Computer processing of information has been used for decades, but the term "big data" – Big Data – had only become widespread by 2011. Big data has enabled companies to quickly extract business value from a wide variety of sources, including social networks, geolocation data transmitted by phones and other roaming devices, publicly available information from the Internet, and sensor readings embedded in cars, buildings and other objects.
Analysts use the 3V / VVV model to define the essence of big data. The designation is an acronym for the three key principles of Big Data: volume, velocity, and variety, respectively.
Big Data is arrays of diverse information that is often generated, updated and provided by multiple sources. This is used by modern companies to work more efficiently, create new products, and ultimately become more competitive. Big Data accumulates every second – even as you're reading this, someone is collecting information about your preferences and browsing activities. Most companies use Big Data to improve customer service, while others use it to improve operational data and predict risk.
For example, VISA uses Big Data to reduce fraudulent transactions, World of Tanks game developers use it to reduce gamer churn, the German Ministry of Labour uses it to analyse unemployment benefit applications, and major retailers compile large-scale marketing campaigns to sell as many products as possible.
It can be divided into the following stages:
An important element of working with Big Data is search, which allows you to get the information you need in different ways. In the simple case, it works in the same way as Google does. Data is available to internal and external parties for a fee or for free – it all depends on the terms of ownership. Big Data is in demand from app and service developers, trading companies and telecommunications companies. For business users, information is offered in a visualised, easy-to-understand form. If the format is text, it will be concise lists and excerpts, if it is graphical – diagrams, charts and animations.
Read also The Beginner's Guide to Web Hosting.
he handling of Big Data involves the use of a specific infrastructure focused on parallel processing and distributed storage of large volumes of data. But there is no one-size-fits-all solution for this purpose. Although a huge number of factors influence the choice of hardware, the only important factor is the software for Big Data collection and analysis. Accordingly, the process of purchasing hardware for a company will be as follows:
Thus, each project will be unique in its own way, and the equipment for its deployment will depend on the software chosen. Let's take for example two server solutions which are adapted to work with Big Data.
This is a powerful and flexibly scalable platform designed for rapid analysis of large data sets of different types. It combines the advantages of a pre-configured hardware platform running on industry-standard components with dedicated open source software. The latter is provided by Cloudera and Datameer. The manufacturer guarantees the compatibility of the system components and its efficiency for complex analysis of structured and unstructured data. PRIMEFLEX for Hadoop is offered out-of-the-box, complete with business consulting services for Big Data, integration and maintenance.
This integrated system makes the most of SAP HANA. FUJITSU's PRIMEFLEX is suitable for storing and processing large amounts of data in RAM in real time. Calculations are performed both locally and in the cloud.
FUJITSU delivers PRIMEFLEX for SAP HANA in a comprehensive manner, with value-added services for all phases – from project decision and financing to ongoing operations. The product is based on components and technologies that have been certified for SAP. It covers different architectures, including previously configured, scalable system support, customised and virtualised VMware platforms.
The standard deviation is, in simple terms, a measure of how scattered the data set is.
By calculating it, you can find out whether the numbers are close to or far from the mean. If the data points are far from the mean value, then there is a large deviation in the data set; thus, the greater the scatter in the data, the higher the standard deviation.
The standard deviation is denoted by the letter σ (Greek sigma).
Standard deviation is used
The standard deviation (σ, s) is a measure of the scatter in a set of numerical data. In simple terms, how far from the arithmetic mean (Mean) the data points are. It can also be called a measure of central tendency: the smaller the standard deviation, the more "clustered" the data are around the centre (the mean).
The standard deviation can be expressed by the formula STD=√ (∑ (x-x)2)/n], which sounds like the root of the sum of the differences between the sample items and the mean, divided by the number of items in the sample."
Should read: "Standard deviation can be expressed as STD=√ (∑ (x-x)2)/n], which sounds like the root of the sum of the squares of the differences between the sample items and the mean divided by the number of items in the sample.
The standard deviation (SD), measures the amount of variability, or dispersion, of individual data values, to the mean, while the standard error of the mean (SEM) measures how far a sample mean (mean) of the data is likely to be from the true population mean.
In probability theory and statistics, the standard deviation is the most common measure of the dispersion of values of a random variable relative to its mathematical expectation (analogous to the arithmetic mean with an infinite number of outcomes). It usually refers to the square root of a random variable's variance, but sometimes it may refer to some variant of that value. In the literature, it is usually denoted by the Greek letter. (sigma).
