Prime SQL Queries for Knowledge Scientists

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I do know the phrase ‘Python’ might be probably the most overused phrase within the context of information science. To some extent, there’s a cause for that. However, on this article, I need to give attention to SQL, which regularly will get neglected when speaking about information science. I emphasize speaking as a result of, in apply, SQL shouldn’t be neglected in any respect. Quite the opposite, it’s one of many holy trinity of the programming languages in information science: SQL, Python, and R.

SQL is made for information querying and manipulation but additionally has respectable information evaluation and reporting capabilities. I’ll present among the foremost SQL ideas you want as a knowledge scientist and a few simple examples from StrataScratch and LeetCode.

Then, I’ll present two frequent enterprise eventualities through which all or most of these SQL ideas have to be utilized.

Primary SQL Ideas for Knowledge Scientists

Right here’s the overview of the ideas I’ll talk about.

1. Querying and Filtering Knowledge

That is the place your sensible work as a knowledge scientist normally begins: querying a database and extracting solely the info you want to your process.

This sometimes entails comparatively easy SELECT statements with the FROM and WHERE clauses. To get the distinctive values, use DISTINCT. If you’ll want to use a number of tables, you additionally add JOINs.

You’ll usually want to make use of ORDER BY to make your dataset extra organized.

Instance of Combining Two Tables: You possibly can be required to record the individuals’ names and the town and state they stay in by becoming a member of two tables and sorting the output by final identify.

SELECT FirstName,
       LastName, 
       Metropolis, 
       State
FROM Particular person p LEFT JOIN Deal with a
ON p.PersonId = a.PersonId
ORDER BY LastName ASC;

2. Working with NULLs

NULLs are values that information scientists are sometimes not detached to – they both need solely NULLs, they need to take away them, or they need to substitute them with one thing else.

You may choose information with or with out NULLs utilizing IS NULL or IS NOT NULL in WHERE.

Changing NULLs with another values is usually finished utilizing conditional expressions:

• NULLIF()
• COALESCE()
• CASE assertion 

Instance of IS NULL: With this question, you could find all the purchasers not referred by the shopper with ID = 2.

SELECT identify 
FROM buyer 
WHERE referee_id IS NULL OR referee_id <> 2;

Instance of COALESCE(): I can rework this instance by saying I need to question all the info but additionally add a column that can present 0% as a number response price as a substitute of NULL.

SELECT *,
       COALESCE(host_response_rate, '0%') AS edited_host_response_rate
FROM airbnb_search_details;

3. Knowledge Kind Conversion 

As a knowledge scientist, you’ll convert information incessantly. Knowledge usually doesn’t come within the desired format, so you will need to adapt it to your wants. That is normally finished utilizing CAST(), however there are additionally some alternate options, relying in your SQL taste.

Instance of Casting Knowledge: This question casts the star information from VARCHAR to INTEGER and removes the values which have non-integer values.

SELECT business_name,
       review_id,
       user_id,
       CAST(stars AS INTEGER) AS cast_stars,
       review_date,
       review_text,
       humorous,
       helpful,
       cool
FROM yelp_reviews
WHERE stars  '?';

4. Knowledge Aggregation

To raised perceive the info they’re working with (or just because they should produce some stories), information scientists fairly often should combination information.

Generally, you will need to use combination capabilities and GROUP BY. Among the frequent combination capabilities are:

• COUNT()
• SUM()
• AVG()
• MIN()
• MAX()

If you wish to filter aggregated information, use HAVING as a substitute of WHERE.

Instance of Sum: You should utilize this question to sum the checking account for every consumer and present solely these with a stability above 1,000.

SELECT u.identify, 
       SUM(t.quantity) AS stability
FROM Customers u
JOIN Transactions t
ON u.account = t.account
GROUP BY u.identify
HAVING SUM(t.quantity) > 10000;

5. Dealing with Dates

Working with dates is commonplace for information scientists. Once more, the dates are solely generally formatted in accordance with your style or wants. To maximise the pliability of dates, you’ll generally must extract elements of dates or reformat them. To try this in PostgreSQL, you’ll mostly use these date/time capabilities:

• EXTRACT()
• DATE_PART()
• DATE_TRUNC()
• TO_CHAR() 

One of many frequent operations with dates is to discover a distinction between the dates or so as to add dates. You try this by merely subtracting or including the 2 values or through the use of the capabilities devoted for that, relying on the database you employ.

Instance of Extracting 12 months: The next question extracts the 12 months from the DATETIME sort column to indicate the variety of violations per 12 months for Roxanne Cafe.

SELECT EXTRACT(YEAR FROM inspection_date) AS year_of_violation,
       COUNT(*) AS n_violations
FROM sf_restaurant_health_violations
WHERE business_name="Roxanne Cafe" AND violation_id IS NOT NULL
GROUP BY year_of_violation
ORDER BY year_of_violation ASC;

Instance of Date Formatting: With the question under, you format the beginning date as ‘YYYY-MM’ utilizing TO_CHAR().

SELECT TO_CHAR(started_at, 'YYYY-MM'),
       COUNT(*) AS n_registrations
FROM noom_signups
GROUP BY 1;

6. Dealing with Textual content

Aside from dates and numerical information, fairly often databases include textual content values. Generally, these values should be cleaned, reformatted, unified, cut up and merged. Resulting from these wants, each database has many textual content capabilities. In PostgreSQL, among the extra in style ones are:

• CONCAT() or ||
• SUBSTRING()
• LENGTH()
• REPLACE()
• TRIM()
• POSITION()
• UPPER() & LOWER()
• REGEXP_REPLACE() & REGEXP_MATCHES() & REGEXP_SPLIT_TO_ARRAY()
• LEFT() & RIGHT()
• LTRIM() & RTRIM()

There are normally some overlapping string capabilities in all databases, however every has some distinct capabilities.

