_no = s1.emp_no

        GROUP BY emp_no)

LIMIT 1000;

As a matter of fact, this is a question about using an SQL subquery – a subset of SELECT statements whose output sets the conditions which the data for the main query will be filtered upon. However, you might not be given this hint, so it is on you to remember that in such a situation a subquery is exactly what you need.

And this is a rather complex query, to be honest. However, by asking you to create one, the questioners can check your command of the SQL syntax, as well as the way in which you approach solving a problem. So, if you don’t manage to get to the right answer, you will probably be given time to think and can definitely catch their attention by how you try to solve the problem.

Curious to know more about using SQL Subqueries? Then go to this tutorial.

What is an SQL View?

To conclude the interview, your potential future employers may prefer to give a toned-down SQL interview question. That’s why they might ask you something that is not related and revert back to asking a general question.

A view is a virtual table whose contents are obtained from an existing table or tables, called base tables. The retrieval happens through an SQL statement, incorporated into the view. So, you can think of a view object as a view into the base table. The view itself does not contain any real data; the data is electronically stored in the base table. The view simply shows the data contained in the base table.

If you’re interested in learning more about this tool, check out our tutorial Introduction to SQL Views.

General tips.

Although you may have answers to all the SQL interview questions you’ve been asked, there are many other components that will determine whether you will land the job. The company you are applying for may have very strict requirements regarding work ethics, backgrounds of employees, and so on. And it all counts, trust me. So, if you want to be fully prepared to make a great first impression, check out the most comprehensive article out there: Starting a Career in Data Science: The Ultimate Guide.

However, nothing else will really matter if you are not a good professional, right? That’s why you have to stay focused on SQL and learn as much as you can about it. If this is what you are eager to do next, check out the tutorials we provided above, or feel free to find more content about SQL on our blog.

Good luck!

 

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Data Science Resume for University Graduates

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How to write a data science resume for university graduates?

Getting your first job after university is not straightforward in any discipline.

Data science is no exception, but easy is no fun, right?

It took everything you had to make it through your education and now you’ve got to start again with the job market. You feel like a newbie. And how does a newbie make an impact? How does a fresh face stand a chance against veteran data scientists with years of experience?

Like I said, it’s difficult.

But data science is a fantastic field to be part of – companies are hiring all over the place. They are looking for enthusiastic and driven new recruits! You know you’d make a superb data scientist and you must show them that.

How? With an awesome resume!

And we’re here to help!

Let’s go through how to write a data science resume for university graduates and share tips with you to help you represent your wonderful self on a sheet of A4. (And then, you can jump straight to our ultimate career guide to discover 10 of the best data science job boards you should bookmark before you send out your resume.)

A couple of general tips before you start

Let’s start with some things to consider before you take pen to paper… or fingers to keys.

How to organize the writing process?

Before you start writing anything, you need to decide on your audience (the employer) the purpose of your text (to show how awesome you are) and the goal you want to achieve (an invitation to interview). Write it on a post-it note so you can always remind yourself.

When you start writing, write everything.

With a CV this will be all your experience, all your skills, everything you want to say about you, your ‘master-copy’ if you will.

After that, is the editing, cross-checking what you have written with your little post-it note, taking all the relevant information and putting it in a neat little package.

Why are keywords important?

Recruiters spend anywhere between 5 and 30 seconds looking at a CV. And they don’t read it, they scan it looking for keywords. Words that signify an applicant is even worth considering.

Many even use an Applicant Tracking System (ATS) which is computer software that does the scanning for them.

Skills are the main keywords, but we will get into that further on.

The company name is a great keyword. It will grab attention and it’s a small touch that makes an enormous difference.

You’ll find the best keywords in the job description and a clever idea is to have a quick google of trending keywords. The more you have in your data science resume, the better. And scattering them through your CV will make the recruiter (or her robot) scan the entire CV.

Lying in your CV is tempting but, oh, so stupid. So, if you want to boost the number of keywords with true backable skills, take some extra courses. Here at 365 we have recognized and valuable courses that will make your CV shine with professionalism. Click here to see some.

Now on to the good stuff!

How to write your data science resume?