The difference between standard deviation and variance can be clearly defined for the following reasons: Dispersion is a numerical value that describes the deviation of observations from the arithmetic mean. The standard deviation is a measure of the dispersion of observations in a set of data relative to their mean. The variance is nothing more than the mean of the squares of the deviations. Standard deviation, on the other hand, is the standard deviation.
The mean sampling error shows how far the sample population parameter deviates, on average, from the corresponding parameter of the general population.
If we calculate the average of the errors of all possible samples of a certain kind of a given volume (n) extracted from the same general population, we obtain their generalizing characteristic – the average sampling error ().
The oscillation coefficient shows the extent of variation relative to the mean, which can also be used to compare different data sets. Thus, in statistical analysis, there is a system of indicators that reflects the dispersion or homogeneity of data. Below is a video on how to calculate coefficient of variation, variance, standard deviation and other measures of variation in Excel.
The marginal error of sampling is denoted by the Greek letter (delta). It is equal to the product of the sampling error by the corresponding coefficient of confidence. So for the first confidence interval, the coefficient of confidence is for the second, and for the third. Replacing the corresponding formulas for resampling, we obtain:
Power BI is Microsoft's comprehensive business intelligence software, combining several software products that share a common technological and visual design, connectors, and web services. Power BI belongs to the class of self-service BI, and BI with resident computing. It is part of a single platform.
Many people don't like analytics because they don't understand how to work with it and why. Today, using the example of Microsoft's Power BI system, we will tell you how knowing a simple analytics software can make life easier for any business. And it doesn't matter whether you are an analyst or a marketer.
If you've ever needed to make a beautiful report, you know that it's very time-consuming. You have to find the data, analyse it, put it together and visualise it beautifully. To simplify the process and the life of marketers/analysts/entrepreneurs, Microsoft came up with Power BI.
This free software knows how to recognise and connect to more than 70 data sources. For example, xlsx, csv files, txt files, data from SQL databases. It can also clean up the data or process it and bring a million tabs into a single data model. Or you can define your own custom metrics that are used specifically in your company.
A major and huge plus of Power BI is that it allows you to make graphically beautiful and understandable reports. Options for any query – histograms, charts, tables, slices, cards, etc. All this can then be saved in a special cloud-based online Power BI Service and "finalize" the report together with your colleagues.
Well, there are five components that make the system work:
This is a kind of program algorithm. That is, first we need to get the necessary data in the window of the same name. This will open a window, where we need to select data for connection. You can pull them from the regular databases, such as MySQL, from Excel spreadsheets or from Internet resources like MailChimp, Facebook and others.
When we have selected the right one, two windows will appear: on the left you will see the previously selected parameters, on the right you will see the data itself. You can immediately click "upload" and start making reports. Or choose "edit", which will just open the Power Query editor.
The editor will appear as a separate window. In it we can organise everything that has come at us. At first glance, the editor window looks something like Word/Excel and other programmes: the toolbar at the top, all queries on the left, and the 'query parameters' window on the right. This window will display all the operations you have done with the data – deleting rows, renaming something.
Logically it is remotely similar to working with layers in Photoshop. In general, in the editor we can clean up, process, bring data back to the same look if it was from different sources, merge or split something.
The main working area with the data will be in the middle. Once you have generated all the queries, you need to click "save and apply". You will then return back to the working window and the programme will remember all the queries you have generated. Further, if you update the data, all manipulations with the initial data will take place automatically.
Next we proceed to the "links" mode. By the way, if the data has already been prepared, you can skip all the above steps and proceed directly to the links.
It's relatively simple – we can set links between the columns of different tables, form their orientation (one-way/bidirectional links), we can also connect multiple tables with each other. Here, of course, we need to learn the tools, so that the output is clear, precise and beautiful. Although the same goes for the editor tools.
Data mode is designed to allow you to augment your current data models with some kind of calculation – measures, tables, columns. An important point here is that all calculations are created with a string of formulas using a special language called DAX. This is a language of functions and formulas that Microsoft has developed for its products. You have probably come across it if you have ever worked with Excel.
Finally, we have come to the most important thing: the "Reports" mode. This is where things get presentable and really clear. All of the report options are contained in the column "visualization". There is also a "filters" panel which allows you to filter some data from a certain page or level of the report.
