Source: This article was Published top10-websitehosting.co.uk By GEORGIE PERU - Contributed by Member: Issac Avila

The internet holds a wealth of information, has literally billions of users worldwide but also contains some really interesting internet statistics and facts. For example, half of the U.K.’s population would be willing to receive their online shopping via drone. This information may seem strange, quirky even, however, it’s relevant in one form or another.

Whether you’re an internet user, website owner, or run a business online, it’s important to know what’s ‘going on’ around the internet, what’s trending, and what’s not. In order to help you succeed in 2018, we’ve put together a helpful and interesting selection of internet facts and statistics for you to gawp at, and share with others!

Facts and Statistics

The Internet – 2018

  • As of 1st January 2018, the total internet users across the world was 4,156,932,140 (that’s over 4 billion users)
  • 2 billion of the world’s internet users are located in Asia, where their population is just over equal to the total internet users across the world
  • In January 2018, data reveals that 3.2 billion internet users were also social media users
  • As of January 2018, the world’s population was estimated to be around 7,634,758,428. Over half of the world’s population is using the internet
  • On 10th April 2018, there were over 1.8 billion websites recorded on the internet
  • In 2018, China has the most active internet users in the world, at 772 million users. In the year 2000, this figure was around 22.5 million
  • Some of 2018’s top Google searches included iPhone 8, iPhone X, How to buy Bitcoin, and Ed Sheeran

Social Media – 2018

  • As of January 2018, Facebook alone had 2.2 billion monthly active users. Facebook was the first social media website to reach over 1 billion accounts
  • YouTube users in 2018 have surpassed the 1.5 billion mark, making YouTube the most popular website for viewing and uploading videos in the world
  • There are now over 3.1 billion social media users worldwide in 2018, which is an increase of around 13% compared to 2017
  • Comparing January 2018 to January 2017 figures, Saudi Arabia is the country with the largest social media usage increase at an estimated 32%
  • Instagram is most popular in the USA and Spain accounting for around 15% of total social media usage in these countries in 2018
  • In France, Snapchat is the second most popular social media user account in 2018, with around 18% of users countrywide
  • Facebook continues to be the fastest growing social media network, with around 527 million increase in users over the last 2 years, followed closely by WhatsApp and Instagram at 400 million
  • In 2018, 90% of businesses are using social media actively
  • 91% of social media users are using their mobile phones, tablets, and smart devices to access social media channels
  • Nearly 40% of users would prefer to spend more money on companies and businesses who are engaging on social media

Websites and Web Hosting – 2018

  • As of 2018, WordPress powers 28% of the world wide web with over 15.5 billion page views each month
  • Apache hosting servers are used by 46.9% of all available websites, followed closely by Nginx at 37.8%
  • 2018 sees 52.2% of website traffic accessed and generated via mobile phones
  • In the last 5 years, since 2013, website traffic accessed by mobile phones has increased by 36%
  • As of January 2018, Japan’s share of website traffic mainly comes from laptops and desktop computers at a measured 69%, compared to 27% on mobile phones
  • With over a billion voice search queries per month, voice is estimated to be a high trending digital marketing strategy in 2018
  • Google is the most popular search engine and visited website recorded in 2018, with over 3.5 billion searches each day
  • Website loading times are now considered a ranking factor in Google. You can find our best web hosting companies here.

eCommerce – 2018

  • In the U.K. for 2018, ZenCart has the biggest market share with over 17% of .uk web address extensions using the software provider
  • In the U.S. as of February 2018, over 133 million mobile users used the Amazon app, compared to 72 million users accessing the Walmart app
  • Nearly 80% of online shopping results in abandoned carts, but we have some handy tips to ensure you can recover your marketing strategy
  • 2018 sees a 13% increase in eCommerce sales since 2016, with the majority of sales being recorded in the U.S. and China
  • 80% of U.K buyers use online commerce research before purchasing a product online or offline
  • Under 33% of U.K. consumers want to pay more for faster delivery, but 50% said they would be willing to accept delivery via drone
  • An estimated 600,000 commercial drones will be in use by the end of 2018 in the U.K. alone

Domain Names – 2018

  • As of April 2018, there are just over 132 million registered .com domain names
  • In the month of January 2018 alone, there were 9 million registered .uk domains
  • 68 million copyright infringing URLs were requested to be removed by Google in January 2018, with 4shared.com being the highest targeted website
  • 46.5% of websites use .com as their top-level domains
  • Approximately 75% of websites registered are not active but have parked domains
  • From 1993 to 2018, the number of hosts in the domain name system (DNS) has more than doubled, reaching over 1 billion

