In this SEO tutorial, I show you how to apply Search Engine Optimization to your WordPress Website using RankMath. Using this free plugin you will be found better in the search results of Google and other search engines. With RankMath you can make sure your search results look clean and are straight to the point.
I will show you how to download and install the free version of RankMath. Then we will configure RankMath and create a Google Analytics and Google Search Console account so you can see all the important statistics in the backend of your website. Using the 404 monitors we will make sure that all your broken links are detected and fixed. With RankMath we can create a Sitemap that will enable Google to index our websites faster.
We will optimize the way all our pages look in the search results. How to go from this. To this. And we will make sure that all our upcoming pages, posts, and product pages are optimized by default.
When we have done that I will show you how to optimize your individual blog posts and pages. And using RankMath you can see every step you need to take in order to be found better. We just have to follow all the suggestions from RankMath and I will walk you through them.
There is so much more we will cover. How to read the website statistics. How to analyze and optimize our website even further. Even though we already get so much with the free version of Rank Math: At the end of the tutorial, I will talk about the features of RankMath Pro. In the description of the video, I show you everything we will cover in the tutorial. If you want to go to a specific place in the tutorial, you can click on the timestamp and go directly to that part of the video.
00:00 RankMath Tutorial Overview
01:52 Download RankMath For Free
The RankMath Setup Wizard
04:28 Setup RankMath
05:33 Import Data From Other Plugins (Like Yoast SEO)
07:50 Setup Google Search Console and Google Analytics
10:10 Setup Your Sitemap
12:15 NoFollow Links
13:51 The RoleManager
14:50 Redirect Broken Urls With The 404 Monitor
16:36 Schema MarkUp
20:23 Introduction to RankMath SEO
21:23 Site Analytics
21:54 SEO Performance
22:29 Keywords (And positions)
25:10 Get More Statistics With RankMath Pro
RankMath General Settings
35:53 Optimise Images Using RankMath
38:32 Google Search Console
42:04 RSS feed Settings
44:07 RankMath WooCommerce
45:37 The 404 Monitor
Titles & Meta Settings
49:08 Global Meta Settings
50:13 Choose a Separator Character
51:17 Configure The Local SEO
51:50 Setup Social Media Settings
52:22 RankMath Homepage SEO Settings
52:39 Archive Search Results Display Settings
54:53 Post SEO Settings
56:15 Page SEO Settings
57:24 WooCommerce Product Page SEO Settings
58:17 Category SEO Settings
01:00:04 Configure Your SiteMap For Google
01:02:04 The 404 Monitor And Redirection
01:06:31 Use Redirections
RankMath SEO Analysis
01:07:32 Analyse Your Website Using RankMath SEO Analysis
Optimize Your Blogposts And Pages
01:16:26 Optimise Individual Posts And Pages
01:17:32 Create A Focus Keyword
01:18:49 (Optional) Use Ahrefs To Find The Best Keywords
01:19:44 Optimise Your Blog post Title
01:20:35 Adjust The Google Search Result Content
01:23:24 Add More Focus Keywords
01:25:39 Optimise The Images In Your Website
01:26:53 Create Pillar Content In RankMath
01:28:04 Index Your Posts
RankMath Schema Markup
01:29:05 Schema Markup In RankMath
01:30:33 Schema Markup For Product Pages
01:35:38 Optimising Your HomePage
01:40:36 Get RankMath Pro
01:43:51 Export And Import RankMath Settings
01:45:17 The Pro’s about RankMath Pro
01:46:33 Configure The Extra Options Using RankMath Pro
01:47:28 RankMath Pro Redirections
01:49:08 Bulk SEO Actions
01:50:29 RankMath Pro Schema Markup
01:52:11 RankMath Pro Analytics
01:52:31 More Options With Schema Markup Pro
01:55:44 RankMath Pro Local Business
01:57:48 Thank You
It’s 2021, everything is getting replaced by a technologically emerged ecosystem, and mobile apps are one of the best examples to convey this message.
Though bypassing times, the development structure of mobile app has also been changed, but if you still follow the same process to create a mobile app for your business, then you are losing a ton of opportunities by not giving top-notch mobile experience to your users, which your competitors are doing.
You are about to lose potential existing customers you have, so what’s the ideal solution to build a successful mobile app in 2021?
This article will discuss how to build a mobile app in 2021 to help out many small businesses, startups & entrepreneurs by simplifying the mobile app development process for their business.
The first thing is to EVALUATE your mobile app IDEA means how your mobile app will change your target audience’s life and why your mobile app only can be the solution to their problem.
Now you have proposed a solution to a specific audience group, now start to think about the mobile app functionalities, the features would be in it, and simple to understand user interface with impressive UI designs.
From designing to development, everything is covered at this point; now, focus on a prelaunch marketing plan to create hype for your mobile app’s targeted audience, which will help you score initial downloads.
Boom, you are about to cross a particular download to generate a specific revenue through your mobile app.
#create an app in 2021 #process to create an app in 2021 #a complete process to create an app in 2021 #complete process to create an app in 2021 #process to create an app #complete process to create an app
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.
[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b)Packages into Memory
#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials
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
#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials
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:
Common symptoms of messy data include data that contain:
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:
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!
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.
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:
Once the package is installed, load it to memory:
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…
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.
#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials
In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.
On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.
But when it comes to comparing blog post performance, things get a bit trickier.
For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).
But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!
My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.
However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.
But then, I learned R, and realized that there was a much better way.
In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!
To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.
Not yet familiar with the basics of R? We can help with that! Our interactive online courses teach you R from scratch, with no prior programming experience required. Sign up and start today!
You’ll also need the
stringr packages installed — which, as a reminder, you can do with the
Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:
post_url: the page path of the blog post
post_date: the date the post was published (formatted m/d/yy)
category: the blog category the post was published in (optional)
title: the title of the blog post (optional)
Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!
For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.
#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials