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Introduction to MariaDB ODBC

MariaDB ODBC is the database driver that uses the ODBC API, that is an Application Programming Interface internally to establish a connection with a database. ODBC driver is open-source and licensed under LGPL, which is one of the standards-based by industries. This ODBC driver can also be used as a replacement to MySQL ODBC provided if the version of MariaDB ODMC driver is 3.5. In this article, we will learn about what ODBC is, and how we can install, do the setup and use MariaDB ODBC, ODBC connector, and network connections required.

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What is MariaDB ODBC?

MariaDB ODBC is the driver that helps connect databases from object-oriented and object-based programming languages. MariaDB ODBC supports ANSI modes as well as Unicode. It is full of features enterprise-level and has very high performance. We can connect to databases such as MariaDB on platforms including Windows and Unix. We can connect from applications that use Business Intelligence, ETL, other customized apps, and reporting tools to MariaDB. We can easily update, modify, and read and write virtually from anywhere. Its main features of it are listed below –

It provides support for complex queries involving joins and aggregation of data.

In real-time scenarios, connecting to the live MariaDB database is provided.

Integrations with other third-party applications, including custom applications, Business Intelligence, ETL, and reporting tools, are available.

Modern cryptography technologies such as SHA-256, TLS 1.2, and ECC are connected securely with ODBC.

Support for all the Unicode of any given language is provided.

MariaDB ODBC is a cross-platform technology that means that it can work on Windows, Mac operating systems, and Linux platforms.

ODBC supports 32-bit and 64-bit applications.

The ODBC driver is based on ODBC 3.8 native protocol.

ANSI SQL – 92 support is provided.

MariaDB ODBC setups proper installer

You can now choose the package you want to install, which can be complete, custom, or typical, as shown below –

You can see the status and progress of the installation process. This step of setup might take a certain time that varies from system to system –

Installation of Linux platform How to use mariadb odbc?

When you go for establishing the connection between MariaDB database and application by using the default command used as shown below –

MySQL

After typing the above command, the default parameters taken from the configuration file are as follows –

Host is localhost

User is the login name of ODBC on the windows platform or login name of Unix.

Password is not passed as a parameter.

For example, when we use the below command –

MySQL -h 235.26.8.108 -u Payal -p password educbaDatabase -port 3000

The output of the execution of the above command is as shown below, where the connection has been established with the MariaDB database server configured at 3000 port number –

ODBC Connector – ODBC connector for MariaDB is the database driver that is completely based on the standards of the industry here. Open database connectivity which stands for ODBC Application programming interface (API), is used. This connector can be used as a replacement for the MySQL connector for ODBC. It provides complete support for Unicode and ANSI modes. Furthermore, it makes use of MySQL binary protocol or MariaDB binary protocol. The recent release of the stable version of the ODBC connector is 3.1.13.

Network Connections

SSL Protocol with Transport Layer Security (TLS) options and parameters are passed. The most common connection parameters include Password, user, server, database, port, host, and other options. For a complete reference, go to this link.

Conclusion

MariaDB ODBC is the driver library package used for establishing the connection between the MariaDB database and applications that are object-oriented or object-based. You can easily install and use it on Windows, Linux, and Mac OS platforms.

Complete Guide On Proportional Tax

Definition of Proportional Tax

Proportional tax, sometimes called flat tax, is a tax levied on the consumer or the taxpayer such that the tax rate is constant on the income earned irrespective of changes to the taxable income.

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Explanation

The guiding principle behind the proportional tax is the notion of socio-economic equality across the income strata in society by making a change in tax rates absent. This means that all the income groups, from lower to higher and very high, pay the same taxes.

Example of Proportional Tax

Proportional taxes find real-world application in a few parts of the globe. Particularly in the United States, a proportional tax is imposed in Colorado, Pennsylvania, Michigan, and Massachusetts. The United States has, in general, a progressive form of taxation, which has been opposed by many analysts and thinkers, citing reasons for the unjust and inequitable imposition of taxes.

