Advantages Of Using WordPress

While selecting web platform it is very necessary to understand the pros-cons of the platform. Following are the advantages of WordPress

  1. Ease of Use

WordPress is very easy to use and has an intuitive interface.  Adding new pages, blog posts, images, etc. on a regular basis is a breeze and can be done quickly.  Because the technology is so simple, time spent on formatting is greatly reduced.

  1. Manage Your Website from Any Computer

WordPress is browser-based. You can log in from any Internet-connected computer and manage your site.

  1. No HTML Editing or FTP Software Required

WordPress is a self-contained system and does not require HTML editing software (such as Adobe Contribute or Dreamweaver). You can create a new page or blog post, format text, upload images (and edit them), upload documents, video files, image galleries, etc. all without the need for additional HTML or FTP software.

  1. Search Engines Love WordPress Sites

The code behind WordPress is very clean and simple, making it easy for search engines to read and index a site’s content. In addition, each page, post, and image can have its own meta tag keywords, description, and title, and be optimized for specific keywords, allowing for very precise search engine optimization.  You can also use tags to further enhance your search engine optimization efforts.

  1. You Have Control of Your Site

No more waiting for your web designer to make simple updates to your site. With WordPress, you have control of nearly every aspect of your site and can easily make those simple updates yourself.

  1. The Design of Your Website is 100% Customizable

WordPress acts as the engine for your website. The look and feel of the site can be 100% customized so your brand can shine through on your site and provide a unique experience to your visitors.

  1. A Blog is Built-in and Ready to Go

Since WordPress was originally created as a blogging platform, blogging capabilities are built-in and are easy to integrate, if desired. Setting up RSS / email subscriptions to your blog, commenting capabilities, and automatically adding the most recent blog posts to other pages of the site (your home page, for example) are also very simple to set-up, and help to extend your company’s reach and make your site more dynamic and interactive.

  1. Extend the Functionality of Your Site with Plugins

Want to add an event calendar, video gallery, Twitter Feed, Facebook Fan Box, and more to your site? WordPress makes this possible with plugins, most of which are free or very reasonably priced.

  1. Your Site Can Grow as Your Business Grows

WordPress sites are very scalable. You can have hundreds of thousands of pages or blog posts on your site and the performance of the site will not be compromised in the least.

  1. Have Multiple Users

As an administrator of a WordPress site, you can set-up multiple users for the website and assign access levels and capabilities to each user.

Virtual Reality Experience By Adobe Soon

No one wants to miss the experience of Virtual Reality. During Adobe’s recent summit event held in London, the company revealed a new virtual reality (VR) application that it has in the works by the name of Project New View.

Adobe’s Project New View will help the marketing Professionals in the future to make decisions by analyzing through VR mode. Check its glimpse below and comment you opinion.

Check The Top 10 VR Trends We’ll See In 2018

Decision Tree Algorithm in Data Mining

Decision trees, and data mining are useful techniques these days.A decision tree is a hierarchical relationship diagram that is used to determine the answer to an overall question. It does this by asking a sequence of sub-questions related to that question. Each branch of the diagram represents a possible choice or answer to a specific sub-question. And each sub-question iteratively reduces the number of remaining choices, or answers, until only the correct one for the overall question, in that particular situation, remains.

Let’s look at an example. In the diagram above, the overall question is, ‘Is the weather good enough to go outside?’ This isn’t a simple question to answer. There are a number of factors to consider. Each bubble in the diagram represents a factor, or sub-question, and each line represents a choice or answer to the sub-question above.

So the first sub-question we ask is, ‘Is it windy?’ If it is, we go down the left of the diagram, if not, we go down the right. Let’s say it is windy. That takes us to the ‘What is the outlook?’ sub-question. If the answer is sunny, we go down the left, if overcast down the center, and if rainy, down the right. Let’s say that it is sunny, so we go down the left. Then the next sub-question is ‘What is the humidity?’. If the humidity is less than 80 percent, the answer to the overall question is ‘Yes’. And if the humidity is greater than 80 percent, the answer is ‘No.’

What is Data Mining?

Data Mining is the process of identifying trends in large data sets.
Steps are as following:

  1. Business understanding
  2. Data understanding
  3. Data preparation
  4. Modeling
  5. Evaluation
  6. Deployment

The data is usually collected and stored in data warehouses.
Then we apply suitable data mining algorithms for identifying trends.
Most popular algorithms are clustering and regression trees.

Data Mining can be done for:

  1. Mining for patterns
  2. Mining for associations
  3. Mining for correlations
  4. Mining for clusters
  5. Mining for predictive analysis

What Is Deep Neural Networks?

A deep neural network (DNN) is an artificial neural network (ANN) with multiple hidden layers between the input and output layers. DNNs can model complex non-linear relationships. DNN architectures generate compositional models where the object is expressed as a layered composition of primitives. The extra layers enable composition of features from lower layers, potentially modeling complex data with fewer units than a similarly performing shallow network.

