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codesolutionsstuff · 2 years ago

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Building Smarter Web Applications with OpenAI for Laravel

OpenAI is a powerful platform that enables developers to build intelligent applications with natural language processing capabilities. Laravel, on the other hand, is a popular PHP framework that provides an excellent foundation for web application development. In this tutorial, we'll explore how to use OpenAI for Laravel, and how it can help us build smarter, more efficient web applications. Requirements To follow along with this tutorial, you'll need the following: - A local development environment with Laravel installed. - Basic knowledge of Laravel and PHP. - A OpenAI API key.

Getting Started

First, we need to install the OpenAI API package. Open your Laravel project in a terminal and enter the following command: composer require openai/api Next, you need to obtain an API key from the OpenAI platform. Once you have your key, add the following to your .env file: OPENAI_SECRET_KEY=your_secret_key_here With the package installed and the API key configured, we're ready to start using OpenAI in our Laravel application.

Generating Text with OpenAI

The OpenAI API provides several capabilities, such as language processing, chatbots, and much more. In this example, we'll use it to generate text based on a given prompt. To get started, create a new route in your routes/web.php file: Route::get('/openai/gpt3', function () { $openai = new OpenAIApi(env('OPENAI_SECRET_KEY')); $prompt = "The quick brown fox"; $completions = $openai->completions(); return $completions->choices->text; }); This code creates a new route that generates text using the OpenAI GPT-3 model. We pass in a prompt, which is the initial text that the model uses to generate the output. We then specify the model to use, the maximum number of tokens to generate, and how many completions to return. Finally, we stop the generation when we reach a new line. To test the route, visit http://localhost:8000/openai/gpt3 in your web browser. You should see some text generated by the GPT-3 model based on the prompt we provided.

Conclusion

In this tutorial, we learned how to use OpenAI in Laravel to generate text using the GPT-3 model. OpenAI is a powerful platform that can help us build intelligent applications with natural language processing capabilities. With the OpenAI API package installed in Laravel, we can easily integrate OpenAI into our applications and use it to make our web applications smarter and more efficient. Read the full article

#Analytics #ArtificialIntelligence #Chatbots #Datascience #Laravel #MachineLearning #NaturalLanguageProcessing #OpenAI

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codesolutionsstuff · 2 years ago

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Building Smarter Web Applications with OpenAI for Laravel

Getting Started

Generating Text with OpenAI

Conclusion

#Analytics #ArtificialIntelligence #Chatbots #Datascience #Laravel #MachineLearning #NaturalLanguageProcessing #OpenAI

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codesolutionsstuff · 2 years ago

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Top 10 DevOps Tools to Watch in 2023

In the coming year, we could want to adopt some new (relatively) DevOps tools because they could significantly increase engineering productivity. So without further ado, let's begin.

1. Pulumi

Let's start with Infrastructure, which is the foundation of DevOps. First things first: like Terraform, AWS CDK, CDK for Terraform, etc., Pulumi is an Infrastructure as Code (IaC) tool. Even if Terraform is arguably the most popular option for IaC today, there are several cons: - You'd need to learn HCL as a new "language" (HashiCorp Config Language). - HCL isn't really a "language" in the traditional sense, or at least not one that is very potent. For instance, a for loop on a module won't be usable until late 2020. Bring on Pulumi. So what is it? It's simple for you to comprehend if you are familiar with the AWS CDK: it is that. Except that it works with all clouds and is ubiquitous (or at least, it attempts to be). If you are unfamiliar with AWS CDK, consider this: With Pulumi, you can manage your infrastructure using programming languages that you are already familiar with, saving you the time and effort of having to learn yet another configuration language. For whom is Pulumi? Great inquiry. If you already know how to write in a language like TypeScript, Python, Go, C#, Java, etc. and don't really want to learn HCL, Pulumi might be the right choice for you. Technically, if you're using AWS, you could also use AWS CDK, however Pulumi makes more sense if you want to orchestrate a hybrid cloud architecture. You might also give Pulumi a go if you already use Terraform extensively but are sick of HCL's restrictions and don't enjoy utilising built-in functions like "count" that make your infrastructure code harder to read. This utility is no longer "new"; on GitHub, it has received more than 14k stars. Yet it is more recent than Terraform. Give it a shot if it fixes a specific issue you're having.

2. SOPS

The open-source text file editor SOPS, which stands for Secrets OPerations, automatically encrypts and decrypts files. The text editor, encryption, and automation are highlighted. Typically, you use these steps to encrypt a text file: - To write, modify, and manipulate the text data, use your preferred editor; then, save the changes as a file. - Encrypt the entire file using an encryption/decryption tool. When the encrypted file needs to be read: - Using an encryption/decryption tool, you must first decrypt the file. - With your preferred text editor, open the decrypted file (which is now a standard text file). This "standard" procedure has one glaring flaw: using two tools (an editor and an encryption/decryption tool) for a single task. Hopefully, you can see where I'm heading with this: SOPS is for that. In other words, utilizing a git repo to store secrets is possible and simple for collaboration because it can be coupled with numerous encryption services (such HashiCorp Vault, AWS KMS, etc.) to encrypt your secret files automatically.

3. Trivy

These days, when you want to build or deploy an app, your first thoughts are probably about containerization and 12-factor apps. Our cloud-native workload mainly relies on container images, so container image security is becoming increasingly important. Any container built from an image inherits all of its properties, including security flaws, incorrect configurations, and even malware. A security scanner is Trivy. It operates anywhere you need it, is dependable, quick, and simple. The most well-known application of Trivy's numerous scanners, which seek for distinct security flaws, is for scanning container images for known vulnerabilities (CVEs). Before publishing it to a container registry or deploying your application, you can use it locally as a CLI tool to scan your local container image and other artefacts. Trivy is also made to be used in CI and is simple to connect with your CI pipelines, making it the ideal fit for the "continuous everything" DevOps philosophy.

4. Cluster API

A Kubernetes subsidiary project called lustrous API is dedicated to offering declarative APIs and tooling to make it easier to set up, maintain, and upgrade several Kubernetes clusters. The Kubernetes Special Interest Group (SIG) Cluster Lifecycle launched the Cluster API project, which automates cluster lifecycle management for platform operators using Kubernetes-style APIs and patterns. The Kubernetes cluster configuration and the accompanying infrastructure, such as virtual machines, networks, load balancers, and virtual private clouds (VPCs), are all established in the same manner as how application developers deploy and manage their workloads. This makes it possible to install clusters consistently and repeatedly in a range of infrastructure scenarios. If the official definition is unclear to you, consider that you can construct a K8s cluster by using just one kubectl apply command. This command is compatible with AWS, Azure, DigitalOcean, Docker, GCP, OpenStack, and other services. For K8s clusters, kubectl apply is all that is required; there is no need to write Terraform modules (or, worse, try to figure out all the parameters of someone else's modules), and there is no need to learn how to use eksctl for AWS and something else for another cloud. Wow, that's impressive. I know. It is thus listed among the top ten tools to keep an eye on.

5. Linkerd

According to reports, Linkerd is the lightest and quickest service mesh in the world. A service mesh is what? A service mesh is an infrastructural layer that is specifically designed to make service-to-service communication quick, secure, and reliable. Linkerd excels in being simple to use. It only takes one line of command to install it. This sentence comes to a close. It's so straightforward that I'm at a loss for words. Let's chat more, though. Quick setup is used. Since even Docker images are small, they are pulled more quickly. There aren't any major differences in the architecture. There are two planes: a control plane and a data plane. The control plane consists of services that handle telemetry, API, control data for the data plane proxies, etc. The data plane also has proxies that operate next to each service instance. For further information, see the official document here. The open-source envoy proxy, a high-performance C++ distributed proxy intended for single services and applications, is used by Istio and AWS App Mesh. It is a sophisticated all-purpose proxy. On the other side, Linkerd uses a specially created Rust-written proxy to be as compact, light, and secure as possible.I'm not here to weigh in on whether Rust or C++ is the best and safest language, but as a contemporary language with a unique approach to memory management (ownership instead of garbage collection), Rust definitely has an advantage. Unlike Istio, Linkerd uses a service mirroring method for multi-cluster administration. The system is almost identical to a single-cluster arrangement in terms of simplicity, with the addition of two multi-cluster setups and a multi-cluster control plane. In conclusion, Linkerd is a unique form of service mesh that is incredibly lightweight, straightforward, and powerful. Without adding complexity, Linkerd enhances the security, observability, and dependability of Kubernetes. Even though it isn't really a new tool, you should try it if the features suit your needs and you value simplicity.

6. GitHub Actions

Another CI is GitHub Actions. So why GitHub Actions? We kind of have to take a close look at it because, among other things, it is on the CNCF tech radar (and is currently at the "assess" stage, making it a "new" tool). Another reason is that GitHub Actions naturally connect with your GitHub repositories and CI frequently interacts with your code. No more issues connecting your CI to your code repositories. Another advantage for startups is that GitHub Actions includes some free quota, which may be more than enough when you've just launched a new product, making it absolutely free.In addition to saving money on running some VMs in a cloud for your own infrastructure just for the CI component, you are probably not going to need to register any additional self-hosted runners for quite some time.