Assume the following 3 tax systems and some cooked-up numbers for the illustration purposes

Income Tax Rates Under

Regressive Tax

Proportional Tax

Progressive Tax

Income Interval (annual)

20% 20% 10% $80,000 – $100,000

20% 20% 20% $100,001 – $120,000

18% 20% 25% $120,001 – $140,000

15% 20% 30% $140,001 – $160,000

Now just see the income taxes paid under these 3 systems,

Income Taxes Paid Under

Regressive Tax

Proportional Tax

Progressive Tax

Income Interval (annual)

$16,000 – $20,000 $16,000 – $20,000 $8,000 – $10,000 $80,000 – $100,000

$20,000 – $24,000 $16,000 – $20,000 $20,000 – $24,000 $100,001 – $120,000

$21,600 – $25,200 $16,000 – $20,000 $30,000 – $35,000 $120,001 – $140,000

$21,000 – $24,000 $16,000 – $20,000 $42,000 – $48,000 $140,001 – $160,000

Observe that Proportional taxes charge the same amount while Regressive taxes charge lower taxes for higher incomes, and Progressive taxes charge higher taxes for higher incomes.

Proportional Tax Rate

Simply put, a proportional tax is a flat tax where everyone pays the same taxes to the tax collector. In contrast, there is 2 other taxation methods, majorly viz. progressive and regressive tax. In the former, the tax rate increases with taxable income. In the latter, the tax rate decreases with the increase in taxable income.

Considering the two extremes of tax classification, we can say that a proportional tax system has a regressive outlook. This is because a flat tax nature of the proportional tax system charges the poor and the rich equally, thus burdening the poor more.

Two important terms to note are the average and marginal tax rates.

The average tax rate is the ratio of total taxes paid to the total income earned.

Average Tax Rate = Taxes Paid / Income

Marginal Tax Rate = Incremental Taxes/ Incremental Income

The following example will illustrate the difference between the two:

If John earns $100,000 and pays $18,000 in taxes, the average tax rate is 18%. If John earns an incremental income of $5,000 and is subject to $1,500 in incremental taxes, the marginal tax rate is 30%.

In essence, the marginal tax rate recognizes how the tax rate impacts the taxpayer to earn incentives (by working more for more income). The average tax rate only gives a measure of how the tax rate has impacted the taxpayer. Applying a marginal tax rate is useless in the proportional tax system as it violates the rule of maintenance of equal tax incidence.

Proportional Tax Graph

Let us take a small economy with 9 income groups with different incomes each. Below is a graph representing the taxation imposed on this economy and trying to justify how the tax collection is skewed.

Tax Rate

Taxable Income

Taxes Paid

Income After Tax

20% $40,000 $8,000 $32,000

20% $50,000 $10,000 $40,000

20% $60,000 $12,000 $48,000

20% $70,000 $14,000 $56,000

20% $80,000 $16,000 $64,000

20% $90,000 $18,000 $72,000

20% $100,000 $20,000 $80,000

20% $110,000 $22,000 $88,000

20% $120,000 $24,000 $96,000

Understanding the Graph

The steep increase in the taxable income suggests how the income spread is within the economy. The relatively gradual increase in taxes suggests that tax collection is not as high in the higher-income groups as in the lower-income groups. Remember that the fraction of tax collection is the same at 20% for groups 1 to 9. The amount of taxes collected in absolute terms matters for the tax collector.

If the tax collection needs to be improved, a tax rate based on earnings potential can be imposed so that high-income groups contribute more in absolute terms.

Advantages

The proportional tax brings the concept of equality into the socio-economic fabric of society.

A proportional tax is simple to implement, manage and revoke if need be

As opposed to the notion of equality, the proportional tax system does not take equitable distribution. By imposing a flat tax, the low-income group pays more taxes in absolute terms than the high-income group.

More often than not, it does not motivate or incentivize the taxpayer or non-taxpaying population of the society to work aggressively to earn more.

Proportional tax widens the bridge between the rich and the poor as flat taxes take more from the poor and less from the rich.

Conclusion

A proportional tax system essentially follows the rationale of vertical segmentation of the society where all economic classes are subject to the same taxes. In contrast, a horizontal segmentation classifies economic classes based on the economic ability to pay the tax. Thus, most tax systems use a hybrid form where the lower strata are taxed on the ability-to-pay principle while the higher strata are subject to a flat (proportional) tax but above a certain income.

Taxation is a significant tool that helps the world’s governments generate revenue to expend on welfare works, much-needed projects, eliminating poverty, paying its people, etc. However, proportional tax may endanger these goals of the governments, considering the lower taxes that, the richer societies are subject to because of the flat tax. Moreover, a flat tax raises questions that relate to equal inflation and price rise for all income groups, for which activists argue that it becomes unjust to pay a flat tax across different income groups.