Format

When you are transforming your ‘master-copy’ into your ‘masterpiece’ it’s good to keep it on one page. Some recruiters are fine with having more but from what I’ve found, they are the minority. The ones who do like the CV on one page, REALLY like it on one page. Double-side it if you really need to but if you focus on your audience and your goal, one page should be fine.

Have a play around with font and sizing, but I’d recommend something easy to read and a size that fits your CV nicely. Don’t go any smaller than 10 though, find a way to lose some words instead.

Choose a photo… then get someone else to approve it. Get one taken professionally if necessary. Photos on a resume are common now* so do it right. And smile, it’s not a passport!

* Some companies explicitly state that photos are not allowed in a CV due to their equal opportunity policy. Those are usually large corporations or banks, so be careful with that.

Contact information

This should be the easy bit – make sure you spell your name right, and obviously, you’re not going to get any call-backs if you write the wrong telephone number.

In all seriousness though, make sure you have a professional sounding email – your name plus some numbers is fine.

Feel free to add any social media accounts to this section, LinkedIn, Facebook etc. If you have a Blog or a website which shows off your skills.

Have a read of our personal branding piece for invaluable information to make your online persona as suave and sophisticated as your real-life one.

Trust me, you may think your privacy settings are water tight, but the internet is sneaky and a shirtless photo of you riding a rodeo bull can always slip through the net.

Sort yourself out a nice profile photo and tactically share some professional sounding posts from BBC news for all to see, you can always change it back once you get hired!

Objective vs summary (or not)

First, What’s the difference between an objective and a summary?

An objective is a short statement saying what you want career-wise.

For example, my objective would be something like:

“English linguistics graduate looking for a writing job, with high salary to fund my cat clothing obsession”

If you ask me, your CV itself is an objective. You want a job for the company you are applying for. So, you gave them your CV.

Unless you are planning on telling them what they want to hear, don’t bother with an objective.

How about a summary?

The goal of a summary, and in fact your whole CV is to show your best qualities in short space.

So, remember to keep it focused – don’t waffle on about irrelevant points. Keep your audience in mind.

Avoid fluff words – “enthusiastic, team-player, loves a challenge, quick learner”, everybody says this, prove your statements with measurable facts.

Sell yourself, don’t be shy, you’re great! You want to stand out in a crowd of experienced data scientists. Put yourself in an employer’s shoes, what would you want in a candidate? Someone just like you right? Show them that.

 

If you really, really need to save space, you can put your summary in a personalized cover letter. A cover letter gives you more words to play with, a chance to thrown in some specific keywords and most positions require a cover letter.

Skills

The skills section is one of the most important, especially for a data scientist and especially especially for a recent graduate! Because you haven’t got much experience, skills are your strength. Skills are your keywords, and you should litter them throughout your data science resume for university graduates. The skills list is just for reference, the examples of your skills are where you show off.

I was going to say go and do your research, find trending buzzwords, work out what skills a data scientist should have etc. Instead, I found a site that lists almost all the skills that employers will be looking for in a data science application.

You’re welcome!

Your job is to go through these, pick out the skills you own and think of times when you have used them. Write them all down. These will lead to that interview.

There are two types of skills, hard and soft: hard are your data science technical skills – R, Python, SQL etc.

Write down as many real-world examples using these skills as you can.

Stating how you “used Tableau to visualize the monthly sales at the car dealership you were interning at. This led your manager to see when the company sold the least add-ons and in turn promoted them at this time, increasing sales by 50% over 3 months” is more hard-hitting than just writing “Tableau” in your ‘Skills’ section.

Note that hard skills can be very job specific. So be sure to check job descriptions; one employer may prefer R over Python and you need to adjust accordingly.

Soft skills are your transferable skills – time management, leadership, communication, etc.

Having examples of these is arguably more important than the hard skills, particularly for a graduate. They will let employers know that you can adapt to and thrive in the work-place. Unless it’s a research-based position, most employers will want to know that you are a capable data scientist outside of academia.

So, you should end up with a bunch of skills and a handful of good examples of when you’ve applied these skills. This will be your master list. You should make it as exhaustive as you can because it will be the backbone for the rest of your data science resume for university graduates.