7. Tekton

Tekton is an additional CI (I know, I copy-pasted this line from the previous section). Key characteristics include: - It can be utilised by a K8s cluster. - You kubectl apply the pipelines after defining them as a native K8s resource. - It currently has a dashboard and CLI. Additionally, you may create, test, and deploy using Tekton in a variety of contexts, including serverless or virtual machines. Tekton pipelines can be used to deploy across several cloud providers or hybrid environments. Should you use it? My take is, if: - you must "own" your CI system (for instance, you may not be able to use the free quota of GitHub Actions for some reason); - you now employ K8s; - you enjoy interacting with K8s in this way; then attempt Tekton. It is easy to install, and you may quickly get it working.

8. HashiCorp Harness

Harness is a CI, but it goes beyond that. It comes from the company HashiCorp, whose name we already know, and it combines several elements into one: - CI - CD/GitOps - feature flags - cloud costs To run your builds, Harness provides hosted virtual machines (VMs). You may build your code worry-free on the infrastructure that Harness offers with Harness Cloud. You may concentrate on creating excellent software by putting less time and effort into maintaining infrastructure. Pipelines and Stages are used to model Continuous Delivery in Harness. You specify what to deploy using Services, where to deploy it using Environments, and how to deploy it using Execution steps in each stage. You may carry out GitOps deployments in Harness using Harness GitOps. In your Git manifest, you specify the desired state of the service you wish to deploy, and Harness GitOps uses your live Kubernetes cluster to sync the state. With the help of the feature management tool Harness Feature Flags (FF), you may alter the functionality of your software without writing new code. It enables you to conceal code or behaviour without releasing updated software. A feature flag is comparable to an effective "if" statement. In conclusion, this is the SaaS CI/CD/FeatureFlags solution to consider if you want them all in one location.

9. Thanos

Let's talk about Prometheus' local storage first: Even if we can use storage.tsdb.retention to set a lengthy data retention duration, such as years, the issue of scalability and planning still needs to be addressed. Long queries might take a lot of memory to process when there have been a number of high-resolution probes used. A rate() function over a year with a 15-second scrape interval, for instance, takes 2.1 million samples, or roughly 2.6MiB of data. And that only applies to one metric. The present TSDB implementation is perfectly capable of handling changing the retention time to years if you have a small infrastructure. Consider using a larger distributed TSDB for larger applications. And Thanos, an open-source, highly available Prometheus setup with long-term storage capabilities, is a solution that addresses this issue. It focuses on long-term storage. Try Thanos if you've already experienced problems with Prometheus storage.

10. HashiCorp Sentinel

Let's discuss about Sentinel now. Sentinel is HashiCorp's implementation of the policy-as-code methodology, in which policies are created, revised, shared, and enforced using code. Sentinel works effectively with other HashiCorp products because it is a HashiCorp product. Sentinel is the ideal tool for you if you frequently use Terraform, Vault, Consul, or Nomad and want to explore Policy-as-Code. Here are a few specific instances of what Sentinel policies can accomplish: - Don't let Terraform be used to provide Cloud resources without tags. - Make sure that only authorised system administrators with active MFA are able to modify critical Vault data. - Allow Nomad to only run Docker workloads. - Consul key updates are only possible during regular business hours. a sample short code: import “tfplan/v2” as tfplan aws_instances = filter tfplan.resource_changes as _, rc { rc.mode is “managed” and rc.type is “aws_instance” and rc.change.actions is not “delete” } main = rule { all aws_instances as _, instance { (instance.change.after.tags else {}) is not empty } } I think it's very self-explanatory. Get the AWS instances from a Terraform plan; after the update, tags cannot be empty (unless you are trying to delete the instance). Keep checking back because I'll shortly post an introduction to Policy-as-Code if you're interested.

Summary

- A brief classification of all the tools described in this article is as follows: - Pulumi, Infrastructure as Code - Security: Trivy and SOPS - K8s/multi-cluster: Linkerd, Cluster API - GitHub Actions, Tekton, and Harness are examples of CI/CD. - Keep an eye on Thanos - HashiCorp Sentinel policy as code Read the full article

#DevOps #DevOpsTools

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codesolutionsstuff · 2 years ago

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What's the difference Agile vs DevOps?

Two software development approaches, Agile and DevOps, share the goal of releasing the finished product as rapidly and effectively as feasible. Even while many businesses are keen to use these techniques, there is frequently some misunderstanding between the two. What is covered by each methodology? Where do they cross over? Should you select one over the other or can they coexist? As usual, we've drawn on the expertise of sector experts to describe and distinguish the two techniques, list their benefits and drawbacks, and emphasize the most appropriate applications for each.

Difference between Agile and DevOps

The primary distinction between DevOps and Agile is that the former is a development and delivery philosophy, whereas DevOps explains how to continually distribute code using contemporary tools and automated procedures.

Agile's Declaration

Agile software engineers act in accordance with the ideals and tenets stated in the Agile manifesto. The Agile Manifesto, which was drafted and endorsed by professionals in software development in 2001, outlines the 12 guiding principles and four core values that all Agile practitioners must uphold. - Functional software above thorough documentation - Persons and interactions, rather than procedures and tools - Adapting to change versus sticking to a plan - Customer involvement during contract negotiations While the Agile Manifesto is remarkably condensed, its significance to the software development community cannot be overstated. The entire text is less than 500 words. The Continuous Delivery of Software to the Client is the Top Priority, according to the Agile Manifesto. Additionally, it offers no clear instructions and makes no suggestions for certain procedures to follow. It is purely an exercise in philosophy.

What does DevOps mean to you?

Unlike Agile, which has a definitional document, DevOps does not. There is no unified definition of DevOps. Even the exact date when DevOps entered the common language is unclear. Many cite John Allspaw and Paul Hammond's 2009 Velocity conference presentation, 10+ Deploys Per Day: Dev and Ops Cooperation at Flickr. Many people also credit Gene Kim's book The Phoenix Project with accelerating the adoption of DevOps. ComparisonAgileDevOpsInception20012007InventorsJohn Kern, Martin Fowler at al.John Allspaw and Paul Hammond at Flickr, and The Phoenix Project by Gene KimHighest priorityContinuous software deliveryContinuous software deploymentFounding artifactAgile ManifestoThe Phoenix Project with a conference session with 10+ deployments per dayImplementation frameworksScrum, Kanban, ScrumBan, Lean, XPCAMS, CALMS, DORAAlternativesWaterfallDevelopment and deployment in silosTeam sizeSmall teams of ten or fewer people use it.As a company-wide strategy, it has been implemented.ScopeDedicated to the creation of a single applicationA company-wide strategy for software deployment Allspaw and Hammond describe how confidence in their software testing methods fostered a high level of trust between development and operations in their Velocity presentation in 2009. As a result, the organisation built a procedure for automatically deploying code into production more than ten times each day. At the time, automating such a thing was deemed revolutionary. Many things have changed in the software development business since 2009, however the following principles of DevOps remain: - Trust and teamwork between the development and operations teams - Comprehensive software testing methods are heavily relied upon. - Integration of cutting-edge tools to streamline development and operations chores - Automation of production deployment with no human involvement or checkpoints

Culture of Agile and DevOps

While many people disagree about what DevOps is, most definitions incorporate the word "culture." This begs the question, what exactly is culture? In general, culture refers to any group of like-minded people who use a common set of tools and adhere to a repeatable set of routines. In essence, culture is comprised of three elements: people, procedures, and the tools they employ

Processes and tools for DevOps

DevOps practitioners weave together various jobs and popular technologies to complete them, including: - Git and GitHub are examples of code repositories and version control systems. - Docker and Kubernetes are examples of containerization and orchestration. - Jenkins and other tools are used for automated and continuous code development and integration. - Kubernetes is used for orchestration. - Analysis of static and dynamic code; - Chef, Puppet, and Terraform are examples of configuration management and infrastructure as code. - AWS, Microsoft Azure, and Google Cloud offer public cloud platforms and services. Continuous integration and continuous deployment of code (CI/CD) is the overarching process that comes from all of these processes.

What are the similarities between Agile and DevOps?

The path to a successful digital transformation begins with accepting that Agile and DevOps can coexist, but also interact. All DevOps professionals believe in the following: - The importance of incomplete work and the automation of manual jobs - The significance of working with self-organized groups comprised of motivated persons; and - The primary focus is the ongoing delivery of software to the client. Every single one of these arguments can be traced back to one of the 12 Agile Manifesto concepts.

How do you combine Agile with DevOps?