How To Refund Dlc On Steam? – Complete Guide

If you have purchased Steam games accidentally or the game does not meet your system requirements, you can apply for a refund.

But how to refund DLC on Steam or what are the recent refund policies?

Steam is a highly popular store and well maintains the standard of selling and refunds.

Today I am going to discuss the technical know-how of how to refund DLC on Steam.

Also read: How To Share Steam Game Libraries

There are some prerequisites that you must take care of before knowing how to refund DLC on steam.

Like the hours you have played the game, or the number of days between the refund request and the purchase.

You must also be particular about the reasons you are starting to apply for the DLC return.

Now let us check the points in detail. Have a look!

Now let us consider how to refund DLC on Steam. The process is simple and a bit straight forward.

Follow the basic steps mentioned below to execute the task.

Step 1: First of all, visit the Steam Support website and then log in with your credentials.

You will get redirected to the support page and here you can easily find out the list of all your purchases.

Step 2: Now select the game you want to return.

Step 2: Select the issue of the purchase.

Step 3: Give a specific reason to support your cancellation process.

You will get the common reasons in the Reason For Refund category.

Step 4: Now you will get a new page and choose the I’d like to request a refund option.

Remember that the game you want to refund is purchased within 14 days and not played for more than 2 hours. Otherwise, it will not be eligible for a refund.

Step 5: Now choose the method of refund.

You may go for Credit or Debit card options or your Steam wallet as the mode of payment.

You will get the amount transferred within 7 days after the approval of your request.

You will get a confirmation email from the Steam Community. So, this is how to apply for a Steam refund.

There are plenty of reasons to claim a refund on Steam. Steam being generous offers easy refunds to the buyers.

Here are some basic reasons on why anyone may want to know how to refund DLC on Steam.

You may have accidentally purchased the game, or it is not compatible with your system.

You played the game and lost interest.

The bonus content or DLC gets somehow missing or the game is having some gameplay problems.

The Retail CD Key is having some problem or there may be some other technical issue.

You cannot find the game in your library or you wish to remove the game from your account permanently.

So, there are plenty of reasons for which you might be returned to the game.

Besides all these issues you may also mention your own and apply for a refund.

Now you might be thinking how long does it take to get a refund from Steam?

So, to clear your doubt I must mention that Steam will consider the request within 2 weeks of purchase and make sure that the DLC is not played for more than two hours.

When you have met all the criteria, you can finish your refunding request process as I have mentioned above.

Steam will let you know through your email about the refund within 7 days.

Steam will consider the payment method you have chosen.

In case you are unable to get the confirmation email, you may directly email Steam and ask for the refund stating the valid reason.

Steam is always going to give you some positive feedback ASAP.

Sometimes you may purchase a title before its official release, and this is referred to as the pre purchase. But you must be aware of the Steam refund time limit.

You can easily claim its refund from Steam. You are eligible to ask for a refund before the game gets officially released.

The 2 weeks and 2 hours refunding policy gets applicable from the date of release of the game.

You may claim a refund for the in-game purchases but with some limitations.

You will have to claim for a refund within 2 days that is 48 hours from the time of purchase.

Make sure that the purchase is by no means transferred, consumed, or modified.

Some third-party developers also provide return and you must go through the description carefully at the time of purchase.

You may be thinking about how to apply for a steam refund if the request gets cancelled?

Here is an easy solution that you apply for the refund once again in case you receive an update that your request got cancelled somehow.

Sometimes you may be buying games in bulk or pre-purchasing a game.

You always have options to apply for your money back in case you are not happy with your purchase.

Hope the technical details of how to refund DLC on Steam services helped you to serve your purpose.

Feel free to shoot us a mail with your ideas or suggestions. We always look forward to hearing back from you.

Complete Guide To Regularization Techniques In Machine Learning

This article was published as a part of the Data Science Blogathon

Introduction

One of the most common problems every Data Science practitioner faces is Overfitting. Have you tackled the situation where your machine learning model performed exceptionally well on the train data but was not able to predict on the unseen data or you were on the top of the competition in the public leaderboard, but your ranking drops by hundreds of places in the final rankings?

Well – this is the article for you!

Avoiding overfitting can single-handedly improve our model’s performance.

In this article, we will understand how regularization helps in overcoming the problem of overfitting and also increases the model interpretability.