References:

  1. https://www.internetworldstats.com/stats.htm
  2. https://www.statista.com/statistics/617136/digital-population-worldwide/
  3. http://www.internetlivestats.com/
  4. https://techviral.net/top-popular-google-searches-2018/
  5. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/
  6. https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/
  7. https://coschedule.com/blog/social-media-statistics/
  8. https://wordpress.com/about/
  9. https://w3techs.com/technologies/overview/web_server/all
  10. https://www.lifewire.com/most-popular-sites-3483140
  11. https://www.statista.com/statistics/685438/e-commerce-software-provider-market-share-in-the-uk/
  12. https://www.appnova.com/6-important-uk-ecommerce-statistics-help-plan-2018/
  13. https://www.statdns.com/
  14. http://www.internetlivestats.com/total-number-of-websites/

Categorized in Online Research

We now live in a world where it seems that everything about us is (or soon will be) tracked and recorded: what we eat, what we watch, how we socialize, what we like and dislike, our vital health statistics—and the list goes on.

Such unprecedented access to personal data presents potentially enormous opportunities to, for instance, help government officials make better policy decisions, allow businesses to operate more efficiently and profitably, streamline the use of public resources, support more personalized healthcare and drug design, and otherwise improve the overall quality of life in our society. The key to seizing these opportunities lies in our ability to convert the available data into significant decisions.

Data Science and Statistics: Opportunities and Challenges

An upcoming six-week online MIT Professional Education course, Data Science: Data to Insights, offered in partnership with the MIT Institute for Data, Systems, and Society (IDSS), will focus on analytics. But it will also address such concerns as the latest trends in machine learning: how to extract meaningful insights and preferences from customer data in general and how to ask the right questions to make better business decisions.

The Challenge

Over the past few decades, we have built infrastructure that can store and process massive amounts of data. However, we still lack the critical ability to seamlessly stitch together various pieces of data to make meaningful predictions that lead to high-impact decisions. Given the endless opportunities that can be unlocked by addressing this shortcoming, I believe this is one of the defining challenges of our times.

Educational institutions can play a leading role in addressing this important challenge. At MIT, the IDSS and its new Statistics and Data Science Center (SDSC) will help address the challenge of turning data into real-world decisions with a two-pronged approach:

  1. Educating our students to be able to work with large amounts of data and to use the tools to extract meaningful information from it. Put another way, we must educate students in all disciplines to be both data scientists and statisticians. This requires that institutions design a streamlined, interdisciplinary educational program that includes elements from engineering, mathematical sciences, and the social sciences.
  2. Developing a research program that eventually produces a statistical data-processing system that can be readily used to make all sorts of accurate predictions. Such a system needs to work with heterogeneous data sources, operate at scale, and lead to predictions that can be effectively interpreted. This ambitious program could help mobilize an interdisciplinary and exciting intellectual effort in data science and statistics for the next decade or beyond.

Thinking About Decisions

Let’s consider how decisions are made. In a typical organization, basic operational tasks depend on decisions about how to invest available resources among different competing options, with an eye on one or more objectives.

For example, the U.S. government makes such decisions while developing its budget. A trading firm invests money in different financial instruments to create portfolios with high returns and, potentially, well-understood risks. A retail organization makes decisions about which merchandise purchases will generate high revenues and profits. A household makes decisions about how to get the most out of the family income. A rational individual makes decisions about what to eat (and what not to eat) to get enough energy and stay healthy.

All such decisions, in a nutshell, boil down to making predictions, then undertaking certain optimization activities using those predictions.

How It Works: A Retail Example

Now let’s look at a concrete example involving an apparel retailer. The retailer’s primary operational problem is figuring out which products to showcase for customers, given various operational constraints such as its budget for buying inventory, the limits on its stores’ shelf space, and its suppliers’ schedules. The question of choosing which products to showcase arises at different times for different types of decisions, such as deciding which products to purchase across the chain of stores, which to ship to various locations from distribution centers, which products to discount, which to promote via e-mail, and which to show to customers when they visit stores or e-commerce sites.

All these questions, in essence, require an understanding of what people like and dislike. Some existing systems do provide these insights, and might indicate, for instance, that blue shirts are trending while red shorts have stopped selling. But how do we convert these insights into action?

Data-Driven Decision Making

Conceptually, data-driven decision making requires connecting decision variables and options to data, and then solving an optimization problem with varying objectives. Operationally, this requires building a data-processing system that might be extremely large-scale and that might need to operate in real time with three high-level components: interfaces, infrastructure, and algorithms.

