# How To Solve The Formula For The Specified Variable Handling Time Series Window Functions in Data Science Interviews

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## Handling Time Series Window Functions in Data Science Interviews

Data scientists handle time series data on a daily basis and being able to manipulate and analyze this data is a necessary part of the job. SQL window functions allow you to do this and it’s a common query for interview data. So let’s talk about what time series data is, when to use it, and how to implement tasks to help manage time series data.

What is Times Series Data?

Time series data are variables within your data that have a time component. This means that each value in this attribute has either a date or a time value, sometimes both. Here are some examples of time series data:

• Daily stock price for companies because the price of each stock is linked to a specific day

• Average stock index prices over the past few years because each price is plotted on a specific day

• One visit to the website within a month

• Daily platform registration

• Monthly sales and revenue

When dealing with time series data a common calculation is to calculate the growth or average over time. This means that you will need to hold a future date or past date and associated values.

Two WINDOW functions that allow you to accomplish this are LAG and LEAD, which are very useful for dealing with time-dependent data. The main difference between LAG and LEAD is that LAG gets data from the previous rows, while LEAD is the opposite, it takes data from the next rows.

We can use either of these two functions to compare month to month growth for example. As a data analysis technician, you are likely to work in the era of relational data, and if you know how to use LAG or LEAD effectively, you will be a very productive data scientist.

Data Scientist interview question for window job

Let’s get into the main sql data science query that this window function deals with. You will see window functions that are often part of interview questions but you will see them a lot in your daily work so it is important to know how to use them.

Let’s answer one question of Airbnb called the growth of Airbnb. If you want to follow along, you can do it here.

The question is to estimate the annual growth of Airbnb using the number of registered hosts as a measure of growth. The growth rate is calculated by taking ((number of troops registered this year – number of troops registered last year) / number of troops registered last year) * 100.

Annual output, number of hosts this year, number of hosts last year, and growth rate. Round the growth rate to the nearest percent and follow the resulting increase in order based on the year.

Method Step 1: Count the host for this year

The first step is to calculate the hosts per year so we will need to subtract the year from the date values.

SELECT text(year

FROM keeper_from::date) AS year,

count(id) current_year_host

FROM airbnb_search_details

WHERE host_since IS NOT NULL

GROUP BY text (year

FROM host_since::date)

ANNUAL DETERMINATION

Procedure Step 2: Count the host for the previous year.

This is where you will be using the LAG window function. Here you can see where we have the year, the number of hosts this year, and the number of hosts last year. Use the lag function on last year’s count and take last year’s value and put it in the same row as this year’s count. This way you will have 3 columns in your view — the year, the recipient count for the current year, and the recipient count for the previous year. The LAG function allows you to easily pull up the last year’s army count on your back. This makes it easy for you to implement any metric such as growth rate because you have all the values ​​you need in one SQL row to easily calculate the metric. Here is its code:

SELECT year,

host_current_year,

LAG(current_year_host, 1) UP (ORDER BY year) AS prev_year_host

FROM

(SELECT text(year

FROM keeper_from::date) AS year,

count(id) current_year_host

FROM airbnb_search_details

WHERE host_since IS NOT NULL

GROUP BY text (year

FROM host_since::date)

ANNUAL DETERMINATION) t1) t2

Method 3: Using a growth metric

As mentioned earlier, it is much easier to implement a metric like the one below when all the values ​​are in the same row. That’s why you do LAG work. Execute the growth calculation cycle (((current_year_host – prev_year_host)/(cast(prev_year_host AS number)))*100) estimate_growth

SELECT year,

host_current_year,

host_last_year,

round(((current_year_host – prev_year_host)/(cast(prev_year_host AS number))*100) estimate_growth

FROM

(SELECT a year,

host_current_year,

LAG(current_year_host, 1) UP (ORDER BY year) AS prev_year_host

FROM

(SELECT text(year

FROM keeper_from::date) AS year,

count(id) current_year_host

FROM airbnb_search_details

WHERE host_since IS NOT NULL

GROUP BY text (year

FROM host_since::date)

ANNUAL DETERMINATION) t1) t2

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