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SQL Interview Success: Unlocking the Top 5 Frequently Asked Queries

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 Here are the five top commonly asked SQL queries in the interviews. These you can expect in Data Analyst, or, Data Engineer interviews. Top SQL Queries for Interviews 01. Joins The commonly asked question pertains to providing two tables, determining the number of rows that will return on various join types, and the resultant. Table1 -------- id ---- 1 1 2 3 Table2 -------- id ---- 1 3 1 NULL Output ------- Inner join --------------- 5 rows will return The result will be: =============== 1  1 1   1 1   1 1    1 3    3 02. Substring and Concat Here, we need to write an SQL query to make the upper case of the first letter and the small case of the remaining letter. Table1 ------ ename ===== raJu venKat kRIshna Solution: ========== SELECT CONCAT(UPPER(SUBSTRING(name, 1, 1)), LOWER(SUBSTRING(name, 2))) AS capitalized_name FROM Table1; 03. Case statement SQL Query ========= SELECT Code1, Code2,      CASE         WHEN Code1 = 'A' AND Code2 = 'AA' THEN "A" | "A
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How to Identify Data Relevant for Data Science Analytics

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Your government, your web server, your business partners, even your body. While we aren’t drowning in a sea of data, we’re finding that almost everything can (or has) been instrumented. We frequently combine publishing industry data from Nielsen Book Scan with our own sales data, publicly available Amazon data, and even job data to see what’s happening in the publishing industry.

Data is everywhere
Sites like Infochimps and Factual provide access to many large datasets, including climate data, MySpace activity streams, and game logs from sporting events. Factual enlists users to update and improve its datasets, which cover topics as diverse as endocrinologists to hiking trails.

How the data is growing

Much of the data we currently work with is the direct consequence of Web 2.0, and of Moore’s Law applied to data. The Web has people spending more time online and leaving a trail of data wherever they go. Mobile applications leave an even richer data trail since many of them are annotated with geolocation, or involve video or audio, all of which can be mined.

Point-of-sale devices and frequent shoppers cards make it possible to capture all of your retail transactions, not just the ones you make online. All of this data would be useless if we couldn’t store it, and that’s where Moore’s Law comes in. Since the early ’80s, processor speed has increased from 10 MHz to 3.6 GHz—an increase of 360 (not counting increases in word length and number of cores).

The need for Storage capacity

But we’ve seen much bigger increases in storage capacity, on every level. RAM has moved from $1,000/MB to roughly $25/GB—a price reduction of about 40000, to say nothing of the reduction in size and increase in speed. Hitachi made the first-gigabyte disk drives in 1982, weighing in at roughly 250 pounds; now terabyte drives are consumer equipment, and a 32 GB microSD card weighs about half a gram. Whether you look at bits per gram, bits per dollar, or raw capacity, storage has more than kept pace with the increase of CPU speed.

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