Practitioners of Agile and DevOps share a same philosophy. Furthermore, their objectives are similar. A DevOps transition's ultimate goal is to fully automate the deployment of working code into production. This is complete DevOps enlightenment. The Agile manifesto emphasises unequivocally that the continuous delivery of software to the client is the utmost priority. Both Agile and DevOps believe that getting working software into the hands of clients is the best approach to produce software, assure transparency, and support long-term development. Agile and DevOps also intend for development and operations teams to employ cutting-edge tools and methods to get product into the hands of customers as quickly as humanly possible.

Differences and similarities between Agile and DevOps

To summarise, the following are the most significant distinctions and similarities between DevOps and Agile: - The Agile Manifesto defines Agile, but there is no commonly acknowledged definition of DevOps. - DevOps refers to a work culture, whereas Agile refers to a software development philosophy. - Continuous delivery is the top priority in Agile, while continuous deployment is the biggest priority in DevOps. - DevOps requires all manual processes to be automated, whereas Agile values "the amount of labour not done." - DevOps practitioners adopt an Agile attitude, whereas Agile participants must be self-organized and driven. People with Agile mindsets who embrace a DevOps culture all have one goal in common: the continuous delivery and deployment of software to the client. DevOps-based digital transformations can be successful if all parties adopt an Agile mentality. Read the full article

#Agile #AgilevsDevOps #DevOps

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codesolutionsstuff · 2 years ago

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How MongoDB Projection Improves Performance

This post summarises all of my research and analysis on the performance gains made possible by utilising projection in MongoDB. We will be able to determine whether using projection will enhance MongoDB query performance at the conclusion of this tutorial. Let's get started without further ado.

What is MongoDB Projection?

With a MongoDB projection query, we can specify the fields that should be returned. By placing a 0 or 1 next to a field's name after included it in a query, we can do projection in MongoDB. It will be visible if you specify the parameter 1; if you specify 0 it will be hidden. Queries by default return all fields from matched documents. The server manipulating the result set using projection criteria will be less effective if you need all the fields; entire documents should be returned instead. However, efficiency can be enhanced by utilising projection to restrict the fields that query results return by: - eliminating unnecessary fields from search results (saving on network bandwidth) - reducing the number of response fields to satisfy a covered query (returning indexed query results without fetching full documents) The MongoDB server will have to fetch each whole document into memory (assuming it isn't already there) and filter the results to return when using projection to remove unneeded data. Depending on your data model and the projected fields, this use of projection can significantly reduce network traffic for query results without affecting memory use or the working set on the MongoDB server. An exception to this rule is a covered query, which saves the server from having to retrieve the entire document by having all requested fields in the query result contained in the index that was used. If other queries don't need to fetch the same content, covered queries can decrease memory usage and enhance performance. Examples Imagine you have the following document to use as an example with the mongo shell: db.data.insert({ a: 'abc', b: new Array(10*1024*1024).join('z') }) A range of values could be represented by the field b. (or in this case a very long string). Next, build an index on the field a:1, which is frequently used by your use case: db.data.createIndex({a:1}) simple findOne() gives a query result that is around 10MB in size with no projection criteria: > bsonsize(db.data.findOne({})) 10485805 The result will only include the field a and the document _id if you add the projection a:1 (which is included by default). The query result is now only 33 bytes, but the MongoDB server is still manipulating a 10MB document to choose two fields: > bsonsize(db.data.findOne({}, {a:1})) 33 This query is not covered because it is necessary to fetch the entire page in order to determine the _id value. As a document's unique identifier, the _id field is included by default in query results; but, unless specifically included, _id won't be included in a secondary index. The results from explain() will display the number of documents and index keys investigated using the totalDocsExamined and totalKeysExamined metrics: > db.data.find( {a:'abc'}, {a:1} ).explain('executionStats').executionStats.totalDocsExamined > 1 The _id field can be removed from this query using projection to create a covered query that just uses the a:1 index. The covered query will be effective in terms of network and memory usage because it won't need to fetch a roughly 10MB document into memory: > db.data.find( {a:'abc'}, {a:1, _id:0} ).explain('executionStats').executionStats.totalDocsExamined 0 > bsonsize(db.data.findOne( {a:'abc'},{a:1, _id:0})) 21 My MongoDB queries are slow. Does my sluggish query—which uses a compound index on the field—get affected by returning a subset? Without the context of a particular query, an example document, and the entire explain output, this cannot be answered. To compare the results of the same query with and without projection, you might run some benchmarks in your own environment. It may be a clear indication that your data model needs to be updated if your projection significantly increases the time it takes for a query to execute overall (including processing and transferring results). It might be preferable to file a new question with specific details to examine if it's unclear why a query is delayed. Read the full article

#ExampleofProjectioninMongoDB #HowMongoDBProjectionaffectsQueryPerformance #MongoDBProjectionTutorial

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codesolutionsstuff · 2 years ago

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Defining, Creating and Dropping a MongoDB collection

We've already covered how to establish a database in prior topics. We already know that there is no command in MongoDB that allows us to build a database. It generates databases automatically. There is, however, a command to build a collection. In addition, we will learn how to create a MongoDB collection. We will also cover MongoDB drop collection with examples. Let's start with Defining, Creating and Dropping a MongoDB collection

MongoDB Create Collection

Tables are mentioned when discussing relational databases. Collections, on the other hand, exist in MongoDB. Collections in MongoDB are created automatically when we refer to them in any command. If it does not already exist, MongoDB will create it for you.

MongoDB Collection Creation Example

db.codesolutionstuff.insert({ name: “dipak” }) If no collection with the name "codesolutionstuff" exists, the above operation will create one. It will be produced automatically. We can also use the "createCollection()" command to create a collection explicitly. We must adhere to the syntax outlined below. db.createCollection(name, options) Before proceeding, it is necessary to understand the distinction between collections and cappedCollection. The collection has no size restrictions, whereas cappedCollection does. We can specify the maximum size and number of documents that can be created. The following parameters are available in MongoDB Create Collection: Parameter typedescriptionName stringThe new collection's nameOptionsdocumentOptional. Options for configuring a capped collection The options document comprises the fields shown below. FieldTypeDescriptioncappedbooleanOptional. True is used to create a capped collection. If you specify true, you must also specify the size parameter.autoindexidbooleanOptional. The default setting is false. This option disables the automatic creation of an index on the id field. sizenumberOptional. The maximum size for capped collection is specified. When the maximum size limit is reached, MongoDB deletes the older documents.maxnumberOptional. The maximum number of documents that can be stored in a capped collection. This takes second place to size.validatordocumentOptional. Allows the user to specify collection validation rules.Validation levelstringOptional. Determines the validation's rigor. Use the words "off," "strict," and "moderate." Let's look at a MongoDB create collection example. >use codesolutionstuff switched to db codesolutionstuff >db.createCollection(“mongodb”, { capped:true, size:1000000, max:2}) { “ok” : 1 } To verify, run the show collections command. >show collections mongodb This implies that a MongoDB collection has been created.

MongoDB Drop Collection

After learning how to construct a collection in MongoDB, let's look at how to drop a collection in MongoDB. MongoDB Drop Collection is even easier to use than it is to create. To remove a collection, type the following command. db.collection_name.drop() If the collection is successfully dropped from MongoDB, it will return true. Again, you should first determine whether or not your collection exists. Use the show collections command to accomplish this. >use codesolutionstuff Switched to db codesolutionstuff >show collections mongodb Now, for MongoDB Drop collection, use the following syntax- >db.mongodb.drop() >true You've now successfully dropped the collection. So that was the MongoDB Create Collection Tutorial. We hope you found our explanation about MongoDB Drop Collection useful.

Conclusion

As a result, we've seen how MongoDB builds collections and how MongoDB deletes collections. Simply keep running these lines of code, and everything will take care of itself. Now we'll go over some more fundamental MongoDB concepts. The data types in MongoDB will be covered in the following article. #Programming #php #laravel #JavaScript #DataScience #MachineLearning #Analytics #AI #ML #Tech #Python #ReactJS #DataScientist #Coding #100DaysofCode #SQL #bot #Cloud #Typescript #Github #Data #BigData #DL Read the full article

#CreatingCollectioninMongoDB #MongoDBCreateCollection #MongoDBDropCollection

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codesolutionsstuff · 2 years ago

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TOP 10 PYTHON TOOLS PROGRAMMERS SHOULD KNOW IN 2022

Python is an excellent language for both beginners and experienced coders, which makes it the best choice for most development projects.

Table of Content

- Keras - Beautiful soup - Jupyter notebook - Theano - Scikit-learn - Sublime Text - Sphinx - Pip - LXML - Selenium Python is very popular with programmers and is the most sought-after language around the globe. Because of this, demand has been increasing at a rapid pace in recent times. Python is an excellent language for both beginners and experienced coders, which makes it the best choice for most development projects. To become a skilled Python programmer, it's important to know the available Python tools. As you read this article, review the most popular 10 must-known Python tools programmers should know in 2022.

1. Keras

Keras is an open-source tool that provides a Python interface for artificial neural networks. It is free and modular, with the ability to reuse code across multiple projects. Other advantages of this framework are that it supports machine learning development with TensorFlow, CNTK and Theano. Keras is a machine learning library that has the capability to emulate the human brain. It also simplifies the creation of deep neural networks and currently supports TensorFlow, Microsoft's Cognitive Toolkit, Theano, and PlaidML among other frameworks.