Interfaces. These provide ways to deliver information to end-customers and sensors to collect information. For example, Web-based (browser) interfaces or mobile applications allow the collection of information about online customer activities. Similarly, such interfaces can help a decision maker in a retail organization interact with data and insights, as well as obtain decision support. The standardization of such interfaces has allowed for massive innovation in this domain over the past decade.

Infrastructure. The role of infrastructure is to provide a means for seamlessly storing and processing massive amounts of data. The need for such infrastructure arose naturally in the late 1990s as the Internet era picked up steam. It’s no surprise that Web-search companies have pioneered basic innovations. Interestingly, Web search, a seemingly simple feature, has led to the development of a generic scalable storage and computation infrastructure. That, in turn, has been the primary reason for recent exciting innovations in scalable computation and data processing.

Algorithms. Data-processing algorithms transform the raw data collected into valuable insights and decisions. Appropriate models are used to connect that data to decision variables. For example, when raw data is generated by people, it may make sense to use a behavioral model to connect that observed data to decision variables. The resulting algorithms use the computation and storage infrastructure, based on the data obtained through the interface, and produce end results that can be delivered to the end user through the interface.

Yet a major challenge is enabling the development of data-processing algorithms for everyone. Unlike the availability of standardized interfaces or a generic computation and storage architecture, we are far from having a generic, data-processing, algorithmic architecture.

Let’s revisit the retail example above. Specifically, consider the decision task of which products to show to customers when they are visiting the e-commerce site—that is, how do we personalize each customer’s experience? Naturally, this depends on data about the specific customer, as well as the data collected about others.

That data is collected through a customer’s browsing history and clicks on the e-commerce website, past purchases, and other online activity gleaned through our Web and mobile interfaces. It is likely stored in a storage infrastructure. It is transformed into real-time, personalized decisions via potentially sophisticated data-processing algorithms that use behavioral models from the social sciences, along with methods from mathematical statistics and machine learning. The data-processing algorithms use the computation infrastructure to be able to make such decisions in real time. In this way, personalized decisions are delivered to the customer through the interface.

Key to building this type of personalization or recommendation system is having access to a skilled team of data scientists and statisticians who can identify appropriate statistical methods and behavioral models to develop data-processing algorithms. They can then design human-friendly interfaces that can collect useful data and subsequently deliver decisions. While this is an expensive undertaking, some of the largest retailers have already taken this route.

On the other hand, the personalization/recommendation system has specific functions that take a very similar form across organizations. That similarity has allowed the development of generic recommendation systems. Therefore, many retailers end up purchasing such systems from outside vendors who simply plug in the personalization system through the interfaces.

Closing the Loop

As discussed previously, one major challenge is going from data to decisions. We already have a lot of data—and we have a good infrastructure to store and process it—but we need to figure out how to process it. The discussion of the personalization/recommendation system explains precisely the two approaches that we can use simultaneously to address this challenge.

First, we must enable organizations to build their team of skilled data scientists. Second, we should develop a generic data-processing algorithmic architecture. Specifically, this data-processing architecture needs to focus on developing a generic prediction system. That’s because a decision-making system basically has two components: predicting the unknowns and using the predictions to perform optimization. Over the past few decades, significant progress has been made to develop the theory and practice of optimization. However, we still can’t define what the generic and universal prediction problem is.

IDSS, SDSC, and ‘Data Science’

MIT launched the IDSS to address societal questions emerging over the next century. While many of these issues involve multiple disciplines, they are all connected through one common challenge: data-driven decisions. To develop an education program and enable research in data science and statistics at the IDSS, MIT created the SDSC under the IDSS umbrella.

We will help address the challenge of transforming data into decisions by enabling the two approaches that I have described through both the SDSC and the IDSS. Specifically, the SDSC will educate sophisticated data scientists and statisticians through interdisciplinary educational programs. The IDSS will provide an interdisciplinary research environment that will allow its members to undertake ambitious research programs in statistics and data science.

Meanwhile, our new six-week, online course

, “Data Science: Data to Insights”, which begins October 4, will share the latest information about ways to apply data science techniques to more effectively address your organization’s many challenges. To learn more about how to create your company’s data-analysis future, please visit the course registration page.

Author : Devavrat Shah

Source : https://www.technologyreview.com/s/602300/data-science-and-statistics-opportunities-and-challenges/

Categorized in Science & Tech

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