This article is written under the assumption that you have a basic understanding of Regression models including Simple and Multiple linear regression, etc.

Table of Contents

👉 Why Regularization?

👉 What is Regularization?

👉 How does Regularization work?

👉 Techniques of Regularization

Ridge Regression

Lasso Regression

👉 Key differences between Ridge and Lasso Regression

👉 Mathematical Formulation of Regularization Techniques

👉 What does Regularization Achieve?

Why Regularization?

Sometimes what happens is that our Machine learning model performs well on the training data but does not perform well on the unseen or test data. It means the model is not able to predict the output or target column for the unseen data by introducing noise in the output, and hence the model is called an overfitted model.

Let’s understand the meaning of “Noise” in a brief manner:

By noise we mean those data points in the dataset which don’t really represent the true properties of your data, but only due to a random chance.

So, to deal with the problem of overfitting we take the help of regularization techniques.

What is Regularization?

👉 It is one of the most important concepts of machine learning. This technique prevents the model from overfitting by adding extra information to it.

👉 It is a form of regression that shrinks the coefficient estimates towards zero. In other words, this technique forces us not to learn a more complex or flexible model, to avoid the problem of overfitting.

👉 Now, let’s understand the “How flexibility of a model is represented?”

For regression problems, the increase in flexibility of a model is represented by an increase in its coefficients, which are calculated from the regression line.

👉 In simple words, “In the Regularization technique, we reduce the magnitude of the independent variables by keeping the same number of variables”. It maintains accuracy as well as a generalization of the model.

How does Regularization Work?

Regularization works by adding a penalty or complexity term or shrinkage term with Residual Sum of Squares (RSS) to the complex model.

Let’s consider the Simple linear regression equation:

Here Y represents the dependent feature or response which is the learned relation. Then,

Y is approximated to β0 + β1X1 + β2X2 + …+ βpXp

Here, X1, X2, …Xp are the independent features or predictors for Y, and

β0, β1,…..βn represents the coefficients estimates for different variables or predictors(X), which describes the weights or magnitude attached to the features, respectively.

In simple linear regression, our optimization function or loss function is known as the residual sum of squares (RSS).

We choose those set of coefficients, such that the following loss function is minimized:

Fig. Cost Function For Simple Linear Regression

Image Source: link

Now, this will adjust the coefficient estimates based on the training data. If there is noise present in the training data, then the estimated coefficients won’t generalize well and are not able to predict the future data.

This is where regularization comes into the picture, which shrinks or regularizes these learned estimates towards zero, by adding a loss function with optimizing parameters to make a model that can predict the accurate value of Y.

Techniques of Regularization

Mainly, there are two types of regularization techniques, which are given below:

Ridge Regression

Lasso Regression

Ridge Regression

👉 Ridge regression is one of the types of linear regression in which we introduce a small amount of bias, known as Ridge regression penalty so that we can get better long-term predictions.

👉 In Statistics, it is known as the L-2 norm.

👉 In this technique, the cost function is altered by adding the penalty term (shrinkage term), which multiplies the lambda with the squared weight of each individual feature. Therefore, the optimization function(cost function) becomes:

Fig. Cost Function for Ridge Regression

Image Source: link

In the above equation, the penalty term regularizes the coefficients of the model, and hence ridge regression reduces the magnitudes of the coefficients that help to decrease the complexity of the model.

👉 Usage of Ridge Regression:

When we have the independent variables which are having high collinearity (problem of multicollinearity) between them, at that time general linear or polynomial regression will fail so to solve such problems, Ridge regression can be used.

If we have more parameters than the samples, then Ridge regression helps to solve the problems.

👉 Limitation of Ridge Regression:

Lasso Regression

👉 Lasso regression is another variant of the regularization technique used to reduce the complexity of the model. It stands for Least Absolute and Selection Operator.

👉 It is similar to the Ridge Regression except that the penalty term includes the absolute weights instead of a square of weights. Therefore, the optimization function becomes:

Fig. Cost Function for Lasso Regression

Image Source: link

👉 In statistics, it is known as the L-1 norm.

👉 In this technique, the L1 penalty has the eﬀect of forcing some of the coeﬃcient estimates to be exactly equal to zero which means there is a complete removal of some of the features for model evaluation when the tuning parameter λ is suﬃciently large. Therefore, the lasso method also performs Feature selection and is said to yield sparse models.

