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SQL Interview Success: Unlocking the Top 5 Frequently Asked Queries

 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

Why Learning Python is so useful?

Why Learning Python is so useful?

I have recently started learning Python. During my learning time, my friends have asked since you are interested in analytics why you need to learn Python. I explained the below reasons. This is one of the powerful languages after Java.

Python is similar to many programming languages that people generally know about: 

Python is very similar to JavaScript, Ruby, and PHP in many respects. 

Most programmers have a working knowledge of these programming languages and this makes it easier for programmers to learn Python. The basic features of these languages such as the use of arrays, anonymous functions, etc., are also present in Python. 

1. Python Machine Learning Libraries:

The variety of machine learning libraries that are available in Python is large.

One can choose between Scikitlearn, Keras, Theano, and Tensorflow. Many neural network libraries such as Keras, Theano, etc., are exclusively available in Python. So, if you want to do cutting edge machine learning work, you must know Python.

2. Python Handles Text Data: 

Unlike statistical software environments such as R, Python excels at handling text data. People who know Python can easily mine text corpus for useful insights. 

Python also provides support for Natural Language Processing through NLTK and sPacy
Python makes distributed computing very easy: Apache Spark has a Python API called PySpark. Using this piece of software, one can easily do distributed computing. PySpark has in recent times become the de-facto API for Spark. 

Extensive support for different data sources: It doesn’t matter if one needs to fetch data from an SQL server, a MongoDB database, or JSON data from some web API; Python can easily support all these data sources with a very clean and elegant syntax. 

3. Benefits of Learning Python

  • Learning Python has many advantages – it gives a user many skills, one can fetch data from different sources, create machine learning models, and do distributed computing seamlessly. 

  • For any programmer, learning Python will not be a difficult task. One can reap a lot of benefits by devoting time to learning Python.


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