Generally speaking, the "reports" mode is the simplest level that Power BI has. Here you simply drag and drop the graph you want into the report field or apply a filter.
In fact, there are plenty of options for whom knowledge of this programme can be useful. It is used by product analysts, SEO specialists, developers and testers. Power BI will be equally useful in an IT company as it is in e-commerce. After all, it is always better to rely on real figures to understand where to take a step for further development.
A minimum use case is to look at ready-made reports from colleagues to draw conclusions or to see the amount of current stock. The software has a real-time dashboard.
The marketer can look at the profitability of different sales channels in order to strengthen some of them or disable them altogether. By the way, Power BI can be connected to Google Analytics and see, for example, the number of visits to the website.
The salesperson can also navigate through reports to understand their effectiveness or to study data on new customers. Company managers basically need to look at and understand the reports in order to understand what is going on in general. By the way, reports can even be viewed from the app, handy when travelling on business.
Well, the creation of these reports can be done by anyone – the commercial director, the head of the sales department, etc. Of course, at a more in-depth and professional level, analysts do it.
Power BI is a true savior in a world of enormous amounts of data that needs to be organized in a nice and clear way. Most importantly, you can do this with any type of data and bring it into a single view. Combine the report from Google Analytics and MySQL.
It's quite easy to use, so it's not just for analysts who want to learn its functionality. All of the reports generated may be stored in the cloud. This means they can be viewed at any time, anywhere, and conclusions can be drawn.
If you've been following Microsoft news, there's a good chance you've heard of Microsoft Azure, formerly known as Windows Azure. This cloud computing service is a big part of Microsoft's business, and it competes with similar services from Amazon and Google.
Microsoft Azure is a cloud computing service that operates similarly to Amazon Web Services (AWS) and Google's cloud platform.
By 'cloud computing' we do not mean the vague term often applied to consumer services that store your data on a remote server. We mean actual computing as a service for companies, organisations and even individuals who want to take advantage of it.
Traditionally, businesses and other organisations host their own infrastructure. A business would have its own web server (or mail server or whatever) on its own hardware. If more capacity was needed, the business would have to buy more server hardware. The business would also have to pay someone to administer this equipment and pay for a reliable Internet connection to serve its customers. In addition, there are hosting companies that host your services on some of their hardware in their data centres, for a fee.
Cloud computing works a little differently. Instead of running your own hardware or paying to use certain hardware in someone else's data centre, you simply pay for access to a huge pool of computing resources provided by Microsoft (or Amazon, or Google). This allows you to host web servers, email servers, databases, file storage servers, virtual machines, user directories or whatever else you may need. When you need more computing resources, you don't need to buy physical hardware. "The cloud shares the hardware and automatically assigns work as needed. You pay for as many computing resources as you need, not a specific number of hardware servers in a rack.
The services you deploy in this way can either be public servers available to all, or part of a 'private cloud' that is used only within the organisation.
When you use cloud computing, your initial costs are greatly reduced. You don't have to invest a lot of money in setting up your own data centre, buying hardware for it and paying for staff. There's no risk of overpaying for too much equipment, or buying too little and not having what you need.
Instead, you put everything you need to host it "in the cloud" provided by a service such as Microsoft Azure. You only pay for the computing resources you use. If you need more, it can scale instantly to meet demand. If you need less, you don't pay more than you need.
For this reason, everything from a company's internal email system to public websites and mobile app services are increasingly being hosted on cloud platforms.
The Microsoft Azure website provides a catalogue of hundreds of different services that you can use, including full virtual machines, databases, file storage, backups and services for mobile and web applications.
The service was originally called 'Windows Azure' but has been renamed 'Microsoft Azure' because it can do so much more than just Windows. For example, you can run Windows or Linux virtual machines in Azure – whichever you prefer.
Digging into those hundreds of services, you'll find you can do just about anything. And for anything Azure doesn't offer in a simple service, you can set up a Windows or Linux virtual machine that hosts whatever software you want to use. You can even host a Windows or Linux desktop in the cloud in a virtual machine and connect to it remotely. It's just another way to use remote computing resources.
A lot of what Azure does is not exclusive to Azure. Amazon, Microsoft and Google compete. Amazon Web Services, for example, is the leader in this area, ahead of Microsoft and Google's offerings.