2. Beautiful soup

Beautiful Soup is a Python package for parsing, finding, and editing parse trees that really take advantage of certain Python features. Convert documents automatically with our new tool. This can save you hours of work & means you don't have to keep answering the same questions again and again.

3. Jupyter notebook

Jupyter Notebook is a favourite destination for data scientists and machine learning engineers, as it helps you write and execute code quickly. All you have to do is run one cell to try it out. Additionally, this Python tool will be one of the best options to consider when it comes to 3D modelling.

4. Theano

If you need to do some mathematical computation and it involves many dimensions, then Theano is perfect for you. It can handle differentiation, different types of computations, testing etc.

5. Scikit-learn

One of the best free Python tools is Scikit-learn. It has features like regression, classification and clustering, which make it invaluable for data analysis models.

6. Sublime Text

Sublime Text is one of those free Python tools that can be used to develop code. It has plugins that you can add to your editor, which provide a range of features, such as auto-complete and much more.

7. Sphinx

Documentation is like a puzzle where you need the right pieces to make it work. Sphinx, a Python tool, can generate numerous files using just a few pieces of information about your project or company. The HTML file for instance gathers all your documentation concerning code and can be browsed in any machine with an Internet connection.

8. Pip

Getting started with Pip is easy. All that you have to do is download it and install it later, preferably with a sound internet connection.

9. LXML

LXML is a Python library specifically for handling XML documents, written in the C programming language. It supports both libxml2 and libxslt (the XML libraries from which it was originally extracted) and has no shortage of features that are missing from other Python libraries. One such example is its support for XPath expressions, without any need to configure the Expressions component explicitly

10. Selenium

Selenium is an open-source automation framework. One of its features is a tool for creating applications and databases. Thus, it can be used widely for computer applications such as computer-assisted learning or multimedia products. Using this helpful tool, you can now test scripts in a number of different coding languages – from Java to Python, Ruby and more! I hope you will like the content and it will help you to learn TOP 10 PYTHON TOOLS PROGRAMMERS SHOULD KNOW IN 2022 If you like this content, do share. Read the full article

#Python #PYTHONLIBRARIES #TOP10

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codesolutionsstuff · 2 years ago

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TOP PROGRAMMING LANGUAGES TO GET A JOB AT GOOGLE, MICROSOFT AND FACEBOOK

It's an available fact that if you want to work at the big tech companies, you need to know how to make an impression.

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- JavaScript - Ruby - Python - C/C++ - Swift - PHP - Java It's an available fact that if you want to work at the big tech companies, you need to know how to make an impression. These words of advice will be able to help you get one step closer on that path. Whether you want to be a web designer, game developer, machine learning enthusiast or work in any other connected tech profession, there is just one thing that you need – programming languages. These days programming languages are becoming the center of everything. Quite a few languages exist which can take aspirants to great heights in this field. As the demand for software developers is increasing rapidly, it is crucial to gain hands-on experience with the leading programming languages. These days, developers can choose from a wide range of programming languages to help land the perfect gig. In this article, we'll discuss which ones attract top companies like Microsoft and Facebook, as well as Google.

1. JavaScript

JavaScript was originally built as a competitor to Java by Netscape, but they quickly realized that it could grow into its own. JavaScript is a popular programming language because it can help developers quickly code new projects. It has many libraries and frameworks to use. There are various languages, frameworks and tools that people can use for front-end development. Node.js is a versatile language for backend development. Jest and Mocha are two helpful platforms that enable the setting up of unit tests to check if the functionality works as intended or not.

2. Ruby

Ruby is a Japanese programminglanguage. It received a lot of popularity because of Ruby on Rails' success. And Ruby on Rails was, at some point, the hottest framework for developing websites. Ruby on Rails can do a wide range of tasks. RoR has made coding popular among people. This technology has displayed programmers how to make coding smarter with its impressive and distinctive attributes. It can handle complicated scenarios and makes programming easy for anyone.

3. Python

Python is an ideal language for beginners to learn programming. It has a simple syntax that's effortless to grasp and use. It can be used in a myriad of software development, web development, and other projects. The Solid programming language provides significant support with APIs and garbage collection, as well as improved interoperability to other languages. In addition, its GUI support is amongst the best in the world.

4. C/C++

C++ is a favorite among software engineering buffs and for good reason. It is relatively fast and stable, which are great for systems programmers, and provides an organized framework called the "STL", which allows developers to write less code because much of it has already been written by various libraries. STL contains many algorithms, structures and arithmetic functions. You should choose C++ as a programming language to solve problems of audio-visual processing.

5. Swift

In 2014, Apple created its own programming language to compete with Microsoft. This was also the founding year of this language when it was used across their business and on their Linux and Mac applications. Swift is an easy, open-source language designed for the coding beginner. It's used by some of the highest demand applications such as WordPress and Firefox where it helps keep everything running smoothly.

6. PHP

PHP is still one of the most popular backend programming languages and useful for web development. Most websites use it including Facebook, Yahoo or any other company you can think of. PHP is also essential for web development so many people decided to learn it. Developers and programmers can start using this language for everything from writing command-line scripts and desktop applications to making traditional websites.

7. Java

Java has a lot of frameworks and libraries. It's used for app development. For instance, Spring uses Java as its backend, while Hibernate uses Java for database access. JUnit is a framework that helps with the creation of unit tests in your Java projects. Java is a building block of Android, which consists mostly of native Android apps. Java is a popular language that was most likely learned after an introductory computer programming course in college or school. Java is often used to teach object-oriented programming to beginners. I hope you will like the content and it will help you to learn TOP PROGRAMMING LANGUAGES TO GET A JOB AT GOOGLE, MICROSOFT AND FACEBOOK If you like this content, do share. Read the full article

#JavaScript #Python #TOP10 #TopProgramming

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codesolutionsstuff · 2 years ago

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14 REASONS TO USE ANGULAR FOR WEB DEVELOPMENT

Angular is one of the most popular JavaScript-based frontend frameworks in the world and also very popular here on GitHub.

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- Simplified MVC Architecture - Two-Way Data Binding - Fast Loading - SEO-Friendly - Faster Development - Using Directives - SPA-oriented features - Gives Structure to Your App - Templates - Enterprise-level Testing - Improved Flexibility - Client-Side Solution - Inbuilt CLI - Multiple Filters The recent survey (1,141,572 of the active sites surveyed) revealed that Angular was the most widely used framework. Additionally (3,016,752 sites historically), it is also quite common Angular is one of the most popular JavaScript-based frontend frameworks in the world and also very popular here on GitHub. It has 219 unique domains and 392,875 total followers, including over 300,000 people following only for updates to Bootstrap 4, which relies on Angular. The numbers are still going up and it's pretty amazing to see how quickly it's all happening Choose Angular for your next web development project. It's one of the most well-known frameworks, and many developers like to use it because it requires less time/effort. Angular is built with TypeScript, which makes the code easier to read and understand. If you find yourself needing the power of Angular but with a bit of a lighter learning curve, this article is for you. In it we'll provide insight into the benefits of using an Angular-based project and how to decide if it's right for your next web development endeavour.

10+ Reasons To Choose Angular For Your Next Project

Here I have outlined a few of the top reasons to use Angular. Moreover, if you need assistance with Angular development or want to optimise your current web application, it would be wise to take advantage of our expert Angular development services.

1. Simplified MVC Architecture

Angular is an easy-to-use web app that uses the MVC structure to separate the model from the view. It helps you work faster with data binding and MVVM architecture. This may be an appropriate choice for larger, more complex projects. The MVC framework is popular, because it allows developers to code the application in stages and then test the code one part at a time.

2. Two-Way Data Binding

Angular also provides two-way data binding. This means that changes in the model will automatically affect the view and vice versa, so you don't have to worry about keeping track of every variable by yourself. Two-way binding is fundamental for large apps as it handles all of the variable state on its own, which saves a lot of time & effort. The process of developing & testing such templates also becomes easier due to this feature.

3. Fast Loading

Angular was designed with business performance in mind. It's so fast and easy, which is why it works well for busy sites with a lot of traffic. This means you won't have to worry about slow loading times or too many resources being taken up by advertising on your site - that's always good for business! In addition, angular's optimization features make it ideal for high-load websites.

4. SEO-Friendly

One of Angular's key features is that it is SEO-friendly and therefore helps your website rank higher in search engines. As opposed to small startups, medium-sized and large corporations can take advantage of this application to grow their enterprise. Google loves Angular, so it's no wonder they built in SEO features. This means you can improve your website's visibility online where its needed most. To make your website SEO and user-friendly, hire dedicated Angular developers from the top rated Angular app development company They can make sure that you always rank high in SERPS and they will take care of all the code optimization aspects too.

