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Gitlab vs GitHub: A Comparison

Gitlab vs GitHub: A Comparison

Knowing which software is best for your needs is crucial when wanting to use a single application for any project planning and source code management. There are many different ones from which to choose, but here we will take a deeper look at Gitlab and GitHub, and how they compare overall.

First, understanding that both Gitlab and GitHub are web-based Git repositories is crucial to understanding what makes them different from others on the market. Git, at the core, is essentially a software management component that allows for the user to track changes made to not just the software, and programs used, but also any information contained within.

By having this capability, Git safeguards that no code overlaps or conflicts with itself, plus it allows for changes to be made within the code without the programmer having to rework the entire base code. Without this version control system, the idea of collaborative work within a team, with frequent modifications and any technical needs, would be virtually impossible.

Git allows for all changes to be kept within a repository, this allows for a more streamlined approach, the ability to code more than one project at a time, and can greatly reduce human error as it is easy to revert back to an old code or clear a coding mistake.

Each developer creates work within their own branch, or repository, apart from the master branch; these individual branches can be merged together whenever needed. Git allows for other services and tools to be integrated with it, which is what makes this control system so universally appealing to developers.

Gitlab, specifically, advertises itself as a complete DevOps platform, one that due to the fact it delivers in a single application, can change the way software is developed. Its goal is to shorten the systems’ development life cycle, (SDLC) but will not sacrifice the quality of the end product.

GitHub, on the other hand, is a development platform that allows for projects to be stored by developers. It has access to many features, task management, bug tracking, wikis, and more. It allows its users to have access to social networking-like features; it is viewed more as social coding.

Similarities

Let’s focus on some of the key similarities they both share:

  • Both offer a highly supportive community of developers on each platform. This means that there is no shortage of updates and maintenance, key for the everchanging world of coding.
  • Multiple issues can be assigned at a time, which means multiple collaborators may work on a single project at once.
  • Tracking is available with both platforms, which includes the ability to enable status changes and the capability to assign an owner to each issue that is being worked on.
  • Bug reports can be sent immediately to both platforms.
  • Labels are used in both. They can help to categorize any issues, make a request for a merge of branches, and can help immensely with tracking all information.
  • Descriptions of issues and merge requests can also be added simply by selecting a template and adding a description.
  • Merge requests need to be approved by one or more people involved. There is a pre-determined list of who is allowed to approve the request for a merge.
  • Each platform uses a Wiki that is built directly into each project, it is a separate system and is seen as a separate Git repository.
  • In either platform, there is the capability of its users to not only collaborate on a source branch but also allow for it to be edited into a fork (a copy of the original). This is a big deal as it lets the maintainers make small changes before any merge happens.

Differences

Some of the differences can be quite intricate and depending on your needs, you may want to research each platform further. But here is a brief summary that may help you choose between the two:

  • GitLab’s permission is based on roles, GitHub allows developers to grant read or write access to various repositories.
  • GitLab offers inner sourcing and internal repositories compared to GitHub.
  • While GitHub is limited with regards to importing projects and issues from sources, it does have a tool known as GitHub Importer to import any data. GitLab can export wikis, uploaded projects and repositories, issues, etc., whereas GitHub is more restricted in its exports.
  • GitHub requires a third-party integration which requires an external application, GitLab is described as a seamless deployment, no third-party needed.
  • GitLab features monthly updates with new features and improvements.
  • GitLab allows developers to move issues between projects, this includes links, comments, history, all of which can be copied and referenced to within the original issue and any future issues.

Ultimately, there are more features that vary slightly with each platform, but you may want to keep in mind that Gitlab can be run on your own servers and provides unlimited private repositories for free. And while GitHub does not offer them for free, it does provide a full history of a thread of comments, compared to Gitlab which does not.

Tutorial: How to Download from GitHub

How to Download from GitHub

How to Download from GitHub

GitHub is founded on Git, a repository hosting service, but GitHub includes unique features and provides a web-based graphical interface, allowing for its users to interact through the use of graphical icons.

With each task, it allows its users to use key features, such as bug tracking, wiki space, and a variety of other basic task management tools. It also provides the developers with tools to complete what is known in the software development life cycle (SDLC), as social coding.

GitHub allows the developer to work on a project with multiple collaborators. In order to work on any sort of project you will need to understand how to download from GitHub, so, let’s dive in and take you through the process, step by step.

