feoloap astra

feoloap astra

1619888764

VueJs - Best VueJs Tutorials and Courses

Choosing the right framework for your product is like buying your first house. You must make a decision that will affect you in the long term and that cannot simply be undone once you’re bored or just aren’t fond of it anymore. And all the while you need to be aware that once you make the investment, you won’t able to afford a new one anytime soon.

Even after you finally find a house you feel you’re ready to pull the trigger on, you still may have some reservations: you love the facade, but the backyard could be a bit bigger. You like the colors of the walls, but you’d prefer a slightly different shade. Plus, the kitchen kinda feels outdated. Well, let’s be honest—there simply isn’t a perfect house out there that would just have it all. Especially when it’s your first one. Even if you decided to build one from scratch, there would always, always be room (room, get it?) for improvement.

And this is exactly how it feels when you have to choose between Vue, Angular, React, maybe Backbone, Aurelia, or some other library. Some of them feel good for your product, some of them don’t. They all have their flaws and all have aspects to them you just love.

(We won’t even try to mention the ever-changing landscape of JavaScript, because it will make you question the very sense of even making such a decision, and then flip the table over once you have it set every now and then.)

Nevertheless, it’s truly awesome that nowadays we’ve got so many viable technology options to choose from, and thus so many avenues we can choose to help us solve our users’ problems.

Most people who follow Monterail know that we personally favor Vue.js and like to suggest it as the technology of choice to our new clients. No two products are the same, however, and we like making educated decisions, unbiased by external factors.

In 2020, Vue.js was (again) in the top 3 trendiest repositories in the JavaScript world, receiving over 176,444 stars on GitHub throughout the year, far surpassing the numbers for Node or Angular. With every passing month, Vue gains more traction and records ever high downloads numbers. There are obviously reasons for that, and we will be discussing them in Chapter One.

When you first start working with it, Vue may at first glance seem that perfect house you’ve been looking for. BUT. That’s right. There are always some “buts”—although in this case, they become noticeable primarily when Vue is compared to other technologies, as covered by Chapter Five.

So, before you decide to add Vue.js to your technology stack, read this.

1
What Makes Vue.js Unique?
2
The Evolution of Vue.js
3
Research and Data From Vue.js Developers
4
Which Companies Use Vue.js?
5
Vue.js vs. the Rest of the JavaScript world
6
What’s the Future of Vue.js?
1
What Makes Vue.js Unique?
“What’s all the buzz about?” you ask? Vue.js has been growing like crazy and there seems to be no stop to its rapid progress. And this is what makes it a one-of-a-kind solution.

We first encountered Vue.js in late 2015 as we were looking for a viable alternative to AngularJS and React.

AngularJS already felt outdated back then and we weren’t fans of React mostly due to the fact we weren’t initially sold on the whole JSX idea. Vue drew our attention due to its super friendly documentation and over the next couple of months our developers researched Vue extensively within several internal projects to see whether it would fit with the rest of Monterail’s stack.

Vue has seen a tremendous rise in popularity since we’ve first heard about it. Although it started out small, it has since been adopted by companies and business entities across the board, big and small, including major industry players such as Alibaba, GitLab, Sainsbury’s, Codeship, and Baidu.

Vue is flexible, easy to learn, and powerful. Its ecosystem is still growing and it already has everything you need to build all kinds of applications (yes, mobile apps, too).

If you’re thinking now that you can say the same things about numerous other libraries, you might be right. You can probably build your dream app with any of them and you will meet your goals. Your users will never know if your product is Vue-based or any-other-framework-based and let’s be honest—most of them don’t really care.

And yet choosing a framework is a hard task, because it will strongly influence your in-house production team (or your offshored team, if you prefer it that way).

So what are Vue’s main selling points that make developers and business owners select it as their technology of choice? What is so unique about it?

What is GEEK

Buddha Community

VueJs - Best VueJs Tutorials and Courses
bindu singh

bindu singh

1647351133

Procedure To Become An Air Hostess/Cabin Crew

Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

 

Physical and Medical standards –

  • Females must be 157 cm in height and males must be 170 cm in height (for males). This parameter may vary from one airline toward the next.
  • The candidate's body weight should be proportional to his or her height.
  • Candidates with blemish-free skin will have an advantage.
  • Physical fitness is required of the candidate.
  • Eyesight requirements: a minimum of 6/9 vision is required. Many airlines allow applicants to fix their vision to 20/20!
  • There should be no history of mental disease in the candidate's past.
  • The candidate should not have a significant cardiovascular condition.

You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

As can be seen from the preceding information, a 10+2 pass is the minimal educational need for becoming an air hostess in India. So, if you have a 10+2 certificate from a recognized board, you are qualified to apply for an interview for air hostess positions!

You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).

So That I may recommend, joining Special Personality development courses, a learning gallery that offers aviation industry courses by AEROFLY INTERNATIONAL AVIATION ACADEMY in CHANDIGARH. They provide extra sessions included in the course and conduct the entire course in 6 months covering all topics at an affordable pricing structure. They pay particular attention to each and every aspirant and prepare them according to airline criteria. So be a part of it and give your aspirations So be a part of it and give your aspirations wings.

Read More:   Safety and Emergency Procedures of Aviation || Operations of Travel and Hospitality Management || Intellectual Language and Interview Training || Premiere Coaching For Retail and Mass Communication |Introductory Cosmetology and Tress Styling  ||  Aircraft Ground Personnel Competent Course

For more information:

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Address:     Aerofly International Aviation Academy, SCO 68, 4th Floor, Sector 17-D,                            Chandigarh, Pin 160017 

Email:     info@aerofly.co.in

 

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Hire Dedicated VueJS Developers

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Willie  Beier

Willie Beier

1596728880

Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

Table of Contents

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Jeromy  Lowe

Jeromy Lowe

1599097440

Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

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Tutorial: Loading and Cleaning Data with R and the tidyverse

1. Characteristics of Clean Data and Messy Data

What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:

  • Free of duplicate rows/values
  • Error-free (e.g. free of misspellings)
  • Relevant (e.g. free of special characters)
  • The appropriate data type for analysis
  • Free of outliers (or only contain outliers have been identified/understood), and
  • Follows a “tidy data” structure

Common symptoms of messy data include data that contain:

  • Special characters (e.g. commas in numeric values)
  • Numeric values stored as text/character data types
  • Duplicate rows
  • Misspellings
  • Inaccuracies
  • White space
  • Missing data
  • Zeros instead of null values

2. Motivation

In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:

  • rollingsales_bronx.xls
  • rollingsales_brooklyn.xls
  • rollingsales_manhattan.xls
  • rollingsales_queens.xls
  • rollingsales_statenisland.xls

As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!

3. Load Data into R with readxl

Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse readr package function read_csv() is the function to use (we’ll cover that later).

Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:

The Brooklyn Excel file

Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the readxlpackage. We specify the function argument skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:

library(readxl) # Load Excel files
brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)

Note we saved this dataset with the variable name brooklyn for future use.

4. View the Data with tidyr::glimpse()

The tidyverse offers a user-friendly way to view this data with the glimpse() function that is part of the tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:

install.packages("tidyverse")

Once the package is installed, load it to memory:

library(tidyverse)

Now that tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:

glimpse(brooklyn)
## Observations: 20,185
## Variables: 21
## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "…
## $ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA…
## $ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 …
## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "…
## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6…
## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,…
## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T…
## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112…
## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3…
## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22…
## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1…
## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "…
## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, …
## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…

The glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.

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