Microsoft also uses Azure to extend Windows in several important ways. Traditionally, organizations that wanted to have a central user directory and management of their PCs needed to run their own Microsoft Active Directory server. Now, in addition to traditional Active Directory software that can be installed on a Windows server, an organisation can use Azure Active Directory.
Azure AD is the same but hosted by Microsoft Azure. This allows organizations to have all of these functions centrally administered without requiring them to host their own Active Directory server (and configuring the often complex infrastructure and access permissions needed to run it remotely).
These services are not identical, but Microsoft is clearly betting that Azure AD is the future. Windows 10 users can join Azure Active Directory via Work Access, and Microsoft's Office 365 service uses Azure Active Directory to authenticate users.
What is the difference between editing and revising an essay? Let's try to answer this question. Very often students mix the two phenomena. However, there is a fundamental difference between them.
Practically any essay editors providing services for students are well aware of the difference between editing and revising a text. You don't have to go into all the intricacies of working with essays at all if you would like to entrust the work to specialists. However, understanding the differences between editing and revision can be helpful to any educated person in any case.
Editing includes checking for text accuracy, consistency in usage and markup, and errors on the side of the text. Errors in the use of capitalization, punctuation, and spelling should also be corrected during editing. Editors also check for consistency and accuracy in references, charts, illustrations, headings, footnotes, page numbers, etc. Editors make sure that the editor and writer do not miss anything. It is the editor who is responsible for the overall quality of the text since the text has already been reviewed and corrected before it was typed.
Revision of the text material is a change in structure, form and, most importantly, content. Revision is aimed at changing the text as such, at changing the logic of the presentation of the material. When revising, the main element of the work is not the text block, as such, but the text contained in this block. As a rule, revising the text of an essay implies making significant changes in it.
Probably nothing, as strange as that may sound. If the author does not disclose a topic in the text, it is unlikely that an editor will be able to do so. But if the author of the material "covers up" the meaning and logic of the text with some verbal redundancy and repetition, then the editor will clear it all up, simplify it, and show the reader what the text was written for.
When revising the text of an essay, the first thing to look at is:
-Reading aloud will allow you to find errors, irregularities in rhythm, and tautology. Read the text aloud several times, and the language itself will stumble on a misspelled sentence;
-Reading from the end of the piece will allow you to perceive each phrase separately, without slipping into the meaning of the text. This way you will clean up each sentence more thoroughly;
-Ask a friend to read your work. People's perceptions are different and the other person will notice flaws;
-Read the text after a break. Taking a break will help you look at the work with different eyes. Your brain will forget how you worked on the text and see what you didn't notice or missed before.
-Reviewing should be done the day after the first reading of the text. Then the words become fresher, it will be easier to identify mistakes and part with unnecessary words. If possible, you can put the essay "on the desk" for a week, so that you can forget its content. After a week, read it again. Now you will have an opportunity to rethink the content of the essay.
We hope that our short article was able to answer all of our readers' questions. If you want to order essay editing or revision services, you can always do it on our website.
This article is devoted to ITIL 4 Foundation PDF (IT Infrastructure Library) – a library of best practices for the provision of IT services, which has become the de facto standard today – a generally recognized standard for managing the maintenance of information systems.
Over the past two decades, information technology has had a great impact on business processes in a wide variety of companies. The emergence of personal computers, business applications, local and global networks has led to radical changes in many areas of business. Under these conditions, the quality of IT services provided to companies is of great importance.
The achievement of business goals by companies today depends to a large extent on the effective use of information technology and on the provision of quality IT services that meet business goals, customer requirements and expectations, while more and more attention has recently been paid not to develop IT solutions (for example, business applications), but the management of services for their maintenance, which guarantees high availability of the solution for end users. At the same time, in the life cycle of IT solutions, their operation accounts for 70 to 80% of the time and financial resources, and only 2-30% of the time and funds are spent on the development (acquisition) and implementation of the product.
Note that today the leaders of many companies are dissatisfied with the quality of IT services provided by their own IT departments. There are many reasons for this. IT projects are far from being always completed within the given timeframe and budget, and post-project support often negates the efforts made to implement projects. The issues of organizing the processing of requests from users and heads of departments by IT departments, introducing changes in the presence of constant operation of existing corporate information systems, efficient use of resources of IT departments – this is not a complete list of problems faced by consumers of IT services. It's no secret that corporate executives often see the IT department as a bottomless pit into which huge amounts of money are thrown, while IT professionals from all other departments often seem like whimsical and impatient children demanding immediate miracles.