First, you need to acknowledge the purpose of your download. For example, is it for viewing purposes or do you plan to experiment with the file? The first intention is easier than the second, so let’s begin with that.

Codebases within a public repository are typically open to the public as they are an open-source; it can be downloaded to your computer as a ZIP file. You will need to access a public codebase, which by the way you don’t need a user account to do so, and find a green button that says, Clone or Download.

Once clicked, simply select Download Zip, all files will begin to download to your computer; this should be easily accessible within your download folder on your computer. You may even be able to access the downloads at the top of your internet navigation menu with the symbol of an arrow pointing down to a line.

Within your download folder, you will need to locate the ZIP file, this file will need to be unpacked. Simply right-click on the file and select unzip or uncompress. Don’t forget to select the folder in which you want the file to be saved.

Now, if you want to do more with the files, as in, actually work with them, you will need to select a different option of how to download from GitHub. This will require you to fork the project or make a copy of the repository on which you intend to work.

Forking a repository gives you benefits for which downloading as a ZIP file simply doesn’t allow; you are free to work on the file without changing or affecting the original. A real benefit to approaching the file in this manner also allows for the less experienced coder to gain some experience in coding.

By creating a fork of the original you may also work on bugs that the original contains, suggest changes to a project, or attempt to add a new feature to the project or repository. But, to do any of this, you need to understand how to fork a project.

If you don’t already have an account, open an account with GitHub, don’t worry, it is free. This is necessary for you to have a place to store your fork. Once this is complete, select the public repository you want to make a copy of, then, in the top-right corner select the button that says Fork.

Depending on the size of the file, you will soon find yourself with a copy of the project. You may find the project within your GitHub account, under your username. You may also locate it under your profile where you will select the button Your Repositories.

Now that you have forked the file, you are free to work on it to your heart’s content. If you feel as though you have made a worthwhile change that you would like to discuss with the developer you may create a Pull Request, which would allow you to discuss your changes further with the project owner.

If they are happy with your change or suggestion the two projects can be merged into the original code. Whether you decide to view the file or experiment with the file, you will find that the download process is less complicated than you may expect.

Tutorial: How to Delete A Repository in GitHub

How to Delete A Repository in GitHub

How to Delete A Repository in GitHub

GitHub is a development platform that is used by developers to store projects. It is founded on Git, and is used mainly for code development but helps to manage the software development cycle while providing access to a variety of features, such as bug tracking, task management, and wikis.

A repository is where all of your projects’ files are stored, including any revisions you have made. Within the repository, you can manage the work and discuss the project in detail with any or all collaborators. Each repository can be owned by one person or can be shared with others.

If you are the owner of a specific repository you can choose to give other collaborators access, or if the repository is owned by an organization, you may choose to give members within the organization access permission which will allow them to also collaborate on the work within the repository. They can be private or public.

There is no limit to the number of public repositories a person or an organization can own, nor is there a limit to the number of collaborators that may have access. If, however, you are choosing to use GitHub Free, while you can have unlimited access to private repositories you will have a limit on the number of collaborators, three. To add more, you will need to upgrade to GitHub Pro, Team, or Enterprise Cloud.

So, what happens when you have a repository within GitHub that you no longer need or want? You will want to delete it, along with all of its stored files from your GitHub account. Although it does require a few steps, they are relatively easy and as follows.

First, you will need to log into your GitHub account, this will require your login name and password. Depending on how many repositories you have, it will affect the ease of you deleting the repository you want.

For example, the landing page will contain a box that displays your repositories. If you know the name, as you have a limited number, you could just do a quick search and delete it that way. But, let’s assume that you have a lot of repositories in GitHub and take you another way.

On the landing page, you will also notice your avatar, this may be an actual picture, possibly of your organization or it could be a generic avatar. By clicking on this page, it will bring up a dropdown menu where you will select your profile page.

Once you are within your profile you will then click on the tab within the offered menu that is labeled Repositories. After you click on this you will have a couple of options, the first is to scroll all the repositories listed, but this could be tedious depending on the number you have.

The other choice is to enter the name in the search bar and select the one that you want. Keep in mind that there are different types of repositories, or you may have some with similar names, so you may also need to select the type from the offered list.