Education

I highly suggest putting your education right after your summary (if you choose to have one). As a graduate, it is your strongest area. You can fill this section with skill, experiences, and work attitudes while showing off your wealth of knowledge at the same time.

Start with your most recent qualification and work backwards. Don’t bother with high school grades-waste of space. If you were accepted into university they couldn’t have been that bad, and they’re certainly not relevant. Lay it out with your degree > school > dates unless your degree is not directly related to data science and your university is one of the best, then you can go with school > degree > dates.

You need to write as many things as you can think of (keep them in your master-copy). If it shows off any skill, write it down. But again, be specific.

This is another chance to brag a little, talk about your thesis, publications, research projects. A benefit of being an academic is your breadth of knowledge. So, show this off.

Jobs in the data science field vary and some positions require more time in academia than others. Your theoretical knowledge is your desirable trait at this point, but lack of experience can raise warning flags for certain employers. So, use this section to your advantage, fill it with technical skills and what you have learned and researched, but use the job description to judge how heavy you rely on it as you may want to put more effort into the next section…

Experience

I’m kidding, you’ve got loads of experience! You’re a young adult who has spent years at university – living life, learning new things, and growing into an awesome data scientist. Now is just the point where you must find experience which is relevant and will make an employer want to get you on their team.

So, how do we do this?

Yes, again, we start strong! Don’t start with your most recent job if it was working as a waiter in a cocktail bar. Your 6-month internship with *insert big-deal company here* should be at the top. You can use your part-time student jobs though as they will highlight your transferable skills. The ones which flaunt that you can fit right into the workplace. Your killer Mai Thai may go down a treat at the staff party but no need to include it in your data science resume for university graduates. However, this is the best place to show off your ‘soft skills’ “Used my verbal communication skills to reduce customer compensations by 35% in June/July” for example.

Employers respect someone who works hard, especially through their studies.

Oh, you didn’t work during your studies? Bank of mum and dad kept you in the black?

No problem, then list your independent projects. Experience doesn’t have to be paid work. Have you been on Kaggle or GitHub? If not, get on there… like NOW! They are great platforms to get some practical experience in data science. Kaggle gives you competitions to do and GitHub is a site for posting your code. These are also great points that you can talk about in your interview.

And speaking of things to bring up in the interview…

Activities and interests

This is where you put a little bit of personality, show them how interesting you are as a person on top of everything.

Don’t play it safe with “I enjoy reading, walking my dog and staying on late at work” Yawn! That’s why they call it ‘interests’. Give the recruiter one last punch in the memory!

Do you play a weird instrument? Bungee jump? Collect dinosaur bones?

The more unusual the better!

You’ve already blown them away with your data science resume for university graduates. This will leave them with one last thing to make sure it sticks in their head.

References

Don’t put your references. I wouldn’t want my details handed around all over the place. When you’ve been offered the job, then give your references. Tell them they will be contacted. It won’t look good on you when your course lecturer responds with “who?” when asked to give a gleaming recommendation about you.

Excellent, I hope this has helped get you pumped to write an epic data science resume for university graduates. You’re going to do great, it’ll take some work on your part, sure, but follow these tips (and if you’re sensible, tips from other articles as well) and you’ll be well on your way to getting that data science job.

To sum up:

And again. Good luck! 

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Data Science Interview Questions

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Answering data science interview questions: one of the trickiest obstacles you will have to overcome in your quest to become a data scientist!

It might calm your nerves to know that almost every job seeker struggles. That’s because data science interview questions cover a bunch of different topics (data science is an interdisciplinary field, after all) and those cheeky interviewers love to throw you the odd curveball.

The first step to hitting those curveballs out of the park is to see them coming, and to see them coming you’ve got to be confident about the rest of your game.

So, you must do your homework! An interviewer can spot someone who hasn’t from a mile away, but you wouldn’t be here if you didn’t know that already though, would you?

There are plenty of articles out there that will give you all the example answers you could hope for and yes, technical questions will come up (so, it’s worth brushing up on the details). But to remember one hundred-odd different examples would only serve to confuse you more, plus what if a question comes up you didn’t study for?