Addressing these issues requires a structured approach to IT service management to make IT efficient and effective. This approach is called IT Service Management (ITSM), and its main principle is to consider the IT service as a unit that is constantly focused on the needs of its users and solving changing problems, while quantifying both the level of quality achieved and the resources used is available. This principle of organizing activities is applicable to companies of any size and does not depend on whether the IT service is part of the company or is an external IT service provider.
Desktop or laptop? A desktop computer has many advantages: it's more powerful, it's cheaper, it's easier to upgrade and repair, it has a better keyboard, more ports, a bigger screen, and more. And only one drawback – the lack of mobility. So, what is the best laptop for hacking?
Panel Self-refresh is a technology by which the display displays a picture when there is no video signal, and changes it at the request of the GPU.
eDP also supports the integration of additional digital packages into the video signal, which allows other interfaces to be implemented on the display board. For example, you can add a microphone, webcam, touch surface, USB hub. This reduces the number of conductors in the cable to connect to the system board and reduces the cost of parts and maintenance.
Unlike LVDS, eDP reduces the total number of lines required for data transmission. And all without loss of quality and with clarity control!
In the next few years, I think the eDP standard will push outdated LVDS from the market. For clarity, I will give a table comparing the technical characteristics of interfaces.
Full HD matrices on the eDP interface are much cheaper than those with LVDS support. This also needs to be taken into account, but for me the choice was not so simple.
In the meantime, I settled on a 15.6-inch diagonal matrix.
Now you need to select the motherboard. It is she who will dictate her rules for supporting (or lack thereof) interface and other equally important connectors.
To choose a motherboard, you need to decide on its form factor. The mini-ITX, Mini-STX and thin mini-ITX formats are best suited to a fifteen-inch matrix.
Mini-ITX implies a motherboard with dimensions of 170 × 170 mm and support for desktop RAM. These boards have a 24-pin power connector from a standard ATX power supply, and the height of the interface connectors is about 4 cm.
Mini-STX is a fairly new motherboard form factor. Significantly smaller than mini-ITX at 147 x 140mm. The advantages include power supply from an external 19 V power supply. Disadvantage: the RAM slots are located vertically relative to the board, the connectors on the rear panel are made in two rows, which increases its size. Of course, they can be soldered, but this contradicts the original requirements for universality.
Thin mini-ITX – 170 x 170mm, same size as mini-ITX. But unlike it, the height here is one interface connector. In addition, such a board can be powered by an external 19 V power supply. My choice fell on the ASRock H110TM-ITX R2.0 motherboard.
HP Pavilion 15 is an ultraportable laptop that provides high performance, has a 15.6 inch full HD display, very useful for professionals who work in cybersecurity and hacking. The notebook supports many types of connections, including 1x USB 3.1 Type C Gen 1, 2x USB 3.1 Gen 1, 1x HDMI, etc. Pavilion 15 has an integrated webcam with a dual array camera, a digital microphone. Bydefault, the laptop comes with Microsoft Windows 10 Home 64-Bit operating system.
The weight is 4.08 pounds, so it’s quite easy to carry from place to another. This HP laptop has a built-in webcam with a dual array, a digital microphone. The laptop has a backlit keypad and numeric pad, helping you to type comfortably. The HP notebook comes with a solid state drive of 512 GB, which helps in penetration testing tasks. It also supports wireless communication standards like Bluetooth and Memory type like DDR4 SODIMM. The laptop has Intel Uhd Graphics with shared graphics memory, which helps in running a dual OS like Windows 10 and Kali Linux.
Lenovo IdeaPad is a good laptop to work on Kali Linux. It is one of the best options to have 14.0″ LED with an anti-glare display. This machine is AMD Ryzen 5 3500U processor that enables hackers to work seamlessly without any hassle.
When we talk about storage, such a laptop has SSD of 256GB and RAM of 8GB. It is sufficient for storing high-quality images and videos. Furthermore, you do not need an external hard drive. However, if you like to increase internal storage space, you can easily do it later on.
The design of this Lenovo IdeaPad is quite impressive compared to other laptops. Its weight is 3.3 pounds, and hence you can carry anywhere you want.
The battery life of Lenovo IdeaPad has 7 hours of battery life that enables you to work without interruption. You do not need a GPU for playing high-quality video as this laptop is equipped with AMD Radeon Vega 8 Graphics.