The options include All, Public, Private, Sources, Forks, Archived, and Mirrors. When you have located the name of the repository you wish to delete you can click on it and you will then be taken to the page for that specific repository.

Below the name of the repository, you will see a settings button. At the bottom of the page, you will need to enter the Danger Zone (cue the theme song to the movie Top Gun). Within this zone, there are a variety of options, from transferring ownership, archiving, and what you ultimately want, Delete This Repository.

Of course, there is a safety precaution the moment you select this choice; it will ask you if you are sure, to which, if you are, you must type in the name of the repository name to double ensure that this is your ultimate desire.

Once you have typed in the name you will then click on the button that says, I Understand the Consequences, Delete This Repository. Once complete, you will be taken back to the home page with a confirming message that the repository you have selected has indeed, been deleted. That’s it, you’re golden.

Machine Learning:  The 3 Ways Modern Computing Is Helping Marketers

machine learning - Machine Learning:  The 3 Ways Modern Computing Is Helping Marketers

Machine learning has been the term on the tongues of every tech-savvy businessperson in recent years, especially as major tech players like Apple and Google have made major moves to implement it into their consumer-facing technologies.  Even with the excitement around the subject, like Apple’s announcement that the iPhone XS would include a devoted machine learning core, many people don’t yet know how machine learning will affect their life and business.

We think it’s important for businesspeople to understand the practical side of technologies that are rising to the forefront of modern business, so with that in mind, we’ll answer the questions “what is machine learning” and “how can it help marketers?

Machine Learning Can Hone Your Audience

One of the biggest mistakes you can make in marketing is missing your target audience.  Every year, thousands of products sell less than they should simply because a marketing professional missed some minute detail about the audience or demographic for their product.  Machine learning is helping to make that much less likely.

Because machine learning is centered around putting adaptive data to good use, marketing applications abound, particularly when coupled with the use of demographic data that already exists.  Social media sites have generated endless valuable information in recent years, but making sense of what is largely referred to as “big data” is a nearly impossible task for even large teams.  With machine learning tools, though, small teams can have intelligent algorithms parse massive stores of data, sussing out increasingly accurate details.

Machine Learning Can Save Time and Work

Technology exists to make life more efficient, safer, and, ultimately, better for all living creatures.  If people are asking “what is machine learning” and not finding out that it is helping humans live better lives, the connection has been missed somewhere along the line.

As time goes on, our work should become easier thanks to technology, not harder.  Smart applications of machine learning tools can do just that, allowing smaller teams to process marketing information that would have been impossible to analyze efficiently in the past.  Instead of countless overtime hours poured into the rote labor, marketing firms can instead rest easy knowing their professionals are making the most of their time – and talent.

Machine Learning Tools Help Maximize Human Talent

If there’s one thing humans will likely always have over AI, it’s that we’re creative beyond imagining.  If there’s one concept that isn’t the answer to the question “what is machine learning,” it’s “creative.”  Machine learning tools are fantastic at analyzing and highlighting patterns from massive collections of data, but they can’t tell us what to do with that data.

Utilizing effective machine learning tools means freeing up human talent to do what humans do best: create.  When most professionals pursue marketing, they don’t imagine doing countless hours of data analysis as the endpoint of their career.  Realistically, most people imagine working with other people, developing strategies, and helping good products reach the eyes of the people who want them. Effective machine learning tools help humans work with humans, ensuring that marketing professionals don’t have to miss the forest for the trees.

How Does Mining Cryptocurrency Affect the Environment?

crypto

Energy use has been a hot topic in recent months, especially as more and more studies point to environmental conditions growing worse with each passing year.  At the same time, the general public has become increasingly aware of cryptocurrency, which has brought both praise and scrutiny to the methods used to mine, trade, and maintain cryptocurrencies.

However, while there’s been plenty of discussion on forums and in videos, it can be hard for newcomers to crypto to find simple-to-parse summaries of environmental and energy information concerning cryptocurrency mining.  So, to help make things easier for those looking to learn, we’ve created a quick informative article to help you gauge whether mining is something you want to participate in.

Energy Use

Most people don’t think of computers as particularly heavy energy-consuming devices.  In truth, however, that’s only true for most mid-level home and office computers.  Gaming computers, servers, and cryptocurrency mining computers all have significantly more power draw than the average device, with miners taking the lead in terms of power draw per user.  Large servers often have higher draw, but they’re able to service so many clients at a time that they are actually considerably more efficient on average.

