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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

A Quick Refresher on All the Commonly used SQL Commands!

a
aparna Last Updated : 30 May, 2022
4 min read
This article was published as a part of the Data Science Blogathon.

SQL Commands
Source: Freepik

Introduction

SQL(Structured Query Language) commands are instructions. It is used to interact with the database, and it is also used to perform specific tasks, functions, and data queries. SQL can perform Several tasks like creating a table, adding data to tables, dropping the table, modifying the table, set permission for users. Here, I explain different SQL commands and respective syntax so that this quick read can be your refresher.

I begin with a broad classification of SQL commands.. 🚀

Types of SQL Commands

Depending on which operation the SQL code will be doing, there are five different types of SQL commands, as shown below.

1. DDL: Data Definition Language

2. DML: Data Manipulation Language

3. DCL: Data Control Language

4. TCL: Transaction Control Language

5. DQL: Data Query Language

SQL Commands

I explain these types and the SQL commands used in the subsequent sections below.

Data Definition Language (DDL)

DDL changes the format of the table, such as creating a table, deleting a table, and altering a table.

All the command of DDL is auto-committed which means it permanently save all the changes in the database. Below are the DDL commands and their syntax.

CREATE TABLE

CREATE TABLE Creates a new table in the database. It allows you to specify the name of the table and the name of each column in the table.

CREATE TABLE table_name (column_1 datatype,
                         column_2 datatype,
                         column_3 datatype);

ALTER

ALTER TABLE Let’s you alter the table as per your requirement.

For example, you can add, remove and rename the columns as shown below.

-- Add a column
ALTER TABLE table_name
ADD column_name datatype;
-- Remove a column
ALTER TABLE table_name
DROP COLUMN column_name;
-- Rename a column
ALTER TABLE table_name
CHANGE COLUMN old_name new_name;

Moreover, ALTER lets you change the table name itself with the below syntax.

ALTER TABLE old_table_name
RENAME TO new_table_name;

DROP

It is used to delete both the structure and record stored in the table.

DROP TABLE table_name;

TRUNCATE

It is used to delete all the rows from the table and free the space containing the table.

TRUNCATE TABLE table_name;

Data Manipulation Language

DML commands are used to Customize the database, and it is responsible for all forms of changes in the database.

The command of DML is not auto-committed, which means it can’t permanently save all the changes in the database. They can be rollback. Below are the DML commands and their syntax.

INSERT

INSERT Statements are used to add a new row to a table.

INSERT INTO table_name 
(column_1, column_2, column_3)
VALUES
(value_1, value_2, value_3);

UPDATE

UPDATE statements allow you to edit rows in a table.

UPDATE table_name 
SET column_name = value
WHERE condition;

DELETE

DELETE statements are used to remove rows from a table.

DELETE FROM table_name
WHERE some_column = some_value;

Data Control Language

DCL commands grants and takes back authority from any database user. It works as shown below.

GRANT

GRANT command is for giving users access to a database.

GRANT SELECT, UPDATE ON My_TABLE TO FIRST_USER, SECOND_USER;

REVOKE

It is used to take back permissions from the user.

REVOKE SELECT, UPDATE ON My_TABLE TO FIRST_USER, SECOND_USER;

Transaction Control Language

Transaction Control Language — as its name suggests — is used to control the actions done by other non-auto-committed commands such as INSERT, DELETE and UPDATE.

As I mentioned, DML commands are not auto-committed so TCL commands can be used with the DML. Below are the commonly used TCL statements.

COMMIT

This is used for saving every transaction to the database.

DELETE FROM VENDOR
WHERE AGE = 29;   
COMMIT;

ROLLBACK

This command aims to undo the transactions that are not saved to the database.

DELETE FROM VENDOR
WHERE AGE = 29;   
ROLLBACK;

SAVEPOINT

This is used for returning a transaction to a specific point without affecting the whole transaction.

SAVEPOINT SAVEPOINT_NAME;

Data Query Language

DQL is used to fetch the data from the database. SELECT is the only and essential command widely and commonly used by all data analysts and scientists.

SELECT

The prime purpose of this statement is to get data from a database. Every query will begin with SELECT, followed by the names of columns you want to get from the table.

A particular column from the table can be selected with,

SELECT column_name FROM table_name;

And all the columns can be determined by using * as shown below,

SELECT * FROM table_name;

Conclusion

This article has taught you about the various SQL commands and simple SQL queries with examples. Some of the main concepts learnt in this article are as follows:

Data Definition Language(DDL) helps you define the database structure or schema.
Data Manipulation Language (DML) allows you to modify the database instance by inserting, changing, and deleting its data.
DCL (Data Control Language) includes commands like GRANT and REVOKE, which help give “rights & permissions.”
Transaction control language or TCL commands deal with the transaction within the database.
Data Query Language (DQL) is used to fetch the data from the database.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

a
aparna

My name is Aparna Gurav, ServiceNow Developer Engineer/Platform Engineer/ at Sportradar Skilled in JavaScript/ SQL /SAP/ ITSM, CMDB,/Advanced Excel/Microsoft Power BI/System Admin Currently staying in Munich Germany. I like to learn New Things, Reading books and write blogs and Articles.

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