5. Faster Development

Angular is a powerful and fast web framework that's easy to use, understand and helps with development speeds. The databinding & simplified MVC make it great for simpler projects where rapid results are desired. This means it's a great choice if you're working on a deadline or need to launch your website quickly, since Angular lets you build things more quickly.

6. Using Directives

This is the most important part of Angular. Directives are like an extension to HTML and make it easier for you to build your web pages and applications with less code.

7. SPA-oriented features

Angular is ideal for Single Page Applications. It has a lot of features that will help you build your app faster, such as routing, deep-linking and more. If you're looking for an Angular SPA builder, then have a look at the examples below.

8. Gives Structure to Your App

Angular provides the structure and organization that you need for your web app. This allows you to work on it more easily and maintain it in the future. It's a good alternative for large projects Additionally, Angular is backed by Google, which means it has a huge community of developers who are always ready to help you out. If you find yourself stuck with your project and want to find answers online, it's just as easy to find help

9. Templates

Angular has templates to help streamline your web app development. It includes pre-compformed HTML, JavaScript, & CSS which you can use to easily build your application. The Angular framework makes project management a piece of cake with the compilation ability.

10. Enterprise-level Testing

Angular is a popular choice because it has a robust testing suite which is good for high-performance applications. If you need to write unit tests or integration tests for your project, then the framework will have all the necessary features available to you. You can also use Angular with deadlines or a quick release.

11. Improved Flexibility

Angular's improved flexibility makes it a great choice for large applications. JavaScript and HTML are well-known, but Angular innovatively uses them to create single-page apps that can be updated easily without reloading the page. This is very valuable because you are ensuring that the code on the page is always up-to-date. You don't have to worry about refreshing the entire page each time you want to make a tiny change.

12. Client-Side Solution

Angular is a JavaScript framework that runs on the user's computer, and since it is client-side, this means that all the code can be written in the browser. Angular can also be run used server-side with NodeJS or Ember. This makes Angular a good choice if you're looking for more control to make your project function the way you want.

13. Inbuilt CLI

Angular also has a command-line interface that makes it easy to start creating new projects, so you don't need to go through the typical setup process. Just run `ng new` and your project will be ready in no time.

14. Multiple Filters

Angular also supports filters that provide a clean and efficient way to organize your code. Angular is often chosen for large-scale development projects because of the many benefits it offers. It has a great system for filtering data and tailoring it to what you need, which makes it perfect when you'll have to work with different views.

Know which Famous Web Apps Build Using Angular

Angular is an open-source web framework for building dynamic web applications. Some celebrities who made use of Angular are:

1. Gmail

Gmail, the popular email service from Google, is built with Angular It uses the framework to create a fast and efficient user-interface that makes it easy to manage emails." It has a simple single-page interface that supports 1.5 billion users daily

2. Upwork

It is a marketplace for freelancers and companies to connect, with an impressive range of services. Its Angular-based design makes it easy to contact service providers, organise payments and receive feedback from projects.

3. Microsoft Xbox

Xbox is one of the most popular gaming consoles around, and it's built with Angular. This framework helps make for a better experience for gamers on their favourite platform, since the app is easy to use.

4. Forbes

Forbes is a popular business magazine that was built with the help of Angular. Not only does it make things user-friendly, but you can access content online easily and quickly.

5. Paypal

Paypal is one of the leading online payment processors that's utilised Angular to create an intuitive UI. Angular has been designed to provide a simple way for users to make payments online. I hope you will like the content and it will help you learn. 14 REASONS TO USE ANGULAR FOR WEB DEVELOPMENT If you like this content, do share it. Read the full article

#Angular #MVC #WEBDEVELOPMENT

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codesolutionsstuff · 2 years ago

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TOP 10 IN-DEMAND DATA ANALYTICS SKILLS TO LEARN IN 2022

Data science is a game-changing technology that has become increasingly popular in an extensive number of industries.

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- Machine Learning - Python - R - Cloud Computing - Deep Learning - Tableau - Google Colab - Statistics - Data Visualisation - Artificial Intelligence (AI) Data science is a game-changing technology that has become increasingly popular in an extensive number of industries. The demand for data scientists has been steadily increasing over the last few years. Many companies, such as yours and mine, are looking to hire a professional who can handle our company's ever-growing volume of data. Data scientists are responsible for making the most of all business data, so I know this job is perfect for you. Data scientists are in high demand, with a shortage of skilled professionals to take on the task. When looking to hire someone for this position, it is important to consider an online program that can ensure candidates are well versed in both new techniques and technology. Visualization is becoming a very important way of making sense of the Excel or Google Sheets which are becoming increasingly common. This will happen as big data becomes more common; the age of machine-analysis has already arrived. Data Visualisation is a powerful way of converting data into something easier to understand. It can make patterns clear, show the most important numbers and present data in a way that's easy to understand. Infographics offer a unique way to make your data more understandable but it’s not as easy as adding an “info” element. You also need to balance between the way information is presented with its functionality. For example, people will be more interested in this infographic since it combines aesthetics and functionality by using visuals that convey the data sets you are representing. The success of a graph lies in the details. The lack of any detail can make it unnoticeable or unclear, but on the other hand if too much is included it may detract from the main idea or "say" too much. It's no secret that making data work together is an art form. Here are the top 10 skills you should study if you want to be a data scientist in 2022

1. Machine Learning

A lot of organizations use machine learning algorithms to predict upcoming events. It's important for these companies to hire data science experts who can create effective analytics algorithms. Data scientists are also able to go a step further and analyze the data further using machine learning technology. To learn more about the importance of machine learning in data science, you should consider enrolling in our ‘PG Program in Data Analytics and ML.’

2. Python

Python has popularised itself as a Data Science language due to its simplicity. Python is great for: data munging, analysis, and visualization of data. Python is one of the most commonly-used languages among data scientists. There are many different things they work on and Python makes it easy to start doing them all. This can help your business grow, as did happen with my company.

3. R

R is another popular programming language in the data science field. It's very easy to learn if you use a reputable online course. It'll teach you all about Data Science through practical examples and lectures. R is great for pulling critical data from huge datasets. This makes it the perfect language for anyone who needs to work with data in a variety of sectors, like healthcare, e-commerce and finance.

4. Cloud Computing

Many firms are turning to cloud computing to simplify their IT infrastructure. It's been proven as a reliable way of keeping up with the latest technology trends. The data analytics course at Imarticus Learning, for example, can help you get ahead in this field.

5. Deep Learning

Deep learning is being used for a wide range of tasks, such as speech recognition, natural language processing, robotics and more. It can help us advance our careers by assisting data scientists in their work

6. Tableau

Tableau is used by businesses worldwide to visualize and analyze data. A huge benefit of Tableau is being able to view the data in easy-to-grasp dashboards. Tableau can connect to many data sources, which gives data scientists a lot of options. To learn more about Tableau read 'Imarticus Learning's Pro-Degree Program in Data Science'.

7. Google Colab

Google Colab is a browser-based platform that enables users to run Python code. The Data Analytics course offered by Imarticus Learning can help you understand the benefits of using Google Colab. The PG Program in Analytics & AI educates students about Google Colab and its position in the business.

8. Statistics

Statistical skills are very important when it comes to data sorting, sampling, and analysis. An understanding of the principals involved in these processes will allow you to develop an effective machine learning algorithm that can extract valuable insights from unstructured data sets.Data scientists are required to carry out statistical analysis on their dataset to check for patterns - Imarticus offers the best resource for learning about this topic.

9. Data Visualization

It is not possible for data scientists to communicate their findings with words alone. Visuals are essential for people to understand the information you are trying to communicate. The best data scientists will have expert skills in data visualisation, which allow them to provide the information in a way that everyone can understand and take action quickly.

10. Artificial Intelligence (AI)

Adding artificial intelligence can help you automate analysis & forecast accuracy. Data scientists are using AI to generate real-time insights from large datasets - and it's the most in-demand skill right now! I hope you will like the content and it will help you to learn the TOP 10 IN-DEMAND DATA ANALYTICS SKILLS TO LEARN IN 2022. If you like this content, do share it. Read the full article

#ArtificialIntelligence(AI)#DeepLearning #GoogleColab #MachineLearning #Python #R #Tableau

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codesolutionsstuff · 2 years ago

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TOP PROGRAMMING LANGUAGES USED IN POPULAR WEBSITES

A programming language is mainly used for developing apps for desktop and mobile devices.