The reasons mining computers draw so much energy boils down to the process required to “mine” a cryptocurrency.  Cryptocurrencies are largely powered by what is known as a “blockchain,” a massive series of encrypted data blocks which contain the data used to represent cryptocurrencies.  To earn cryptocurrencies, generally these data blocks must be decrypted, a process which requires an incredible amount of mathematical computation.

bitcoin2 - How Does Mining Cryptocurrency Affect the Environment?

Mathematical computations of such complexity usually require powerful processors, the most affordable of which are found in graphics processing cards (GPUs.)  While one GPU generally draws more power, most mining rigs utilize the processors of multiple GPUs resulting in incredibly high power draws to a single mining computer.

Environmental Impact

Because of the massive power draw that cryptocurrency mining rigs possess, much of the environmental impact is going to be tied to what sort of generators power companies local to each miner use.  A miner whose electricity is sourced from nuclear, for example, will have much less of an environmental impact than someone whose electricity is sourced from coal.

The problem, though, is in the scale.  Non-nuclear, green energy sources, such as solar and wind, do not produce enough energy to sustain many mining rigs, and green energy companies have reported that the popularity of mining rigs has led to more energy waste than they were able to produce.

With that in mind, environmentally-minded individuals should take caution to research their local energy sources before committing to cryptocurrency, especially for currencies like BitCoin, whose blockchain design makes the mining of coins continually more difficult on a computational level.

While one miner is unlikely to have a noticeable impact on anything but their own energy bill, if people keep jumping onto the cryptocurrency trend, many locations will see their energy production needs skyrocket.  With increased production comes increased pollution, and with increased pollution comes a further endangered environment.

Unfortunately, at least for the time being, cryptocurrency mining using existing structures is not particularly environmentally friendly.  Though there is potential for more efficient systems, individuals should take their time before investing in a crypto mining rig outright!

How Gaming Went From Couches and Gamepads to Worldwide Faceoffs

IT office

Video games have a much longer history than most gamers realize, and their relevance going forward into the future seems to grow greater by the day. When thinking on the history of video games, many people would imagine arcade machines of the 70s and 80s, the Atari, or the Nintendo Entertainment System.

In truth though, the first recognizable video game was invented in 1947.

Known as the “Cathode-ray tube amusement device,” the first video game was a simple simulation of an “artillery shell,” represented by a glowing line on the screen moving in an arc towards objects drawn on a plastic screen overlay. The movement of the artillery shell could be controlled by a player working a set of control knobs.

After the Cathode-ray tube amusement device came further intrepid devices. Throughout the 1950s, various simple games like chess were brought to an electronic format. The advancement of electronics technology allowed for basic multiplayer games to be created, though they were not simple or affordable enough for everyday people to have in their homes.

Many of the early video games were specifically designed for tech demos and exhibitions as opposed to personal use.

In the 1970s came arcade cabinets and the first recognizable video game consoles. These platforms encouraged multiplayer and spectator enjoyment of video games, making video games a considerably more personal and social event than ever before. The popularity of early consoles created space for an industry to grow, and, less than a decade later, a surge of gaming consoles like the Atari 2600 and then the NES.

zxc 1 - How Gaming Went From Couches and Gamepads to Worldwide Faceoffs

Consoles became the primary way that most people enjoyed video games, as they were relatively affordable, easy-to-set up, and didn’t require any sort of inside knowledge of tech like early PCs did. As PCs became more user-friendly, they would appear in homes all across the world, making the way for a boom in PC gaming that has never really abated.

One of the most incredible advancements of early PC gaming came with the invention and spread of the Internet and the World Wide Web.

As more computers were connected to the internet, doors opened for remote multiplayer of video games. At first, games like Sid Meier’s Civilization were played in a multiplayer format by emailing data back and forth as each player took their turn. The game would update each local copy as data was downloaded from each email, allowing players to have a contiguous game across multiple remote computers.

As internet tech grew and evolved so, too, did the games that utilized it. Soon enough, entire gaming platforms existed to facilitate easier, faster, and more in-depth multiplayer gaming. Servers hosted by companies or individual consumers allowed for numerous players to connect to the same game and play together at their own leisure.