👉 Limitation of Lasso Regression:

Problems with some types of Dataset: If the number of predictors is greater than the number of data points, Lasso will pick at most n predictors as non-zero, even if all predictors are relevant.

Multicollinearity Problem: If there are two or more highly collinear variables then LASSO regression selects one of them randomly which is not good for the interpretation of our model.

Key Differences between Ridge and Lasso Regression

👉 Ridge regression helps us to reduce only the overfitting in the model while keeping all the features present in the model. It reduces the complexity of the model by shrinking the coefficients whereas Lasso regression helps in reducing the problem of overfitting in the model as well as automatic feature selection.

👉 Lasso Regression tends to make coefficients to absolute zero whereas Ridge regression never sets the value of coefficient to absolute zero.

Mathematical Formulation of Regularization Techniques

👉 Now, we are trying to formulate these techniques in mathematical terms. So, these techniques can be understood as solving an equation,

For ridge regression, the total sum of squares of coefficients is less than or equal to s and for Lasso regression, the total sum of modulus of coefficients is less than or equal to s.

Here, s is a constant which exists for each value of the shrinkage factor λ.

These equations are also known as constraint functions.

👉 Let’s take an example to understand the mathematical formulation clearly,

For Example, Consider there are 2 parameters for a given problem

Ridge regression:

According to the above mathematical formulation, the ridge regression is described by β1² + β2² ≤ s.

This implies that ridge regression coefficients have the smallest RSS (loss function) for all points that lie within the circle given by β1² + β2² ≤ s.

Lasso Regression:

The image below describes these equations:

Image Source: link

Description About Image: The given image shows the constraint functions(in green areas), for lasso(in left) and ridge regression(in right), along with contours for RSS(red ellipse).

Points on the ellipse describe the value of Residual Sum of Squares (RSS) which is calculated for simple linear regression.

👉 For a very large value of s, the green regions will include the center of the ellipse with itself, which makes the coefficient estimates of both regression techniques equal to the least-squares estimates of simple linear regression. But, the given image shown does not describe this case. In that case, coefficient estimates of lasso and ridge regression are given by the ﬁrst point at which an ellipse interacts with the constraint region.

Ridge Regression: Since ridge regression has a circular type constraint region, having no sharp points, so the intersection with the ellipse will not generally occur on the axes, therefore, the ridge regression coeﬃcient estimates will be exclusively non-zero.

Lasso Regression: Lasso regression has a diamond type constraint region that has corners at each of the axes, so the ellipse will often intersect the constraint region at axes. When this happens, one of the coeﬃcients (from collinear variables) will be zero and for higher dimensions having parameters greater than 2, many of the coeﬃcient estimates may equal zero simultaneously.

What does Regularization achieve?

👉 In simple linear regression, the standard least-squares model tends to have some variance in it, i.e. this model won’t generalize well for a future data set that is different from its training data.

👉 Regularization tries to reduce the variance of the model, without a substantial increase in the bias.

👉 How λ relates to the principle of “Curse of Dimensionality”?

As the value of λ rises, it significantly reduces the value of coefficient estimates and thus reduces the variance. Till a point, this increase in λ is beneficial for our model as it is only reducing the variance (hence avoiding overfitting), without losing any important properties in the data. But after a certain value of λ, the model starts losing some important properties, giving rise to bias in the model and thus underfitting. Therefore, we have to select the value of λ carefully. To select the good value of λ, cross-validation comes in handy.

Important points about λ:

λ is the tuning parameter used in regularization that decides how much we want to penalize the flexibility of our model i.e, controls the impact on bias and variance.

When λ = 0, the penalty term has no eﬀect, the equation becomes the cost function of the linear regression model. Hence, for the minimum value of λ i.e, λ=0, the model will resemble the linear regression model. So, the estimates produced by ridge regression will be equal to least squares.

However, as λ→∞ (tends to infinity), the impact of the shrinkage penalty increases, and the ridge regression coeﬃcient estimates will approach zero.

End Notes

Thanks for reading!

Please feel free to contact me on Linkedin, Email.

About the author Chirag Goyal

Currently, I am pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, Deep Learning, and Artificial Intelligence.

