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 knowledge science. To a point, there’s a cause for that. However, on this article, I need to deal with SQL, which regularly will get neglected when speaking about information science. I emphasize speaking as a result of, in follow, 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 a number of the principal SQL ideas you want as a knowledge scientist and a few simple examples from StrataScratch and LeetCode.

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

Predominant 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 information you want in your process.

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

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

Example of Combining Two Tables: You may be required to listing the individuals’ names and the town and state they stay in by becoming a member of two tables and sorting the output by final title.

SELECT FirstName,
       LastName, 
       Metropolis, 
       State
FROM Particular person p LEFT JOIN Tackle 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’ll be able to choose information with or with out NULLs utilizing IS NULL or IS NOT NULL in WHERE.

Changing NULLs with another values is often performed utilizing conditional expressions:

• NULLIF()
• COALESCE()
• CASE assertion 

Example of IS NULL: With this question, you will discover all the purchasers not referred by the client with ID = 2.

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

Example of COALESCE(): I can rework this instance by saying I need to question all the information but additionally add a column that may present 0% as a bunch response price as an alternative 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 steadily. Knowledge typically doesn’t come within the desired format, so you should adapt it to your wants. That is normally performed utilizing CAST(), however there are additionally some options, relying in your SQL taste.

Example of Casting Data: 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 higher perceive the information they’re working with (or just because they should produce some reviews), information scientists fairly often need to combination information.

Generally, you should use aggregate functions and GROUP BY. Among the widespread combination capabilities are:

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

If you wish to filter aggregated information, use HAVING as an alternative of WHERE.

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

SELECT u.title, 
       SUM(t.quantity) AS stability
FROM Customers u
JOIN Transactions t
ON u.account = t.account
GROUP BY u.title
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 response to your style or wants. To maximise the flexibleness of dates, you’ll generally must extract components of dates or reformat them. To do this in PostgreSQL, you’ll mostly use these date/time functions:

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

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

Example of Extracting Year: The next question extracts the 12 months from the DATETIME kind 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;

Example 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

Other than dates and numerical information, fairly often databases include textual content values. Generally, these values need to be cleaned, reformatted, unified, cut up and merged. Resulting from these wants, each database has many textual content capabilities. In PostgreSQL, a number of the extra common 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.

Example of Finding the Length of the Text: This question makes use of the LENGTH() operate to search out invalid tweets primarily based on their size.

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

7. Rating Knowledge

Rating information is among the widespread duties in information science. For example, it may be used to search out the most effective or worst-selling merchandise, quarters with the best income, songs ranked by variety of streams, and the best and lowest-paid workers.

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

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

Example of Ranking: 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 operate class, they will have many alternative makes use of. You’ll be able to learn extra about them within the window functions article. Nevertheless, their principal attribute is that they will present analytical and aggregated information on the similar time. In different phrases, they don’t collapse particular person rows when performing calculations.

Example of FIRST_VALUE() Window Function: One window operate instance is to indicate the newest consumer login for a selected 12 months. The FIRST_VALUE() window operate 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 complex SQL queries, with subqueries or CTEs used for sub-calculations referenced in the principle question.

Example of Subqueries and CTEs: The question under makes use of the subquery to search 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 will be written utilizing CTE as an alternative 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

In this example, you should 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 Shifting Common

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

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 easy methods to use SQL in your information science duties. Whereas SQL shouldn’t be made for advanced statistical evaluation or machine studying, it’s excellent for querying, manipulating, aggregating information, and performing calculations.

These instance queries ought to show you how 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 instructing 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 high firms. Nate writes on the newest tendencies within the profession market, provides interview recommendation, shares information science tasks, and covers the whole lot SQL.