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- JavaScript - Java - Python - C++ - TypeScript - Golang - Dart Predominantly, programming languages are used to tell a computer what tasks it should complete. It does this by writing down sets of instructions in computer languages like C (C, C++, Java, Python), for example. A programming language is mainly used for developing apps for desktop and mobile devices. Here are the top languages that will be most popular in 2022

1. JavaScript

JS is the current market leader and an excellent programming language. The development of this language all happened in 1994 when it was created to be a Navigator scripting language for Netscape (at that time, one of the best browsers). JavaScript, the programming language that runs web pages on the Internet, was invented in 1995 by Brendan Eich at Netscape. Google has since adopted it with their Chrome browser and it is now widely popular among programmers of the future. Due to its popularity, JavaScript is the most often recommended programming language on the Internet. Developers from all over the world love to use JavaScript and this shows in the statistics. THis language is applicable to nearly all kind of development, like mobile app development, web development and desktop app development. Angular and Vue are two frontend frameworks, while Node.js is a powerful language for backend development tasks. Jest is one of a number of tools that can be used to verify the functionality of a project. You might also want to look into Mocha or Fuse for example. Of course, if you’re not very comfortable with HTML, CSS, and JavaScript (even though there are stacks of resources that teach you) then the easiest solution would be to use vanilla HTML, CSS and JS. Developers around the world can't get enough of JavaScript because it is one of the most supported languages in terms of packages. That being said, there are a lot of new solutions that can help you out with using this language.

2. Java

Oak was the first programming language to have a significant global impact when it was developed by James Gosling, Patrick Naughton & Mike Sheridan in 1991. The new programming language used the same format as C/C++, but had some new ideas to make it more appealing to a wider audience. Java is coded on the principle of Write Once, Run Everywhere. It has been designed to make it easy for everyone, no matter what type of computer or operating system they may be using, to use Java programs. Java also provides handy frameworks - libraries powering the back-end of Java projects. Spring, for example, is a framework used in app development. JUnit helps you set up unit tests for your Java project. Java is also used in native Android apps. For example, Java powers Android's SDK itself. Java is one of the most popular languages used in programming courses. Java teaches object-oriented programming to beginner programmers and it can be found in a lot of colleges and schools. Java is also highly respected in analytics & research, but the only problem with it is the lack of support packages and applications. There's very little community involvement - something that most major languages have. Although it's a language that is easy to learn, and so has appealed to adopters, Java can be difficult to master.

3. Python

Python has been used in the Dutch industry since the late 1980s. Initially, it was designed to be a competitor for Java. However, Python did not really take off until it became heavily popular among researchers in recent years. It's well-known that Python is one of the most versatile programming languages around, with a perfect score on the IEEE Spectrum rankings. It also has a lot of support and respect in the developer community, at 44.1%. Python is suitable for pretty much anything, because it has web frameworks like Django and Flask or scientific tools like Jupyter and Spyder. If you’re looking for automation, Selenium is worth checking out. Python offers a lot of flexibility through the use of its language and pretty much anywhere can benefit from it. These are some of the more popular tools produced by Python. Python is one of the most popular programming languages and there are tons of packages, frameworks, & open source software available. There are many programming languages available for machine learning and data science in general. However, Python is probably the most popular. R and MATLAB also offer a lot of competition, but Python’s the undisputed king of businesses large & small. A significant number of the frameworks and libraries used in machine learning are made in Python, so it seems like a language worth taking on if you're interested in learning more about this topic. It's also beneficial because the majority use Python.

4. C++

Perhaps one of the most surprising responses in this article will come from C++. Despite being the language people often use to learn about data structures & algorithms, it rarely finds practical usage. One example of how C++ became successful is when it was first created by Bjarne Stroustrup in 1982. The programming language was created as an extension of the C programming language and went on to be used widely across many industries. C++ may be best known for its use in gaming, but there are a number of other uses for it. C++ is used by some analytics software and by the Unreal Engine. The Unreal Engine's scripting language is also C++ which gives developers all of the functionality they need to develop games. C++ can be really useful for software development. It is perfect for projects that need to be flexible in their method but want the benefits of object oriented programming. C++ is still in use and ranked 4th in the TIOBE index. It's easier to understand than other languages, which is ideal for certain types of work. The language is also extensively used in system software development. Security is the prime consideration in any sensitive area like the OS and C++ provides the best level of security. Its low compilation time also comes in handy. Nightly builds can be performed much more quickly, minimizing possible vulnerabilities. You can find the largest number of people learning C++ among all other semantic languages. Most students would start their data structures courses building trees, queues, and various other data structures in C++. Naturally, It is relatively easy to pick up and learn, as well as simple to master if one pays attention to details.

5. TypeScript

TypeScript has several different applications and can be used whether you are developing for the web, desktop apps, or an app for your phone. Head to our website for more information about these! It also contains a lot of other useful resources for small businesses. TypeScript is the second most popular language loved by 67.1% of developers (only second to Rust). TypeScripts focus on development means it doesn't have much appeal to scientists. However, with the new features that TypeScript has - like static types which were introduced in ES6 and can be found in modern, popular languages like Java and Go - one might see a greater interest among programmers. The skill required to learn this language is much less than that of JavaScript – and many behaviors that are difficult to understand are easier in TypeScript. In other words, you have a slightly lower chance of hurting yourself.

6. Golang

New languages are rising on the horizon and are challenging JavaScript and Python which have been the major pillars of this industry for a long time. Google developed Go to advance the cause of functional programming, and it has already earned a large following in just a few years. It’s made it the fifth most popular language according to StackOverflow and is Go is used in a variety of areas including for developing robust software and for backends for websites and mobile applications. At the moment, Go even supports some basic web development. With more and more websites now being powered by JavaScript, it is becoming apparent that TypeScript is fast becoming the next web language. It’s already full of features and continues to rise in popularity. The Go programming language is slightly more difficult to learn than other languages, but it's worth the effort. It's an open-source language that gets frequent updates, so staying updated is a necessity.

7. Dart

Dart is quickly becoming a vital player in the industrial sphere. Google’s contribution in the spheres of languages has significantly increased to compete with the increase in popularity of Microsoft’s TypeScript. Dart has been highly adored by programmers and companies alike, which has led to an overall rise in its popularity Dart is often used for building software that can run on various different devices. For instance, Flutter is a framework made with Dart that runs on all sorts of devices. Flutter is quickly surpassing React Native as the most popular framework today. This comes with a ton of benefits for developers like improved UI and UX, higher performance and simpler codebase management. Dart is easier to learn than JavaScript and has a more intuitive approach to more complex cases. Programmers now have TypeScript & Dart for use in their own work. I hope you will like the content and it will help you to learn TOP PROGRAMMING LANGUAGES USED IN POPULAR WEBSITES If you like this content, do share. Read the full article

#JavaScript #Python #TOPPROGRAMMINGLANGUAGES #TypeScript

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codesolutionsstuff · 2 years ago

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TOP 10 DATA SCIENCE TOOLS WE MUST LOOK FORWARD TO IN 2022

Data scientists focus on extracting meaningful data from unstructured information and analyzing it.

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- Python - Jupyter Notebook - Apache Spark - D3.js - TensorFlow - Keras - Xplenty - IBM SPSS - PyTorch - KNIME Data scientists focus on extracting meaningful data from unstructured information and analyzing it. In order to do this, they need to explore the possibilities of what can be discovered from data. This kind of knowledge has grown increasingly important over time. The following are some of the top 10 data science tools that one should know about in 2022.

1. Python

Python is one of the most popular programming languages in Data Science and Machine Learning, as well as being one of the most popular general-purpose languages in use. Python is a simple but powerful programming language, which means people of any skill level are able to use it. Python is open-source and so it is available to use without cost. It also makes things easier because you can change or customize the code on your own. Python is a simple language to learn and it reduces the cost of program maintenance. Plus, you can use code blocks to improve readability. Excel is a language that can be used for multiple purposes, from data analysis, to AI and more. Python is a language and tool for computer programming that has several benefits. It's an Object-Oriented, Procedural, Functional and variations of this such as OOPF (Object-Oriented Procedural Functional). Developers can also extend Python with C or C++.

2. Jupyter Notebook

Jupyter Notebook is an open-source web application that enables interactive collaboriation among data scientists, data engineers, mathematicians and other users. This is a computational notebook tool that you can use to create, edit and share code in order to communicate ideas more efficiently. Users can also upload images and other information to complement their work. Jupyter Notebooks make it easy to add code, computations, comments, data visualizations and more to a single file that can be shared with and revised by colleagues As a notebooks can serve as a complete computational record of all the interactions your data science team has, which are important for collaboration. Jupyter notebooks are a type of file that can be uploaded and downloaded. They also have a version control system so you know what was updated. Another thing that they do is enable you to view your work in public, even if you don’t have Jupyter on your own computer.

3. Apache Spark

Apache Spark is an open-source data analytics and processing engine which, according to its proponents, can handle large amounts of data ranging from several petabytes. The quick processing rates of Spark data has driven a lot of its popularity as it helps organizations deal with large datasets. This is quite an accomplishment considering that the program was created back in 2009 and is currently one large open-source community. Due to its speed, Spark is well-suited for processing continuous intelligence applications powered by near-real-time processing of streaming data. It can also be used as a general-purpose distributed processing engine in order to extract, transform and load data. Spark has been touted as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.