Instance of Discovering the Size of the Textual content: This question makes use of the LENGTH() perform to seek out invalid tweets primarily based on their size.

SELECT tweet_id 
FROM Tweets 
WHERE LENGTH(content material) > 15;

7. Rating Knowledge

Rating information is without doubt one of the widespread duties in information science. As an example, it may be used to seek out the very best or worst-selling merchandise, quarters with the very best income, songs ranked by variety of streams, and the very best and lowest-paid staff.

The rating is finished utilizing window capabilities (which we’ll discuss a bit extra within the subsequent part):

• ROW_NUMBER()
• RANK()
• DENSE_RANK()

Instance of Rating: This question makes use of DENSE_RANK() to rank hosts primarily based on the variety of beds they’ve listed.

SELECT host_id, 
       SUM(n_beds) AS number_of_beds,
       DENSE_RANK() OVER(ORDER BY SUM(n_beds) DESC) AS rank
FROM airbnb_apartments
GROUP BY host_id
ORDER BY number_of_beds DESC;

8. Window Capabilities

Window capabilities in SQL permit you to calculate the rows associated to the present row. This attribute shouldn’t be solely used to rank information. Relying on the window perform class, they’ll have many various makes use of. You may learn extra about them within the window capabilities article. Nevertheless, their foremost attribute is that they’ll present analytical and aggregated information on the identical time. In different phrases, they don’t collapse particular person rows when performing calculations.

Instance of FIRST_VALUE() Window Perform: One window perform instance is to indicate the most recent consumer login for a selected 12 months. The FIRST_VALUE() window perform makes this simpler.

SELECT DISTINCT user_id,
       FIRST_VALUE(time_stamp) OVER (PARTITION BY user_id ORDER BY time_stamp DESC) AS last_stamp
FROM Logins
WHERE EXTRACT(YEAR FROM time_stamp) = 2020;

9. Subqueries & CTEs

Subqueries and CTEs (often called tidier subqueries) permit you to attain a extra superior stage of calculations. By realizing subqueries and CTEs, you’ll be able to write complicated SQL queries, with subqueries or CTEs used for sub-calculations referenced in the principle question.

Instance of Subqueries and CTEs: The question under makes use of the subquery to seek out the primary 12 months of the product sale. This information is then utilized in WHERE for the principle question to filter information.

SELECT product_id, 
       12 months AS first_year, 
       amount, 
       worth 
FROM Gross sales 
WHERE (product_id, 12 months) IN (
    SELECT product_id, 
           MIN(12 months) AS 12 months 
    FROM Gross sales 
    GROUP BY product_id
);

The code might be written utilizing CTE as a substitute of a subquery.

WITH first_year_sales AS (
    SELECT product_id, 
           MIN(12 months) AS first_year 
    FROM Gross sales 
    GROUP BY product_id
)

SELECT s.product_id, 
       s.12 months AS first_year, 
       s.amount, 
       s.worth 
FROM Gross sales s
JOIN first_year_sales AS fys 
ON s.product_id = fys.product_id AND s.12 months = fys.first_year;

Enterprise Examples of Utilizing SQL

Let’s now have a look at a few enterprise circumstances the place information scientists can use SQL and apply all (or most) of the ideas we mentioned earlier.

Discovering Finest Promoting Product

On this instance, you will need to know subqueries, information aggregation, dealing with dates, rating information utilizing window capabilities, and filtering the output.

The subquery calculates every product’s gross sales for every month and ranks them by gross sales. The primary question then merely selects the required columns and leaves solely merchandise with the primary rank, i.e., best-selling merchandise.

SELECT sale_month,
       description,
       total_paid
FROM
  (SELECT DATE_PART('MONTH', invoicedate) AS sale_month,
          description,
          SUM(unitprice * amount) AS total_paid,
          RANK() OVER (PARTITION BY DATE_PART('MONTH', invoicedate) ORDER BY SUM(unitprice * amount) DESC) AS sale_rank
   FROM online_retail
   GROUP BY sale_month,
            description) AS ranking_sales
WHERE sale_rank = 1;

Calculating Transferring Common

The rolling or transferring common is a standard enterprise calculation to which information scientists can apply their intensive SQL data, as in this instance.

The subquery within the code under calculates revenues by month. The primary question then makes use of the AVG() window capabilities to calculate the 3-month rolling common income.

SELECT t.month,
       AVG(t.monthly_revenue) OVER(ORDER BY t.month ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS avg_revenue
FROM
  (SELECT TO_CHAR(created_at::DATE, 'YYYY-MM') AS month,
          SUM(purchase_amt) AS monthly_revenue
   FROM amazon_purchases
   WHERE purchase_amt>0
   GROUP BY 1
   ORDER BY 1) AS t
ORDER BY t.month ASC;

Conclusion

All these SQL queries present you the way to use SQL in your information science duties. Whereas SQL shouldn’t be made for complicated statistical evaluation or machine studying, it’s good for querying, manipulating, aggregating information, and performing calculations.

These instance queries ought to provide help to in your job. When you don’t have a knowledge science job, many of those queries will come up in your SQL interview questions.

Nate Rosidi is a knowledge scientist and in product technique. He is additionally an adjunct professor educating analytics, and is the founding father of StrataScratch, a platform serving to information scientists put together for his or her interviews with actual interview questions from prime firms. Nate writes on the most recent traits within the profession market, provides interview recommendation, shares information science initiatives, and covers the whole lot SQL.