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

Complete Guide To Postgresql Numeric

Introduction to PostgreSQL Numeric

While dealing with numbers, extra care needs to be incorporated while storing them, and the right type of datatype should be declared for our numeric field according to the value expected to be stored in it. PostgreSQL provides 10 data types that can store and handle numeric values in PostgreSQL databases. This article will discuss all the datatypes to handle numeric data in PostgreSQL. We will also see its storage size and range required and allowed for each datatype and take an example to understand how these values are stored and retrieved.

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Numeric Datatypes in PostgreSQL

In all, 10 numeric data types are present in the PostgreSQL database and contain integral values with 2,4, and 8 bytes of size, floating-point values, and auto-incrementing serial values with variable ranges. The table below lists the name of the data type, the size required to store each value belonging to that datatype, and the usage specifying when and where that data type should be used.

Numeric Datatype Size in bytes Usage

Smallserial datatype 2 bytes space Integers that will be autoncremented and are small in size.

Serial datatype 4 bytes space Integers that will be autoncremented and are medium in size.

Bigserial datatype 8 bytes space Integers that will be autoncremented and are large in size.

Smallint datatype 2 bytes space Integers that will have a small range of values.

Integer datatype 4 bytes space Most commonly used and preferred datatype for storing integral values.

Bigint datatype 8 bytes space Integers that might have a large range of values.

Decimal datatype Variable size It is a decimal value with user-specified and exact precision values stored in it.

Double precision datatype 8 bytes space It is a decimal value with variable precision values stored in it with a maximum of 15 decimal digits of precision.

Numeric datatype Variable size It is a decimal value with user-specified and exact precision values stored in it.

Real datatype 4 bytes space It is a decimal value with variable precision values stored in it with a maximum of 6 decimal digits of precision.

Integer Datatypes

Let us create one table named educba_integer_demo with three columns of datatype smallint, Integer, and bigint.

CREATE TABLE educba_integer_demo(small smallint,medium integer, big bigint);

that gives the following output if the table is created successfully.

Let us enter some records in it.

INSERT INTO educba_integer_demo VALUES(32767,2147483647,9223372036854775807);

If the value is inserted correctly, the output will be as follows –

We will increase the value of the big column by one to exceed the range and observe the outcome when we make such an attempt.

INSERT INTO educba_integer_demo VALUES(32767,2147483647,9223372036854775808);

The output will be as follows –

As you can see, the system throws an error stating that the bigint value is out of range.

Now, attempt to insert a value that is not in the range of small int using the following query –

INSERT INTO educba_integer_demo VALUES(32778,2147483647,9223372036854775807);

that gives the following output with an error saying smallint is out of range –

Inserting a value that is out of range of the Integer also shows the error as follows when a query like this is fired –

INSERT INTO educba_integer_demo VALUES(32767,2147483650,9223372036854775807);

Inserting all the three values that are not in the range provides the following error when such an attempt is made by executing the following query –

INSERT INTO educba_integer_demo VALUES(32790,2147483750,9223372036854775900);

As can be seen, it throws the error for only the first column that it traverses, which in our case is the smallint data type column.

Serial Datatypes

In Postgres, we have three data types available to create the columns that behave in the auto-incrementing fashion for storing values that will automatically be incremented by 1 by default and are unique fields. There are three datatypes – smallserial, serial, and bigserial datatypes. In general, developers use the serial data type in Postgres to store auto-incremented column values that range from 1 to 2147483647. When we don’t have to store many values, we can use smallserial datatype ranging from 1 to 32767. If we know that our database will store a lot of rows, even more than 2147483647, then we can use a bigserial datatype that has a range of 1 to 9223372036854775807.

Let us create the table having all these three data typed columns named educba_serial_demo using the following query –

CREATE TABLE educba_serial_demo(small smallserial,medium serial, big bigserial);

Output:

INSERT INTO educba_serial_demo VALUES (DEFAULT,DEFAULT,DEFAULT),(DEFAULT,DEFAULT,DEFAULT),(DEFAULT,DEFAULT,DEFAULT);

Executing the above query gives the following output –

Let us see what values are inserted in the educba_serial_demo table using the select statement –

select * from educba_serial_demo;

that provides the following output –

One can notice that the values are automatically inserted incrementally for all three columns.

Floating-point data types

We have four datatypes to store floating-point numbers with different ranges and either user-defined or variable precision. Numeric and decimal have user-defined precision and exact up to 131072 and 16383 digits before and after the decimal point. At the same time, real and double have variable precision with 6 and 15 decimal digit precision, respectively. We can use them per our use case and requirement and specify the precision and scale of numeric and decimal datatypes.

Conclusion – PostgreSQL Numeric

There are 10 numeric data types available in our PostgreSQL database that have different ranges and occupy different storage spaces in the database. We can use them as per our convenience and requirement. However, we must be careful using them and consider their behavior and range.

Seo Copywriting: The Complete Guide

There’s no denying the importance of search engine optimization.

Fully 68% of all online experiences start with a search engine, and organic searches are responsible for 53.3% of all website traffic.

Google reports that SEO traffic is 10 times greater than social media and 5 times greater than PPC.

But here’s the problem – Google doesn’t completely pull back the curtain to reveal every single factor of their ranking algorithms, leaving SEO experts to make educated guesses.

And the factors we do know about are constantly changing as Google makes updates to improve searches for users.

So, how do you make sure your content is ranking well on Google and landing on page 1 of the SERPs?

The answer – by bringing SEO copywriting into your content strategy.

In this comprehensive guide, you’ll learn the do’s and don’ts of SEO copywriting and a step-by-step process to optimize your content for Google’s ever-evolving search algorithms.

What is SEO Copywriting?

Search engine optimization (SEO) is the practice of enhancing a website for the purpose of achieving high rankings with search engines, especially Google.

SEO copywriting follows a similar goal but focuses on the content creation process, ensuring it offers maximum value and readability for both Google as well as regular users looking for information.

Google’s primary focus with each round of updates has always been to make search results more relevant for its users.

This is good news for content creators because if your focus is on building the best possible website for your audience, you’re already optimizing for high Google rankings. Still, there are additional steps to maximizing those rankings.

SEO copywriting is the creation of content that:

Google can understand and index.

Provides answers or relevant information for search queries.

People find engaging enough to read and share.

Is organized in a way that viewers and search engines can both easily read.

Targets keywords and phrases that users are searching for on Google.

SEO copywriting usually has additional goals that encourage readers to take a specific action – for example, to buy a product, subscribe to a newsletter, etc.

But the primary focus is on quality content. Any additional calls to action come second.

How to Plan, Create & Format SEO Content That Ranks in Google

If you’re going to produce high-quality SEO copywriting, you need to do some prep work before you jump into the writing process.

There’s a delicate balance between writing for Google and writing for humans.

If you write specifically for Google, your writing will be stiff and repetitive as you slip your keywords in word-for-word at every opportunity. That’s not how humans naturally speak.

If you write specifically for your audience without any consideration for Google, your content probably won’t rank well because you aren’t drawing enough attention to your targeted keywords.

But if you write with both Google and your human audience in mind, you’ll achieve maximum SEO potential.

Phase 1: Keyword Research

Before you start writing, you need to know your topic.

This is why keyword research is important.

You’ll want to target specific keywords that are relevant to your website’s established niche and have low competition (which gives you a much higher chance of landing on the first SERP).

The example below is from Semrush, which is one of many keyword research tools available to assist you:

KWFinder is another great tool that can help you find long-tail keywords to target.

These valuable resources can help you pinpoint the best keywords to target and generate ideas for similar keywords and variations you may not have considered.

Phase 2: SERP Intent

SERP (search engine results page) intent – also sometimes referred to as search intent – puts topics and keywords into perspective based on why someone is searching for a particular keyword on Google and how they intend to use the information.

Understanding search intent will allow you to properly target your intended audience based on their motives for using a search engine.

For example, let’s say we’re targeting [best pizza] as a keyword.

What is your audience actually searching for?

The best local pizza restaurant with outdoor dining?

The best pizza recipe to make at home?

The best pizza delivery service?

The best pizza toppings?

Knowing the search intent behind your keywords is just as important as the keywords themselves so you can tailor your content to the needs and expectations of your audience.

If you’re targeting [best pizza] from the perspective of a local pizzeria offering dine-in, takeout, and delivery, you’ll want your content to be for people in the area searching for places to eat, not people who are looking for online recipes.

Phase 3: Plan and Outline Your Article

Keyword?

Check.

Search intent?

Check.

Time to start writing?

…Not quite yet.

Before you dive right into the content, you need to finalize your plan.

Remember, Google and other search engines reward readability and organization, which means you should spend a little extra time planning out your content to ensure a logical flow and presentation.