Deep architectures include many variants of a few basic approaches. Each architecture has found success in specific domains. It is not always possible to compare the performance of multiple architectures unless they have been evaluated on the same data sets.

DNNs are typically feedforward networks in which data flows from the input layer to the output layer without looping back.

Recurrent neural networks (RNNs), in which data can flow in any direction, are used for applications such as language modeling. Long short-term memory is particularly effective for this use.

Convolutional deep neural networks (CNNs) are used in computer vision. CNNs also have been applied to acoustic modeling for automatic speech recognition (ASR).

Reference:https://deeplearning4j.org/neuralnet-overview

Many  application is developed for Deep Learning which are  very help to other famous applications.

What Is Data Wrangling?

Data wrangling is the process of cleaning, structuring and enriching raw data into a desired format for better decision making in less time. In other words, it is the process of cleaning and unifying messy and complex data sets for easy access and analysis.

  1. With the amount of data and data sources rapidly growing and expanding, it is getting more and more essential for the large amounts of available data to be organized for analysis.
  2. This process typically includes manually converting/mapping data from one raw form into another format to allow for more convenient consumption and organization of the data.

The goals of data wrangling:

  1. Reveal a “deeper intelligence” within your data, by gathering data from multiple sources
  2. Provide accurate, actionable data in the hands of business analysts in a timely matter
  3. Reduce the time spent collecting and organizing unruly data before it can be utilized
  4. Enable data scientists and analysts to focus on the analysis of data, rather than the wrangling
  5. Drive better decision-making skills by senior leaders in an organization

The key steps to data wrangling:

  1. Data Acquisition: Identify and obtain access to the data within your sources
  2. Joining Data: Combine the edited data for further use and analysis
  3. Data Cleansing: Redesign the data into a usable/functional format and correct/remove any bad data

How to Remove Duplicate Data in R

During the processing of data cleansing, it is often required to remove duplicate values from the database. A very useful application of subsetting data is to find and remove duplicate values. R has a useful function, duplicated(), that finds duplicate values and returns a logical vector that tells you whether the specified value is a duplicate of a previous value. This means that for duplicated values, duplicated() returns FALSE for the first occurrence and TRUE for every following occurrence of that value, as in the following example:

> duplicated(c(1,2,1,6,1,8))
[1] FALSE FALSE TRUE FALSE TRUE FALSE

If you try this on a data frame, R automatically checks the observations (meaning, it treats every row as a value). So, for example, with the data frame iris:

> duplicated(iris)
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [10] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
....
 [136] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE

If you look carefully, you notice that row 143 is a duplicate (because the 143rd element of your result has the value TRUE). You also can tell this by using the which() function:

> which(duplicated(iris))
[1] 143

Now, to remove the duplicate from iris, you need to exclude this row from your data. Remember that there are two ways to exclude data using subsetting:

  • Specify a logical vector, where FALSE means that the element will be excluded. The ! (exclamation point) operator is a logical negation. This means that it converts TRUE into FALSE and vice versa. So, to remove the duplicates from iris, you do the following:

> iris[!duplicated(iris), ]

Specify negative values. In other words:

> index <- which(duplicated(iris))
> iris[-index, ]

In both cases, you’ll notice that your instruction has removed row 143.

How To Connect Tableau to Adobe Analytics

By default, there is no functionality in Tableau to connect Adobe Analytics or vice versa.Till now no Connector is available for data transfer developed by Adobe or Tableau.

Option available for now is to get file processed from Data Warehouse which is supported by Tableau i.e

  • Tableau Workbook (.twb)
  • Tableau Packaged Workbook (.twbx)

But now cognetik developed a connector which is made available to analysts & marketers worldwide.

It’s free to use, for now, it’s got a fairly easy set-up, it takes a few minutes to import the data and it allows for easy refreshes.

In addition to Adobe, works fine with Facebook Ads, Facebook Pages, Adwords, Bing, Kochava, Youtube and Twiter.

 

How To Install R & R-Studio

R is a fundamental open source, case-sensitive programming language. RStudio is an active member of the R community and an integrated development environment (IDE)for R.

You need to install both R and R-Studio on your system before actually getting started with R. In this page, you will be guided through the installation process and get introduced to both of them.

Install R

Step 1: Download the package relevant to your system (Windows or Mac or Linux) from the Comprehensive R Archive Network (CRAN) website.

Step 2: Install R like you normally install any new software package.

Now, Install R-Studio

Step 1: Download the R-Studio Desktop package from the R-Studio website.

Step 2: Install R-Studio using user’s setup process.

Before you move on, make sure you have installed both R and R-Studio on your system. In this lecture, you will be introduced to different components of R-Studio.