We want to take you through the interview typology. Show you what data science interview questions are made of and what the interviewers are looking for. Each section will include tips and strategies on how to best approach a question in a logical way, and what do data scientists love? That’s right…

The money!… O.K, the logic!

Data scientists go crazy over logic, so it makes sense for you to comprehend the underlying principles, rather than to repeat someone else’s words.

This is not only how you understand the game, but how you win it!

The following are the types of question we will cover, you can read them in whichever order you like, as long as it’s logical!

Let’s begin!

1. Technical questions
   
   1. Mathematics
   
   
   2. Statistics
   
   
   3. Coding
   
   
   4. Machine learning
   
   


2. Practical experience questions


3. Behavioural questions


4. Scenarios (A.K.A case study questions)

1.   Technical questions

A strong grasp of mathematics, statistics, coding, and machine learning is a must for a data scientist. You are likely to be asked to demonstrate your hands-on technical skills but prepare to show off your theoretical techniques, too!

You must possess a considerable amount of knowledge, as your interviewer will want to measure that knowledge. They will also want to know how well you can articulate complex concepts – Something any data scientist should be comfortable doing.

1.1 Mathematics

Mathematics underpins the study of machine learning, statistics, algorithms, and computer architecture, among others. So, applied maths is at the heart of the matter. Showing a good grasp of mathematics signals to the interviewer that you could quickly adapt to those other fields.

Data science interview questions

Be prepared to answer some quick (mental) maths questions, such as:

• What is the sum of numbers from 1 to 100?


• A snail falls down a well 50ft deep. Each day it climbs up 3ft, and each night slides down 1ft. How many days does it take him to get out?


• You have a 10x10x10 cube, made of one thousand 1x1x1 cubes. If you remove the outer layer of this structure, how many cubes will you have left?

Questions like these are to check you have basic maths skills and shouldn’t be too tricky for you.

Things become a little more interesting when encountering puzzle questions. Employers use them to test your lateral thinking. Use them as an opportunity to show off your problem-solving skills. Think outside the box. Be sure to vocalise your solution. This will give the interviewer an idea of how you go about solving a problem, even if you don’t come to the right solution (or maybe you found an even better solution and the interviewer won’t believe you unless you tell them how you came to it).

Here are some real-life data science interview questions:

• A race track has 5 lanes. There are 25 horses and one would like to find out the 3 fastest horses of those 25. What is the minimum number of races one would need to conduct to determine the 3 fastest horses?


• Four people need to cross a rickety bridge at night. Unfortunately, they have a single torch and the bridge is too dangerous to cross without one. The bridge is only strong enough to support two people at a time. Not all people take the same time to cross the bridge. Times for each person: 1 min, 2 mins, 7 mins and 10 mins. What is the shortest time needed for all four of them to cross the bridge?

Finally, there are those hard maths problems.

It is unlikely that you’ll be given an equation to solve, rather you’ll be asked a simply worded question which requires conceptual preparation to answer. Furthermore, it may intertwine with probability theory, even if it seems it doesn’t.

Some examples are:

• Consider an extension of rock, paper, scissors where there are N options instead of 3 options. For what values of N is it possible to construct a fair game, whereby ‘fair’ we mean that for any move that a player plays there are an equal number of moves that beat it or lose to it?


• In a country in which people only want boys, every family continues to have children until they have a boy. If they have a girl, they have another child. If they have a boy, they stop. What is the proportion of boys to girls in the country?


• A fair coin is tossed at each stage. The player wins the game when tails appears. The payoff depends on the number of heads that appeared prior to the tails. If the game ends at the first stage, the payoff is 2 dollars. If it ends on the second stage, it is 4 dollars. On the third – 8 dollars, and so on. At each stage the payoff for winning doubles. What would be a fair price to pay a casino for entering the game?

Finally, don’t get surprised if they ask you to solve some problems along the lines of:

• What is the first derivative of x^x


• Why is irrational

While not being able to answer such questions is not a deal-breaker solving them will help you stand out from the crowd. These problems are given to check if you are interested in mathematics, showing enthusiasm and some logical thinking could be more impressive than finding the solution.