Soon enough, gaming and electronics companies realized the value that could come from maintaining dedicated online services for the purposes of multiplayer. Services like Playstation Plus, Xbox Live, Gamespy, Steam, and Battle.Net all served as considerably more stable and easy-to-access frameworks for gaming companies to deliver their works to players who were excited to play together.
Now, multiplayer gaming has gone truly global, with matches of popular games even being televised on the same channels that broadcast professional sports!

Esports is a career that many skilled gamers pursue and succeed at. What’s more is that millions of people around the world carry gaming systems in their pocket in the form of smartphones, which allow multiplayer gaming during transit, in coffeeshops and libraries, and even in the great outdoors.

Even the sky doesn’t seem to be the limit for the evolution of gaming. Games are the subject of considerable psychological research, the purpose of which is to determine if gamification of our daily lives can be beneficial to us as a global society. After all, games are fun; they provide unprecedented efficacy at stimulating joy, motivation, and social interaction that might otherwise not be possible without them.

9 great computer inventions you need to know about

feat7 - 9 great computer inventions you need to know about

The computer itself is considered the best invention in history. Nowadays, computers offer us a range of benefits such as researching virtually any topic fast and easy, fostering global communities of people, allowing unlimited business potential, enabling us to communicate easily with anyone in the world, supporting creativity, ensuring access to education and medical knowledge, and serving as part of essential tools like cars or robots, to name just a few. But the road to what we have today was both long and fascinating. Let’s have a look at the greatest computer inventions ever:Vintage calculator

1. Charles Babbage’s computer

The first computer ever built dates back from 1821 and is called “The Difference Engine”. Its purpose was to output mathematical tables and it was commissioned by the British government. Charles Babbage started work on this computer but never managed to complete it due to its high production cost.

2. The first computer program, created by Ada Lovelace

Ada Lovelace, Countess of Lovelace and English mathematician, was the first to observe that Babbage’s computer The Analytical Engine had more applications than just performing pure calculations. While translating Italian mathematician Luigi Menabrea’s records on Babbage’s computer in 1841 she left notes with her translation, and one of the notes was the algorithm needed to compute Bernoulli numbers by the analytical engine – or the first computer program.

Old defect computer

3. First working programmable computer: Z3

Z3 was the third computer built by Konrad Zuse, and it was the world’s first programmable computer. The invention of this machine made Zuse to be regarded as the inventor of the modern computer. Completed in Berlin in 1941, Z3 was a fully automatic digital computer and its average calculation speed was 0.8 seconds for addition and 3 seconds for multiplication. Unfortunately the original Z3 was destroyed in the bombing of Berlin in 1943.

4. ENIAC – the first general purpose programmable electronic computer

ENIAC was developed in 1946 and it was able to solve a variety of numerical problems through reprogramming. This digital computer was huge – it weighed 27 tons, it occupied 167 square meters, and consumed 150 kW of electricity. Today, parts of the ENIAC are held a multiple institutions around the world. ENIAC is remembered for helping with computations to determine the feasibility of the world’s first hydrogen bomb.

5. The first personal computer, Simon

Dasher computer

A relay-based computer, Simon was sold for $600 and was built to demonstrate the concept of the digital computer. Its only use was an educational demonstration, and it could perform four operations: addition, negation, greater than, and selection. Simon was limited to a 2-bit memory and output through five lights.

6. The first real-time graphics display computer by IBM (1951)

AN/FSQ-7 is known for being the largest computer system ever built, with 24 installed machines, each weighing 250 tons, and using a total of 60,000 vacuum tubes. It was able to perform approximately 75,000 instructions per second for networking regional radars and it was used for Cold War ground-controlled interception. Stations were equipped with light guns to select targets on screen for further information.

7. First mouse

Wireless mice

The first mouse appeared in 1964 and it was one of the steps taken to make computers more user-friendly. The idea belonged to Douglas Engelbart, which created a device with a pair of small wheels (one turning horizontally and the other vertically) which could be used to move a cursor on a computer screen. The device evolved to perform multiple semantic gestures, such as drag and drop and selection, and to be updated to modern versions, such as optical mouse, laser mouse, wireless mouse, inertial mouse (which doesn’t need a surface to operate), the gaming mouse, and the ergonomic mouse, developed to provide optimum comfort and prevent repetitive strain injuries of the hand.