4. D3.js

D3.js is one of the most popular open-source tools for creating data visualizations in browsers. It allows developers to use web standards - like HTML, CSS, and Scalable Vector Graphics - to create custom charts and graphs, making it easier than ever to showcase your information online. D3’s developers have described it as a flexible & dynamic tool that requires minimal effort to generate visual representations of data. D3.js makes it possible for designers to bind data to documents, including the DOM, and then use DOM manipulation methods to make interactive visualizations based on this data. First released in 2011, D3 is used to design a range of data visualizations and it provides interactive, animated and annotated content. There are 30 modules, 1000 visualization methods and over 7 trillion data points from various sources. Plus, not many data scientists have JavaScript skills. This is why they may opt for commercial tools like Tableau instead of D3 (which is more for visualization developers).

5. TensorFlow

TensorFlow is a free machine learning software developed by Google that is especially popular for constructing deep neural networks. You can feed in Tensors that are similar to multidimensional arrays, and then use a graph structure so it can move through the list of operations you define. It also includes an eager execution programming environment that runs operations individually, providing more flexibility for research & debugging models. Google made TensorFlow open source in 2015 and Release 1 following suit soon after. TensorFlow.js is now part of TensorFlow, integrating the Keras high-level API into its core programming language. Additionally, you can work with a custom ops library to tailor TensorFlow to your own specific needs.

6. Keras

Keras is an open source Python-based interface to the TensorFlow machine learning platform. It is a deep learning API & framework designed to be written & run on top of TensorFlow. Keras has been in development for a while now and you might also want to check out this article if you're interested in using it as well Keras previously supported many back ends but is now tied exclusively to TensorFlow and no longer facilitates experimentation. Keras was designed with ease of access in mind, meaning it's less burdensome on your coding ability. The goal with Keras is to accelerate the process of implementing and training deep learning neural networks through a development process that runs quickly. The Keras framework offers sequential and functional interfaces that can be used to create deep learning models.

7. Xplenty

Xplenty is an ETL and ELT platform which integrates all your data sources to create a complete task-checking software for building data pipelines. This elastic, scalable cloud software can not only integrate and process, but also prepare the data for analytics. It is a suite of SaaS tools that provides solutions for marketing, sales, customer support, and developers. The Sales solution has features to understand your customers and let you fill in missing data from their CRM through centralizing metrics & sales tools Its customer support solution will give you a better picture of your customer and make sure that you are using their product in an effective way. It can do personalized support, as well as automatic upselling. Our marketing solution helps you to build effective, comprehensive campaigns and strategies. We offer features such as data transparency, easy migrations, and connections to legacy systems.

8. IBM SPSS

IBM SPSS is a family of software that can be used to manage and analyze complex data. It includes two primary products: SPSS Statistics, a statistical analysis and data visualization tool with over 60 years of trusted experience and SPSS Modeler, a data science and predictive analytics platform that's easy to use. SPSS Statistics is a perfect choice for exploring data and discovering patterns in your business. It can help you organize your analytics process masterfully, starting from the research phase through to deployment. This makes it easier to access structured data, has a convenient UI & good integration with R and Python. They can automate procedures and offer export-import ties to SPSS. SPSS was founded on 1968 and is now owned by IBM. It creates statistical analysis software and predictive modeling platform. The IBM SPSS product line was officially called IBM SPSS, but it is commonly referred to as just "SPSS".

9. PyTorch

A popular open-source framework for developing and training deep learning models, PyTorch is touted by its proponents for its ability to quickly & seamlessly transition into production deployment. PyTorch was designed to be easier to use than its predecessor, Torch. It’s based on Python and provides more speed & flexibility than the original framework. Swift released in 2017 and is oddly well-known. It's used to create tensors which are designed for optimizing models. The tensor operations supported in PyTorch's framework provide a powerful solution for scientific computing. It combines the data access and vectorization capabilities of NumPy arrays with the ease-of-use and speed of GPUs.

10. KNIME

KNIME is a platform for data scientists where they will be able to use and expand on the available tools that are at their disposal. This open-source software will help them in blending various data types as well as explore new avenues of analytical work and research. It can be useful for repetitive and time-producing tasks. This coding language is commonly used in experiments & big data, expanding to Apache Spark and platforms of all kinds. I hope you will like the content and it will help you to learn TOP 10 DATA SCIENCE TOOLS WE MUST LOOK FORWARD TO IN 2022 If you like this content, do share. Read the full article

#Datascience #Python #TOP10

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codesolutionsstuff · 2 years ago

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TOP 10 PROGRAMMING LANGUAGES THAT DATA SCIENTISTS SHOULD LEARN IN 2022

Data science is such an important profession, and new jobs are arising that require people with expertise in this realm.

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- JavaScript - Java - Scala - Python - R - SQL - C++ - Julia - MATLAB - HTML Data science is a field where skills in software engineering, analytics, and mathematics are all useful. Given the demand for data scientists with programming skills, mastering only one of these areas could restrict your opportunities. A data scientist with a software background is more independent and does not need outside resources to work with data because these two worlds interact so often these days. Data science is such an important profession, and new jobs are arising that require people with expertise in this realm. Below is a list of the most popular programming languages that data scientists should learn in 2022.

1. Javascript

In the modern tech world, it is hard to imagine a developer that doesn't use Javascript at least on some level. It's a high-level programming language and one of the core technologies of this era. Javascript was originally made for front-end development but now it can be geared towards back-end work with the advent of NodeJS. It's also one of the best programming languages to learn as a data scientist in 2016.

2. Java

Java was initially built by James Gosling, Mike Sheridan and Patrick Naughton in 1991 as the language Oak. It had a big global impact being the first programming language to enable efficient high-level abstractions. Java is one of the most widely used programming languages used these days. Java is back-End code, which makes it a good choice for any device that has an internet connection. This includes not only web development but also Android and other programs. It's also common to use Java on desktops because this language is easy to use and provides a lot of support if you have

3. Scala

Scala has a close relationship to Java, which is why it’s often associated with data engineering. To that end, Scala’s interoperability benefits from being able to compile and execute on the Java Virtual Machine. In addition, Scala was developed as an extension of Java in response to certain perceived problems with that programming language. Scala is an excellent choice for high-speed data handling. Seen in enterprise data science, Scala also has a ton of other uses that are great for any company.

4. Python

The Python programming language will continue to be among the most popular ones out there and it's all thanks to its readability, fantastic support for many industries, ease of acquisition, and incredible versatility. This language shows a 2.17% increase in popularity, as opposed to the decline for Java in the same period.

5. R

R is a programming language that is widely used in data science, while Ruby is also very popular. It's particularly favored by startups, with companies such as AirBnb and Twitch using it. The demand for Ruby on Rails is up - it's fuelled by a full-stack web application framework by the same name which runs on the Ruby language.

6. SQL

SQL is used by programmers to locate and retrieve data from a database and also to update, add or remove records. A common misconception is that SQL stands for “Seletal Quadrangle Language” but this isn’t true. SQL is a very useful programming language for database management, which finds a broad application in many market fields.

7. C++

One of the most shocking answers you will find out below is that C++ is used in analytics along with research as well as in game development. The popular game development engine, Unreal Engine, uses C++ for scripting in games and software alike. You can do a wide range of things through scripting with the help of this versatile programming language.

8. Julia

Julia is a relatively new language and it isn't as mature as older languages. However, Julia is becoming more popular among data scientists that work in high-performance computing environments. It not only has a fast development process, but also produces apps that perform as well as those created in languages with less resources.

9. MATLAB

Machine Learning Toolbox is a programming language and environment that caters to statistical computing. It offers a set of dynamic visualizations and also comes with a deep learning toolbox. The toolbox favors trends in the recent technologies and supports most languages.

10. HTML

HTML is a popular platform for developing cross-platform games and applications that render well on different browsers. HTML5, a newer version of the language, can often be used in place of JavaScript to accomplish similar tasks. A programmer usually doesn't need programming understanding or fluent knowledge of algorithms to be able to use HTML, which is a simple language. It's because of this that it's increasingly becoming the preferred choice for game designers. I hope you will like the content and it will help you to learn TOP 10 PROGRAMMING LANGUAGES THAT DATA SCIENTISTS SHOULD LEARN IN 2022 If you like this content, do share. Read the full article

#JavaScript #Python #TopProgramming

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codesolutionsstuff · 2 years ago

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TOP 7 BENEFITS OF SOCIAL MEDIA FOR BUSINESS YOU SHOULD BE APPREHENSIVE OF …

Social media allows you to drive content snappily and fluently to large number of implicit guests at little to no cost. Internet has made power of word of mouth.

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- Increase your brand mindfulness and reach with a little budget - Know your guests/ prospects more - Engage with your guests - Increase your website business and hunt ranking - Increase Digital Exposure - Expand Deals and Reaching Followership - Cut Marketing bring Social Media Marketing can be a largely effective way of gaining guests via your website. With an effective strategy in place, all you have to do is find out what channels your target followership frequents, and draft the perfect updates that'll really intrigue them Everyone is talking about social media but you still don’t see how Facebook, Twitter, Instagram or LinkedIn can help your business grow? Then are good reasons to start being more active on social media

1. Increase your brand mindfulness and reach with a little budget

Social media allows you to drive content snappily and fluently to a large number of implicit guests at little to no cost. The Internet has made the power of word of mouth. But rumors travel as presto as good news! Having a social media strategy puts you in control of the exchanges passing around your business online. It allows you to tell your story, driving your brand character.