1.2 Statistics

Did you know, data Scientists were once called statisticians? The two professions aren’t one and the same, but many data scientists have finished a statistics degree. And that’s no wonder! Statistics is one of the ‘founding fathers’ of data science. Logically, you will be tested on your ability to reason statistically. Even if theoretical knowledge isn’t your strongest suit, you need to use precise technical language.

Consider the following question: What is the difference between false positive and false negative?

It seems that you need to provide some textbook definitions…

Got you! Nobody wants to hear generic theory; it’s boring and you will blend in with the crowd.

Employers will want you to identify situations where you can implement the theory.

If there is a whiteboard, use it! Draw a confusion matrix! Go through the theory and show how it applies!

While still talking statistics, what are some other questions that may pop up?

• What is the null hypothesis and how do we state it?


• How would you explain a linear regression to a business executive?


• Tell me what heteroskedasticity is and how to solve it.


• What’s the Central Limit Theorem and what are its practical implications?


• How do you find the correlation between a categorical variable and a continuous variable?


• Explain p-value. Present it as if talking to a client.


• What do you understand by statistical power and how do you calculate it?


• Please explain the differences between overfitting and underfitting.


• Explain what cross-validation is. How and why is it used?

Did you think those last two are machine learning questions? Well spotted, now we see that ML overlaps with statistical concepts!

• Could you give examples of data that does not have a Gaussian distribution, nor log-normal?


• What is your favourite statistical software? State three positive and negative aspects of it.


• Explain bootstrapping as if you’re talking to a non-technical person.


• State some biases that you are likely to encounter when cleaning a database.

We have stepped away from dull statistics and taken a lunge forward to… practical data science.

1.3 Coding

Every data scientist needs a certain amount of programming knowledge. You don’t have to be a pro, but employers will want to see that you have a decent grip on it and have the potential for rapid improvement.

Python, R, and SQL are the bread-and-butter programming languages in data science. Questions about these three staples should not come as surprise.

R and Python are interchangeable, so knowing one or the other will usually suffice (but knowing both won’t be a disadvantage).

‘Can you be more specific?’

Yeah, sure:

R

• How are missing values and impossible values represented in R?


• What is the difference between lapply and sapply?


• How do you merge two data frames in R?


• What is the command used to store R objects in a file?


• How can you split a continuous variable into different groups/ranks in R?


• Please explain three key differences between Python and R.

Python

• Which Python library would you prefer to use for Data wrangling?


• How can you build a simple logistic regression in Python?


• What’s the shortest way open a text file in Python?


• Have you done web scraping in Python? How can you do that?


• Please explain what is a ‘pass’ in Python.


• Please explain how one can perform pattern matching in Python.


• You have duplicate values in a dataset for a variable in Python. How would you handle them?


• What tool would you use in Python to find bugs?


• What’s your preferred library for plotting in Python: Seaborn or Matplotlib?

SQL

• Explain the difference between INNER JOIN and OUTER JOIN.


• You have a table called with Cust_ID, Order_Date, Order_ID, Tran_Amt. How would you select the top 100 customers with the highest spend over a year-long period?


• If you were stuck on a desert island with a database that contained all the knowledge ever created, but you only had 10 SQL statements that you could ever use, what would they be?


• Describe the different parts of an SQL query.


• What is the difference between DELETE and TRUNCATE?


• What is the difference between UNION and UNION ALL?


• Write down a SQL script to return data from two tables.


• What is the difference between a WHERE statement and a HAVING clause?


• Tell me the difference between a primary key and a unique key.


• What is the difference between SQL, MySQL and SQL Server?

Often these programming questions are written on a whiteboard, meaning you may want to practice coding on paper, away from the computer. Ouch!

1.4 Machine Learning

A familiarity with machine learning methodologies is essential for every aspiring data scientist. You should be prepared to explain key concepts in a nutshell.

It’s quite possible that the interviewer will outline a prediction problem and ask you to come up with algorithms. With the algorithms, expect to touch upon commonly observed problems and their fixes.Check out the following machine learning questions we’ve picked for you:

• What is the difference between supervised and unsupervised machine learning?


• Explain your favourite algorithm to me in less than a minute.


• How would you deal with an imbalanced dataset?


• How do you ensure you are not overfitting with a model?


• What approaches would you use to evaluate the prediction accuracy of a logistics regression model?


