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Learn How To Create Youtube Ads Step By Step For Complete Beginners.
Create A Google Ads Account: https://ads.google.com/home/
Get A Professional Youtube Ad Created https://www.darrelwilson.com/fiverr
In this youtube ads tutorial, ill show you to advertise on youtube. You dont need a youtube video to get started and you can market your own website. Il show you how to use display ads and also youtube ads and talk about how you can set different audiences and also advertise based off location.
Youtube TimeStamps
00:01:18 Creating An Account
00:03:20 Tools And Billing
00:04:10 Creating A Display Ad
00:21:40 Creating A Video Ad
Checkout my other Multilingual Wordpress Tutorial Youtube Channels!
Spanish Channel: https://www.youtube.com/channel/UC1Sm53FofDOVA5T0pzyebEA
Arabic Channel: https://www.youtube.com/channel/UCDxCp7gMbzmNIjUqOCu1xcg/videos
Hindi Channel: https://www.youtube.com/channel/UCjTje7OJghnjEJ8d4llefQw
Portuguese Channel: https://www.youtube.com/channel/UCOs-QXBdlmT0YL3I-bnyeug
French Channel: https://www.youtube.com/channel/UCLEH_xAh0VxCTSq1AQBYPsg
German Channel: https://www.youtube.com/channel/UCTkmZD2BCJpedWe4Hb5QVLw
Thanks for watching party people! Feel free to visit my website for more info: https://www.darrelwilson.com
#wordpress
1626614768
Affiliate Marketing Tutorial for Beginners with Google Ads (Low Budget)
Are you struggling to get success on Affiliate marketing? If you’re interested to start affiliate marketing using Google Ads then you should complete my tutorial. It’s an evergreen method to start affiliate marketing and get success. If you’re able to run your ads campaigns then finish my tutorial. Step by step I have described everything here.
Why This Tutorial is Recommended?
-You can start affiliate marketing today
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-100% super targeted traffic
-Don’t need to make website post or make videos
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-Totally newbie-friendly
-Just $20/25 is needed to invest per day
-Don’t need to wait to get results
-It’s an evergreen affiliate marketing method
After watching this tutorial I hope you can apply it easily. If you can grab affiliate marketing by applying Google ads once you can get results for a lifetime. It’s an evergreen method for Google ads lovers.
Watch Complete Tutorial Here >>
#affiliate marketing tutorial for beginners with google ads #affiliate marketing tutorial for beginners #affiliate marketing tutorial #google ads tutorial
1599097440
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
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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-173bb26184b)
Packages[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
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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 readxl
package. 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:
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.
#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials
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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