2. Know your guests/ prospects more

Social media shouldn’t only be about broadcasting your communication to your suckers and followers. Taking the time to actually hear to your guests is absolutely precious to your business. Learning about your followership will help you feed juggernauts and produce offers to your target guests, and ultimately give you a better return on investment.

3. Engage with your guests

Did you know that 71 of guests who endured a quick and effective brand response on social media were more likely to recommend this brand to others? Interacting with your followership and converting a positive word of mouth will efficiently help you attract further guests.

4. Increase your website business and hunt ranking

Not only does social media help you direct people to your website, but having your posts liked or participated will ameliorate your hunt ranking. Being active on social media will make your company appear advanced on Google’s hunt runner.

5. Increase Digital Exposure

Interacting a social network can significantly increase your reaching preference social media leads domestic exposure to worldwide access and huge quantum of diurnal druggies.

6. Expand Deals and Reaching Followership

Harkening to your prospects of social network can help to respond specific requirements will beget increase deals also expand client base

7. Cut Marketing bring

Compare to traditional channel like advertising social marketing is affordable for any business. Remember that the channel itself is a free. I hope you will like the content and it will help you to learn TOP 7 BENEFITS OF SOCIAL MEDIA FOR BUSINESS YOU SHOULD BE APPREHENSIVE OF … If you like this content, do share. Read the full article

#SEO #SocialMedia #SocialMediaMarketing

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codesolutionsstuff · 2 years ago

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TOP 10 DATA SCIENCE JOB SKILLS THAT WILL BE ON HIGH DEMAND IN 2022

Data science is all about getting the best out of data. Data science is considered to be the right way to go when it comes to extracting insights from raw data.

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- DataWrangling - Statistics - DataVisualization - Building Pipelines - Critical Thinking - Programming - Problem Solving - Model Deployment - Communication - Teamwork Yes, there are plenty of courses and books on data science considering how much it is relied upon in this day and age. However, there are many skills and qualifications that are needed in order to be a successful data scientist. With that in mind, I would like to share with you the top skills that data scientists should have.

1. DataWrangling

Data science is all about getting the best out of data. Data wrangling is the process of transforming data from one form to another, which is important for a variety of reasons such as making it easier and more efficient to use. This is extremely important as data science can involve a lot of work, including building models and exploring new features.

2. Statistics

Data science is considered to be the right way to go when it comes to extracting insights from raw data. The best part is that you can literally turn any data set into meaningful information. It doesn't just take raw data, but also looks at finding better insights and then moving on with sorting it all out. Data sets are generally easier to analyze using statistical analysis because it helps to identify relationships between the sets.

3. DataVisualization

Data science is closely tied to analytics, and the quality of your analytics depends on the quality of your data scientists's communication. You'll need to make sure you hire people who can communicate their insights well in order for them to be successful in meeting business targets. Here are three ways a good data visualization can often help a data scientist:

4. Building Pipelines

In the field of data science, there will be occasions where it is necessary to present your findings. If a project or model does not exist, then someone with robust pipelines can create one. A successful data scientist is also distinguished by their ability to build projects or models rather than relying on existing ones. This saves time as well.

5. Critical Thinking

Critical thinking is the skill that we need to use in order to make well-informed and appropriate decisions, based on data and facts. It might seem challenging at first, but with a little patience, it becomes more easy to do.

6. Programming

Successful data scientists need programming skills to really make a lasting impact. If you are familiar with languages like Python, R, and Java, then this is for you. This is because most interactions with the computer are done through programming. Learning how to do this means that you can never fail in your endeavours.

7. Problem Solving

A data scientist's job is to solve problems. Data science is often associated with many tricky problems that need solving right away. Once a data scientist understands the problem, it is important to be able to synthesize that understanding. The ability to translate the understanding into production-ready code will allow for more reliable results.

8. Model Deployment

The deployment model is one of the most important aspects for any data scientist. It helps predict customer needs & how to better serve them which is the goal of all organizations.

9. Communication

Data science can be used to turn raw data into analytics which is easier for everyone in your company to understand, so that you can make better decisions as a whole. This highlights the importance of understanding, and taking a softer approach when communicating to all types of employees. Writing down & translating the technical aspects of your work to the non-technical employees, means that everyone understands what's going on.

10. Teamwork

Data scientists should never be expected to work in isolation. Science thrives on collaboration and experimentation. Data scientists work closely with other departments, such as Finance, IT, Operations etc. This is why teamwork is important I hope you will like the content and it will help you to learn TOP 10 DATA SCIENCE JOB SKILLS THAT WILL BE ON HIGH DEMAND IN 2022 If you like this content, do share. Read the full article

#Datascience #DeepLearning #MachineLearning

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codesolutionsstuff · 2 years ago

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5 Python Ideas That Will Help You Advance Your Career

Using these Python Ideas in your code will make you a seasoned developer! Python is a high-level, object-oriented language that is gaining popularity and is easy to use. It is versatile, dynamic and robust, which makes it an excellent choice for both students and professionals. In addition to that, Python is the second most loved and preferred programming language after JavaScript. It is applicable in practically all technical domains. So, demand for Python developers will keep increasing in the upcoming years. The following are four important concepts that any developer would be wise to incorporate into their work in order to stay ahead of the game.

1. Understanding lists and dictionaries

This is an often overlooked concept in programming that can cause a lot of confusion. What if you create a list called ‘x’ and then, assign this list to the variable ‘y’? x = y = x Append new value in the y list and then print both lists: y.append(6) print(y) # Prints print(x) # Prints You're probably asking why the new value was added to both lists! This occurs because, unless otherwise specified, lists are not copied when assigned in Python. A new reference to this list is instead created. This means that y is just a reference to x and thus it works as you wanted it to. This means that changes in either variable will be reflected in the same list. You must use the.copy() method to make a duplicate of the list: x = y = x.copy() y.append(6) print(y) # Prints print(x) # Prints

2. Context managers

Python's Context Managers tool, a classic example of Resource Management, helps in allocating & releasing resources when the need arises and ensures that all aspects of a resource are handled properly. The most used and recognized example of a context manager is with the statement. The file-opening/closing 'with' mostly indicates the start and end of each file.. file = open(‘data.txt’,’w’) try: file.write(“Follow Me”) except: file.close() The with context manager enables you to do the task of opening a file in write mode and closing it in one line if something is not right. This would be especially useful for closing the file automatically if, for example, the user tries running the script but does not have permission to write to this file. with open (‘data.txt’,’w’) as f: f.write(“Follow Me”) Notice that we never called f.close() even though we opened it previously. Context managers handle these tasks automatically, and they will also catch exceptions if they are raised while cleanup is being done. Context Manager's usefulness goes far beyond just files; they could be used to manage a game's state or a database connection for example!

3. Generators

A generator is a kind of function that returns an object that can be iterated over. It contains at least a yield statement. The yield keyword in python is used to return a value from a function without destroying its current state or reference. A generator is a function that contains the yield keyword. A generator will only generate one piece of data and anything else you ask it to once. They are very memory efficient and take less space in the brain. Example def fib(limit): a,b = 0,1 while a < limit: yield a a, b = b, a + b for x in fib(10): print (x) Yield will pause the execution of a function and return a value from it every time. On the other hand, return terminates it.

4. Type hinting

Hints are used to make your code more self-explanatory and thus easier to read. One way you can do this is by hinting the type of the parameter & return value of a function. For example, we want to validate that the input text of a user is always an integer. To achieve this, we write a function that returns True or False based on our validations: def validate_func(input): ... Now, you see the usefulness of this function. It's not that complicated if you just take a look at the definition. Without that, it would be much more difficult to understand how this works. What is the input parameter's variable type? Where does it come from? Is it already an integer? What if it isn't? Does this function return anything, or just raise an exception when something goes wrong...? Some questions can be answered by refactoring to this code: def validate_func(input: str) -> bool: ... With this function, it's easier for a first-time reader to understand.

5. Logging

Logging is the process of recording the code that a programme runs. Logring facilitates debugging by displaying the steps made by a programmer while creating code. Python provides some modules that make logging fairly simple, and logs can be outputted to files afterwards if necessary. - You can use this to determine what is wrong with your text. - Success has been confirmed. - When an unforeseen scenario arises, issue a warning. - Error: Because of a more serious issue than a warning. - Critical: A critical error occurs after which the software is unable to run. Soon, I will provide a special article on "Logging in Python." Subscribe to receive an email when it is published.

Conclusion

Here are the Top 5 Python Ideas That Will Help You Advance Your Career. The concepts mentioned above are just a few of the Python insights that experienced developers keep in mind. I hope you found this essay useful and learned something new. Read the full article

#developer #Python #PythonCareer #PythonIdeas

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