Before you begin writing, ask yourself:

What is the purpose of the article? Are you hoping to inform, educate, promote a product or service, solve a problem, or answer a question?

What do I want to achieve? What’s your end goal? Are you hoping to get more website traffic and ranked keywords? Or do you want readers to take an action?

Who is my target audience? Knowing who will be searching for and reading your content is just as important as knowing what you’ll be writing about. If you’re talking to a Gen Z audience, your tone and language isn’t going to be the same as it would be if you were addressing a 65+ audience.

How will I order the information I present? What are the logical steps you need to include? For example, many articles present a problem and then propose a list of solutions to address the said problem.

What structure will best frame my content? A listicle, for example, will be laid out differently than a how-to article.

At a minimum, you should outline your headings and subheadings so you have a logical plan when you start writing.

Phase 4: Structure Your Article

As you build up your outline, pay attention to the structure of your article.

Readability is key, not just for Google, but also for readers who will likely be skimming through your article on a mobile device.

Be on the lookout for opportunities like these.

Visual media. Long blocks of text are difficult to read, especially on mobile devices. If your human audience struggles to read your article, Google isn’t going to be favorable toward it, either. Break up text with images, videos, and white space.

Subheadings. Make it easy for skimmers to scroll through your content and find the most valuable sections based on their needs and interests.

Lists. Break up important points into bulleted or numbered lists whenever possible. Lists are easy to read for humans, but they’re also a favorite for Google to easily generate information in a featured snippet.

For example:

Each point that Google listed in the featured snippet is a separate numbered subheading in the article.

See why lists and subheadings are so crucial?

One of the most common writing structures used in journalism is the inverted pyramid. This technique organizes an article with the most important information at the beginning.

Within the first paragraph, a reader should have all of the necessary details. Later paragraphs are used to elaborate and provide examples.

This tried-and-true writing technique works well for many types of content, especially if the article’s primary purpose is to answer a question.

Make your article structure part of your outlining process so you’re ready to hit the ground running with the next step.

Phase 5: Content Creation

Finally, it’s time to write.

It seems like a lot of prep work to reach this stage. But all of that time, research, and planning is what separates successful SEO copywriting from run-of-the-mill blog posts cranked out with minimal effort.

As you write, keep your keyword and audience in mind, but remember to let the writing flow naturally.

Don’t stuff your keyword into every other sentence. If you’re staying focused on your topic, the keywords and phrases will fit seamlessly into your writing as you go.

Be sure to include your keyword in at least the first subheading, preferably more than one.

Yes, you want to make it easy for Google to catalog the main points of your article. But ultimately, you’re writing for the humans who will be reading it, not the search engines.

Including links in your content is important for SEO. Be conscious of the websites you’re referencing so you can ensure you have high-quality links. Alexa is a great resource to check a website’s authority.

Phase 6: Edit

Do not hit publish on a rough draft without editing first!

SEO copywriting is all about producing high-quality content.

That means poor research, typos, disorganization, and other errors will take a serious hit on your credibility. Your content needs to showcase your expertise, authority, and trustworthiness (E-A-T).

Ideally, you should have a content calendar so you can stay on track and let your articles sit for a day or two before you return to them for a final proofread.

At the very least, read through your article a few times before you hit publish. Reading out loud can help you to identify areas that are awkward and lose their flow.

Be sure to check for:

Spelling.

Grammar.

Punctuation.

Active Voice.

Transition Words.

Tone.

Readability.

Sentence Structure.

Sentence Length.

Paragraph Length.

When necessary, break long paragraphs into smaller ones for better readability.

Be critical of your own work. Does the article convey the point you wanted to make? Does it have a clear call-to-action for the reader? Does it stay on topic?

Don’t be afraid to ask for feedback from others or enlist professional help to make sure you’re publishing the highest quality article possible.

The Secret to SEO Copywriting Success

The most important lesson to remember is that SEO copywriting is a process.

If you’re whipping out articles on a whim with no research or planning, you’re not going to see the same level of success that you would if you slowed down and approached content creation with a step-by-step approach.

Not everyone has a natural-born talent for writing.

And that’s okay.

You don’t need to have that talent in order to be successful at SEO copywriting.

If you’re willing to do the work, your SEO copywriting skills will improve over time.

As they say, practice makes perfect.

More Resources:

Image Credits

All screenshots taken by author, July 16, 2023.

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