8. The first touchscreen

Touchscreens may seem like a recent invention, but you’ll be surprised to find out that the first touchscreen in the world was developed in 1965. Unlike modern touchscreens, this one had no pressure sensitivity (it was either contact or no contact) and it was able to register only a single point of contact (it wasn’t multitouch). This type of touchscreen was used by air traffic controllers in the UK until the 1990s.

9. The first portable computer – Compaq Portable

The first product of the Compaq Computer Corporation, this portable computer was launched in 1982. Its price was $2,994 (equivalent to approximately $7000 today), weighed 13 kg, and it could be folded into a case the size of a portable sewing machine. Two years later, IBM released a similar computer, more affordable and less sophisticated, though.

Modern laptop

As time went by, computer technology exploded and it would take thousands of pages to write an exhaustive history of computer inventions. Every day new inventions in the field are made, and a long way has been completed since Charles Babbage’s computer in the early 19th century. The question is – what will the future bring?

8 major contributions of computer science

feat6 - 8 major contributions of computer science

Making a complete list of computer science contributions would be a very difficult job, because almost every aspect of daily life has been influenced and transformed by computing. However, we can identify some major breakthroughs or innovations that have brought significant contributions to a variety of fields. Computer science has changed society in an unprecedented manner and has definitely shaped the world we know today.

Old computer

1. Determining the third major leap in human technological progress

The first leap was the Agricultural Revolution, estimated 8000-5000 BC, followed by the Industrial Revolution (1750-1850 CE). The period of time until the third leap was considerably shorter, and in the 20th century we witnessed the Information Revolution. And now, in the 21st century, we see 90s computers as retrograde and primitive – this is because computers have caused a massive acceleration in the rhythm of progress development.

People on escalator

2. Increasing information storage capabilities

It has been estimated that the world’s capacity to store information has reached 5 zettabytes in 2014 or the informational equivalent of 4,500 stacks of printed books from Earth to the Sun. Today, more pictures are taken every couple of minutes than the entire number of pictures taken in the 19th century. Having enough internal memory on your computer does not even matter anymore because you can store information on the cloud.

Enigma machine

3. Automation and productivity

The Information Age we live in today has swept the Industrial Age paradigm and made it possible to increase manufacturing value while around a third of manufacturing jobs fell. A good example is the one of the United States manufacturing industry, where in the period between 1972 and 2010, manufacturing value increased 270% while the number of people employed in the industry decreased from 17,500,000 to 11,500,000. This has happened because of automation and computerization, which were blamed by many for destroying jobs. However, data has shown that technology may destroy jobs in the short run but leads to creating others on the long term.

4. Breaking the Enigma Code in World War II

The western Allies were able to obtain the victory in World War II after they managed to read Morse-coded radio communications of the Axis powers which had been enciphered using Enigma machines. German armed forces and their allies were using a type of enciphering machine called Enigma to send messages securely. The Enigma code was broken by English mathematician Alan Turing who invented with a fellow scientist, Gordon Welchman, a device which reduced the work of code-brokers – a type of computer nicknamed as the Bombe. His work was so significant that it had made the single biggest contribution to Allied victory. So much so, that Hollywood even decided to make a movie about cracking the enigma code, ‘The Imitation Game’.

Trading software

5. Mapping the Human Genome

Natural processes and situations characterized by great complexity can be experimented entirely only by software, such as the biological project aimed at determining the sequence of nucleotide base pairs that compose human DNA, known as the Human Genome Project. The project started in 1990 and the last milestone was achieved in May 2006, when the sequence of the last chromosome was published in Nature. The mapping of the human genome has multiple benefits and applications such as genotyping viruses, identifying mutations that cause cancer, predicting medication effect, and developing the forensic applied sciences. The data could be analyzed only by developing dedicated computer programs.

6. Artificial intelligence

Many of us think of robots that could one day overturn humanity when we hear about artificial intelligence, but AI is completely different from that. AI for Good is used to describe those artificial intelligence applications that benefit society, such as aviation systems, speech recognition software, personal assistants, robo-advisors used in the investment management industry, healthcare robots and equipment, software creating news and writing pieces, telephone customer service, and robotic vacuum cleaners to mention just a few.

7. Computer graphics

Computer graphics can be used to create images and videos, which is called computer-generated imagery or CGI. Even films that do not contain CGI are created using digital cameras or post-processed using digital video editors – modern entertainment could not exist without computer tools.