• Explain the steps needed for data cleaning and wrangling before applying machine learning algorithms


• How do you deal with sparse data?


• Could you explain the Bias-Variance trade-off?

Additionally, you may stumble upon way too specific or way too vague questions such as:

• Explain the difference between Gaussian Mixture Model and K-Means.


• Tell me about a machine learning project you admire.

Remember the whiteboard tip?

Make it your interview BFF! Trying to answer a machine learning question with only words would take at least 5 minutes. And that’s 5 minutes you could spend giving the interviewer examples of other amazing things you know. The interviewer will already know the concepts, so you can exemplify your answer with a drawing and a short explanation taking less than two minutes. Voila!

2.   Practical experience questions

Technical questions are important, and a data scientist needs to know the answers and how to put them into practice.

There are countless data science questions and an interviewer is not going to waste time asking dozens of questions to gauge whether you are the candidate for them. Instead, why not ask you to give your experience.

These are practical experience questions, designed to shed light on your pace of work, experiences, and habits. To avoid having to sift through your back catalogue of experiences on the spot, have in mind a few experiences that are versatile – Ones that exemplify different skills based on the question.

Let’s give you taste of those:

• Summarize your experience.


• Tell me about your first data science pet project.


• How do you keep up with the news about politics, economics, and business? What about data science?


• So, Python is your preferred programming language. What experience do you have with R? Tell me what you have done with that.

Of course, you can get it vice-versa:

• 
  
  • So, R is your preferred programming language. What experience do you have with Python? Tell me what you have done with that.
  
  
  • Do you have experience in Tableau?
  
  
  • What kind of RDBMS software do you have experience with?
  
  
  • Have you taken any online courses related to data science? If yes, how many did you complete with a certificate?
  
  
  • What companies have you worked at? What was your role? Elaborate on the day-to-day activities you were asked to perform.
  
  
  • Do you have a project portfolio? Maybe a GitHub or a Kaggle profile? What projects have you implemented? *They may pick the most interesting one to them* Let’s discuss this specific project in detail
  
  

3.   Behavioural questions

Like any other job interview, employers are interested in how you handle workplace situations, how you work in a team and whether you are a good fit for the company.

Behavioural questions can be asked indirectly, for example, the interviewer may pose broad questions about your motivation or the tasks you enjoy.

Certainly, there is not a right answer here. The intent is to judge your past responses as they can accurately predict future behaviour. Moreover, behavioural questions are also seeking to evaluate if you can communicate clearly and persuasively.

Let’s see an example: Describe a situation when you faced a conflict while working on a team project.

Instead of asking hypothetical questions (“How will you deal with…”), the interviewer is hoping to elicit a more meaningful response by pushing you to chat about a real-life past event. Don’t fall into the trap of just generalising your example, the interviewer will be looking for four things in your story:

• Situation: What was the context? (devote around 10% of the answer time)


• Task: What needed to be done? (devote around 10% of the answer time)


• Action: What did you do? (devote around 70% of the answer time)


• Results: What were the accomplishments? (devote around 10% of the answer time)

Also known as the STAR technique, these steps will help you present your answers in a clear and succinct fashion. Don’t get confused – they don’t want a rigid answer with each step accounted precisely but a story that delivers the technique in a flowing yet concise way.

Bear in mind that some behavioural questions are long-winded and sound vague, but the STAR approach comes in handy whenever you hear: “How did you deal with…” or “Describe a time when…”.

Here are some hot tips when answering behavioural questions.

Show some passion – enthusiasm about your past experiences shows you are a person who cares about their work, don’t make the mistake of thinking your employer will want to hear how much you hated your previous jobs. On that note though, try and be specific – don’t go off on a tangent about all the things you liked about the job, stay relevant, you don’t want to appear like you can’t focus on the point at hand. And lastly, if your story describes some conflict with another team member, end on a positive note – show you are not someone to hold a grudge

Dying for examples? Here you go:

• 
  
  • Please describe a data science project you worked on (Yes! It overlaps with the ‘practical experience category!)
  
  
  • Tell me about a situation when you had to balance competing priorities.
  
  
  • Describe a time when you managed to persuade someone to see things your way.
  
