NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Student Works > Introduction To SQL: Part I

Introduction To SQL: Part I

Daniel Brancusi
Posted on Sep 30, 2020
Database Computing Representing Information Storage 3d Rendering

What is SQL?

"SQL (pronounced "ess-que-el") stands for Structured Query Language. SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems."  ANSI will come into play later as there are often many ways to execute the same task in SQL.  Make sure to check with your employer if they maintain ANSI standards in their code.   "SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database."  There are several different companies that operate database management systems with SQL, including Microsoft and Oracle.  While SQL standard commands are largely transferable, most systems also have unique property extensions.   There are also NoSQL databases, however we will not be covering these.  "NoSQL databases are increasingly used in big data and real-time web applications."  Some notable NoSQL databases include MongoDB, Oracle NoSQL Database, and (the best named) Voldemort.

SQL is a Relational Database Management System - RDMS for short.  The data held within a RDBMS is stored in tables like the one below.

A table from SQLDeveloper

All SQL tables are composed of fields.  A field is simply a column within a table.  As an example, in the table above, the fields are MAKE, MODEL, MODEL_YEAR, MILAGE, etc.  Each field contains information for every record in the table and each record has its own row in the table (for example, there are 30 records in the table above).  While small tables such as the one above are relatively simple to interpret with the eye, tables can have millions of entries. Therefore, we need an efficient way to retrieve the information we're looking for.  

Select, From, Where...

Three of the most widely used keywords in SQL are SELECT, FROM and WHERE (note: you do not have to capitalize these words but I do).   

SELECT

The SELECT statement tells SQL what fields (or columns) you would like to use in the query.  Within the statement, aggregation functions such as COUNT, MAX, MIN and AVG can also be used (these will be covered in a later post).  Finally, the * operator can be used to represent all.  As an example, in our table above we could write a statement to select all fields in two ways:

-- OPTION 1: LIST ALL FIELDS
SELECT 
    MAKE, 
    MODEL, 
    MODEL_YEAR, 
    MILEAGE, 
    COLOR, 
    PRICE, 
    CONDITION_CD 

-- OPTION 2: USE THE * OPERATOR
SELECT *

As we continue forward, the use of the * operator will become more natural.  Also, new lines are not required for each field, however for readability I often will put each field on its own line.  This is especially true with a long list so referencing back becomes significantly easier.

FROM

The FROM statement lets SQL know which table(s) should be referenced in the query.  It may seem logical that FROM should begin the query - and you would be largely correct!  When an SQL query is run, the FROM command is the first item executed (along with JOIN, which we'll get into in the next post).  Nevertheless, for writing SQL queries we keep the structure of SELECT followed by FROM.  

Within the context of our query, FROM specifies the table(s) that should be referenced. Using our LOT_CARS table from earlier:

-- EXAMPLE 1: USING ONE TABLE

SELECT 
  MAKE, 
  MODEL_YEAR, 
  CONDITION_CD
FROM LOT_CARS
;

-- EXAMPLE 2: USING MULTIPLE TABLES

SELECT 
  LC.MAKE, 
  LC.MODEL_YEAR, 
  LC.CONDITION_CD,
  CT.DESCRIPTION
FROM LOT_CARS AS LC, 
     CONDITION_TBL AS CT
;

note: notice the semicolon after each statement.  "The semicolon character is a statement terminator. It is a part of the ANSI SQL-92 standard"

While the first example above should be readily understood, we'll go into some details about the second example.  First, let's explain the AS next to LOT_CARS and CONDITION_TBL.  The AS allows us to alias each table however we wish (there are some rare exceptions).  We can also use AS to alias how columns are labeled when our query returns a result.  There is no actual need to use the AS operator (we can simply place a space between the table name and the alias).  However, including the AS operator enhances readability.  Finally, we have added LC. and CT. to our selected fields.  Because we are referencing two different tables, we need to specify which table we would like to use if more than one of the selected tables contain a field with the same value as selected in our query.  When field names are unique, the prefix is not needed. 

We can also return a result utilizing only SELECT and FROM:

/* RETURNS THE MAKE AND MODEL_YEAR FIELDS FROM THE LOT CARS TABLE */

SELECT 
  MAKE, 
  MODEL_YEAR
FROM LOT_CARS
;

note:  to comment out a line in SQL we use two dashes and to comment out multiple lines we begin with /* and end with */

WHERE

While a table can be returned using only SELECT and FROM, the WHERE clause adds an immense amount of power to queries by filtering the returned results to only those that meet the condition of the WHERE clause. There are many conditional statements in SQL that can be used within a WHERE clause (as well as throughout a query) and will be familiar to someone with even minimal programming experience.   

OPERATORDESCRIPTIONEXAMPLE
=Checks if operands are equalMAKE = "Ford"
!=Checks if operands are not equalMODEL != "Civic"
<>Checks if the two operands are equal or not. If values are not equal then the result is true. This is the same as != but is "correct" under ANSI standardsMODEL <> "Civic"
>Checks if the left operand is greater than the right operand5 > 4 (True)
<Checks if the left operand is smaller than the right operand5 < 4 (False)
!<Checks if the left operand is not smaller than the right operand5 !< 4 (True)
!>Checks if the left operand is smaller than the right operand5 !> 4 (False)
>=Checks if the left operand is greater than or equal to the right operand5 >= 4 (False)

SQL also has many logical operators.  Some of the most important are listed below.

OPERATORDESCRIPTION
ALLreturns true if ALL of the subquery values meet given condition
ANYreturns true if ANY of the subquery values meet given condition
ANDThe AND operator allows the existence of multiple conditions in an SQL statement's WHERE clause
BETWEENThe BETWEEN operator is used to search for values that are within a given minimum and maximum value
EXISTSThe EXISTS operator is used to search for the presence of a specified record
INThe IN operator is used to determine if a record's value is contained in a specified list
LIKEThe LIKE operator is used to search for a specified pattern
NOTThe NOT operator reverses the meaning of the logical operator it preceeds
ORThe OR operator is used to combine multiple conditions in an SQL statement's WHERE clause.
UNIQUEThe UNIQUE operator returns distinct occurrences within a specified field

We are now ready to use what we've learned to write queries in SQL.  Let's add in a simple where clause to our previous example: 

--EXAMPLE 1
SELECT 
  MAKE, 
  MODEL_YEAR, 
  CONDITION_CD
FROM LOT_CARS
WHERE MAKE = "Ford"
;

--EXAMPLE 2
SELECT 
    MAKE, 
    MODEL_YEAR, 
    CONDITION_CD
FROM LOT_CARS
WHERE 
    CONDITION_CD BETWEEN 1 AND 3 
    AND MODEL_YEAR <> 2017
;

You can now go out and write your own queries in SQL!  Next time we'll discuss joins and their use.  If you have any questions leave a comment below!

About Author

Daniel Brancusi

View all posts by Daniel Brancusi >

Leave a Comment

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application