Computer graphics

8. Algorithmic trading

The liquidity and efficiency of financial markets has been increased by using techniques such as algorithmic trading, machine learning, and high frequency algorithmic trading. Algorithmic trading eliminates the need of constantly watching stocks and manually sending small slices of the order or child orders out to the market all the time. This technique has made it possible to execute large orders in markets which don’t support the entire size at once and to minimize the cost and risk in the execution of an order.
These are just a few contributions of computer science in multiple fields; we’d say that it’s impossible not to come into contact with computer technology at this day and age, whether you are in a private or public space, in nature or in an office. Computer science has made our lives easier and safer, and in spite of the drawbacks, we owe a lot to scientists in this field.

9 differences between computer science and information technology

feat5 - 9 differences between computer science and information technology

Are you passionate about computers and don’t know which degree to choose between computer science and information technology? Or just wondering what the difference between these two terms is, since both of them are obviously related to computers? Understanding the difference is vital, because your career path can differ a lot if you choose one instead of the other. Here is what you should know:

Laptop close up - 9 differences between computer science and information technology

1. Computer science experts are scientists

The first difference between the two is obvious for any language-wise person. Science is not the same as technology. The first is “knowledge or a system of knowledge covering general truths or the operation of general laws especially as obtained and tested through scientific method”, while the latter is “the practical application of knowledge especially in a particular area”. By training yourself in computer science you become a scientist, as you deal with the theory of computational applications. The main areas of concern for computer scientists are software, operating systems, and implementation, and computer scientists develop new ways to manipulate and transfer information by using advanced mathematics and algorithms.

2. IT professionals are the users of technology

While computer scientists develop the technology, it’s the information technology professionals who use it. These experts solve a variety of business problems by utilizing operating systems and software. A good metaphor to understand each role is the one of a house: computer engineers are construction workers who build the house. Computer scientists add systems and facilities to the house, such as plumbing, lights, running water, and so on. IT professionals are the inhabitants who employ these appliances in order to attain a desired effect.

Network cable - 9 differences between computer science and information technology

3. One is more theory, the other is mostly practice

As a computer scientist, you will train in the theory of computation and the design of computer systems. This discipline is close to mathematics and there are three broad areas of work: designing and implementing software, finding new ways to use computers, and solving computing problems. Those who study information technology will deal with the daily computer needs of various organizations and make sure technology is integrated within the institution’s infrastructure and solving its business problems.

4. The two disciplines lead to different career paths

Computer scientists deal with how computers work and build operating systems that do what they want; their field is based on mathematics, which is the language of computers. Examples of careers in computer science are applications software developer, systems engineer, and web developer. On the other hand, IT professionals are responsible for using and troubleshooting programs and applications developed by computer scientists. Jobs in the IT field include information security analyst, network architect, computer support specialist, database administrator, and systems administrator.

5. Workplaces differ for the two professions

IT professionals are usually found in business environments where they install networks and computer systems, while computer scientists are found in a larger variety of environments; besides businesses they can also be found in universities and video game design companies.

Person using computertechnology - 9 differences between computer science and information technology

6. As a computer scientist, you need to enjoy mathematics

Since computer science is about programming computers using mathematical algorithms, you will study mathematics intensively in university. A lot of independent work is involved, with you writing code and applying complex algorithms. If you enjoy more installing computer systems and maintaining networks and databases, IT is a better degree and career option for you.

7. As an IT professional, you should be a good problem solver and be trained in customer service

If you work in the IT industry, you will interact on a daily basis with clients in order to help them solve technological problems. Aside from skills such as SQL and Linux, IT requires assets seen in other business fields, like customer service, technical support, and project management. And definitely a lot of patience with training and assisting end users.

Printed circuitboard - 9 differences between computer science and information technology

8. Different personality traits are required

IT professionals need to be comfortable interacting with others and have good communication skills. In order to develop and execute solutions you may need to work with cross-functional groups, and be a team player. On the other hand, computer science professionals are often independent and introvert personalities, who can focus in a solitary environment on writing code and developing complex algorithms. The typical computer scientists would probably not be so pleased to train the new company employee and answer to their questions.

9. Not exactly a difference, but remuneration is not the same for the two fields

Median salaries for IT workers range between $48,900 for Support Specialists to $79,680 for Systems Analyst. It’s a good annual salary, but working in the computer science field can be even more rewarding: median salaries range from $74,280 for Computer Programmers to $93,350 for Software Developers.