  
  • How did you deal with a situation when you had to adapt to a difficult situation?
  
  
  • Describe a time when you were bored at work. What did you do to motivate yourself?
  
  
  • Select a product or app you really like and make a recommendation on how it could be improved.
  
  
  • Describe a situation where you effectively worked under pressure.
  
  
  • What have you liked and disliked about your previous position?
  
  
  • Have you ever faced a problem you couldn’t solve?
  
  
  • Describe a situation when you failed to meet a deadline.
  
  
  • Our team is brand new and is under-financed. We have no standard procedures or training, and everything is ad-hoc. How would you go about this situation?
  
  

Curious to find out what are the 5 skills you need to match any data science job description? Then check out this article.

4.   Scenarios (aka case study questions)

The purpose of scenarios is to test your experience in various data science fields. Case study questions will likely look for skills outside of the technical toolkit. For instance, they may be looking for logical reasoning or business understanding. It’s important for you to demonstrate structured thinking, reasoning, and problem-solving skills. After all, you can’t be a good data scientist if you cannot identify the underlying problems.

Let’s see how this works:

The sales department has increased the selling price of all items by 5%. There are 10 items, all with different price tags. Before the price increase, gross revenue was $500,000 with an average selling price of $1. After the price increase, gross revenue was $505,000, with an average selling price of $0.95. Why hasn’t the price increase had the desired impact of increasing revenue and average selling price?

This question requires thinking in a business context, so you need to come up with insights and clearly communicate them. Scenarios are a great opportunity for the employer to get a sense of how you tackle problems which will reflect your overall attitude towards work.

You can be also given market sizing questions, called guestimates by some, a term that sounds like you just need to take a stab in the dark, which is just not the case. While reaching a conclusion does require a degree of guesswork and estimation, the process of how you use them is difficult and requires rigid logic.  There is not a single correct answer to questions like these and chances are that the interviewer doesn’t know the exact answer, either. Here is an example:

How many SUV’s in the parking lot downstairs? How many ping-pong balls can fit into this room?

You’ve likely come across questions like these. For market sizing problems, your result should be of the same order of magnitude as the actual number. In any case, don’t worry too much about the figure. In fact, the employer won’t be that interested in the result, more the path you took to provide a number. So, focus on the structure instead, and don’t forget to articulate the problem-solving process you undertake. The easiest way to approach the problem is to write down your structure and then speak out loud – don’t skimp on your reasoning.

Some questions don’t have exact answers.

In any case, you may want to practice on these real data science interview questions:

• If a product costs $4.00, with an $8.00 sunk cost, and we charge X amount of dollars along with a $10 annual fee, how many do we need to sell to break even, etc?


• The conversion rate for a specific chair is 0.5% for the first 50,000 shoppers that look at it. The price of the chair is $250. Our company makes 27% profit on the sale. The next 50,000 shoppers will get a 10% discount. What is the conversion rate we must achieve to receive the same profits as before?


• You get X amount of views on a website, Y amount of people click on the ad, then Z amount of people enter their names after, where X, Y and Z are given. How much does it cost to acquire a customer? What’s the conversion rate? Would it make sense to run the campaign comparing the value of customer acquisition to the revenue gained from conversion rate?


• How many mattresses are sold each year in the United States?


• What will be the size of 3D TV sets in India?


• How many data scientists are there in the USA?

Some questions seem odd, right? That’s normal. Don’t hesitate to ask clarifying questions to get to the point. Questions won’t make you look like you have gaps in your knowledge, but rather will show that you pay attention to detail.

For more on the business aspect of a data scientist’s job, read our article 5 Business Basics for Data Scientists.

An interview is a dialogue, not a written test!

Excellent, now you have read through the article, consider our typology as the starting point in your interview prep. However, we have only scratched the surface when it comes to examples of data science interview questions you may encounter. The industry is booming and as such, companies are constantly adapting their interview sessions (what may be a common question today may be one hardly asked in 2 years). This is especially true with start-ups that undergo constant changes. Data science interview questions vary in their peculiarities, but the types of questions remain the same, so having a base knowledge of these types with a good amount of preparation will allow you to logically tackle any question the interviewer has up her sleeve.

Good luck!

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