Modern office - 9 differences between computer science and information technology
So, if you are oscillating between the two fields, the obvious difference between computer science and information technology described above should make your decision very simple. As long as you know who you are and what you like, the choice is easy.

6 reasons why computer science is a science

feat4 - 6 reasons why computer science is a science

Have you ever thought that computer science could actually not be a science? There are plenty of voices who claim so, since the very beginnings of this discipline. The main argument for not considering computer science an actual science is that science deals with fundamental laws of nature. Since computers are manmade, computer science is considered an erroneous term, and information technology is preferred by the anti-computer science camp. The answer to this dilemma depends on how we understand science and what we consider as the object of study for computer science. Let’s see some points of view that support the pro computer science position:

Computer chip board - 6 reasons why computer science is a science

1. Computer science follows the scientific paradigm

As long as scientific paradigm is the process of developing hypotheses and testing them through experiments, computer science meets the criteria of a proper science. Moreover, successful hypotheses become models used to explain and predict world phenomena. What computer science does is to study information processes and computers are used to test hypotheses. Research in the field makes it possible to use models to build better programs with fewer defects.

2. Computer science does study naturally-occurring processes

Computing qualifies as an exact science because it studies information processes which occur naturally in the physical world; furthermore, computer science is used for prediction and verification. Computer science does not study computers, which indeed are manmade, but information processes, which can be both natural and artificial.

Binary system - 6 reasons why computer science is a science

3. All the generally accepted criteria that make a science are met by computer science

According to Peter Denning, a professor at the Naval Postgraduate School in Monterey, California, who is advocating that computing is a science, says that computer science satisfies all the accepted criteria of being a science, and those criteria include: an organized body of science, an experimental method to test hypotheses, a track record of non-obvious discoveries, and an openness to any hypothesis being falsified.

4. Computers are not at the center of computer science

The definition of computer science being the study of phenomena surrounding computers is not correct. It has been discovered that computation is not performed only by computers – in 2001, Biology Nobel Laureate David Baltimore said that cellular mechanisms are natural computational means to read DNA and construct new living cells, which has determined Denning to realize that “Computation is the principle, the computer is simply the tool”. Ultimately, computers are tools to study information processes which already exist in nature.

5. Computer science has a set of principles

According to the same before mentioned author, the principles of computer science can be organized in seven categories: computation, communication, coordination, recollection, automation, evaluation, and design. The seven categories are not principles in itself, but groups of principles.

Computer scientists - 6 reasons why computer science is a science

6. “Computers have as much to do with computer science as telescopes have to do with astronomy”

This quote is attributed to Edsger W. Dijkstra, a Dutch computer scientist, and its full version is: “[Computer science] is not really about computers — and it’s not about computers in the same sense that physics is not really about particle accelerators, and biology is not about microscopes and Petri dishes…and geometry isn’t really about using surveying instruments. Now the reason that we think computer science is about computers is pretty much the same reason that the Egyptians thought geometry was about surveying instruments: when some field is just getting started and you don’t really understand it very well, it’s very easy to confuse the essence of what you’re doing with the tools that you use.” – it is a famous quote which supports Denning’s point of view by using some very accomplished figures of speech.
Does this all mean that computer science is not such a fortunate term and computational science should be used instead, since the science in debate deals with computing processes? Apparently, computer science and computational science are two different things, and computational science is “the application of mathematical models to computations for scientific disciplines.” The latter is closer to engineering, while computer science sticks more to the scientific part.

Computer keyboard - 6 reasons why computer science is a science
To conclude with, computer science is indeed a misleading name, and could better be named computing science, since computing is the application of systematic treatment to information. The name of computer science though continues to be preferred because the term is too familiar and has been used since 1956. However, the term computing science is being used by multiple departments of major universities, which like to emphasize the difference. Another term which is in use in Scandinavian countries is datalogy, which suggests that the discipline is about data and data treatment. Yet another alternative term is data science, proposed by Peter Naur – who was the first professor in datalogy at the Department of Datalogy at the University of Copenhagen, founded in 1969.

Printed circuit board - 6 reasons why computer science is a science
So, those who say that computer science is not a science are somewhat right – computing science is the real term in question, and computing science really is a science.

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