Podcasts are one of the simplest types of content. Unlike vlogging, you don't need expensive equipment. Let's talk about the best way to start by choosing the best laptops for podcasting.
Today, many people call podcasts the new radio. There is some truth in this: podcasts are attracting an ever-larger audience, they are listened to at home, in the car, on the subway on the way to work. The most important difference between a podcast and radio is that anyone can create their own, and for this you don’t even need special expensive equipment. We tell you how to choose the right equipment and software for recording podcasts, how much it can cost.
Of course, you can also record a podcast from your smartphone, but this will give you a very entry-level product at the output. For better recording, a computer is indispensable. Unlike, for example, streaming, a podcast does not need powerful and productive equipment – it is enough to limit yourself to a simple and reliable laptop. The requirements for the characteristics are not high: for example, you can not pay much attention to the video card and be content with the integrated chip, it will not affect the recording of the podcast in any way.
The processor does not have to break performance records either, an Intel Celeron / Intel Pentium Silver chip (or a similar model from AMD) is enough to record a podcast. When choosing the amount of RAM, it is better to give preference to a laptop with 8 GB of memory (in extreme cases, at least 6 GB) – so the computer can easily cope with multitasking.
If you're planning on recording podcasts outside of your home, the portability of your laptop will also be important, so opt for a lighter model so you can comfortably move around the city with it. There are many models weighing around 1 kg – you will definitely have plenty to choose from. For example, Acer Swift 1 weighs only 1.3 kg and has a thickness of 1.5 cm – it will fit in almost any bag or backpack. Plus, the laptop has a well-implemented cooling system: it is quiet even under load, so no heart-rending howl of fans will be recorded.
Almost everything depends on the sound quality in a podcast — you won’t be able to keep the audience even with the most interesting topic and the coolest guest if your conversation is accompanied by crackling, noises and echoes. The sound quality is directly affected by the microphone – let's talk about them in more detail.
For starters, there should be as many microphones as there are people in the conversation. If you're new to the world of podcasting, don't buy a port studio or mixing console right away: if your new hobby doesn't get you going, you'll be sorry for the money spent.
Start with a simpler solution – a USB microphone or a regular lavalier. Fundamentally, except for the form factor and connection method, they do not differ from each other: both start at about a thousand rubles for a decent entry-level model. Among lavalier microphones, for example, Boya BY-M1 can be distinguished – this is a budget and fairly high-quality device that will cost you about 1,500 rubles.
Pay attention to the interface: if you are going to write podcasts through a laptop, options with a regular 3.5 mm jack will do, if on a smartphone, it is better to look for a suitable USB or Lightning option.
However, microphones differ not only in connection method, but also in design: dynamic and condenser microphones can be distinguished here. For a beginner, it is definitely better to stop at the first option: such microphones have low sensitivity, therefore they “eat up” extraneous sounds, noises and echoes. Condenser microphones are extremely demanding on sound quality and room acoustics.
There is no universal advice on a microphone brand – you can look at the products of Audio-Technica, Behringer, Sennheiser, AKG.
Are you thinking of beginning your career as a podcaster for enjoyment? It is likewise suitable for spreading information. In order to start podcasting you will require a powerful computer and some other gear for that. So, nowadays I am reviewing some best laptops for podcast recording for you so you can choose one for yourself. You will need an ideal computer with great storage, a proper size display screen, and an effective processor for podcasting. But this is not all. Additionally, you should take into consideration some other factors as well such as battery life, memory space, and price. So, check them all out!
A powerful, durable laptop with a unique design that lets you get more out of your games. Comes with the ASUS Aura RGB Lighting system, so you can personalize your keyboard, case, mousepad, and more to match your mood. Powered by the latest Intel Core i7-8750h processor and NVIDIA GeForce RTX2080 graphics, the S15 is ready for the most challenging tasks. The keyboard and trackpad are backlit for convenient typing and gaming, while a large display offers a vibrant and immersive viewing experience. With two USB-C ports, one HDMI port, and a Thunderbolt 3 port, the S15 is equipped to connect to multiple devices simultaneously. The S15 also features a fingerprint sensor for added security, and it can be personalized with your favorite ROG gaming keycaps.
You can do editing on a 13-16 inches MacBook. However, using 13 inches notebook will best for travel also.
This laptop comes with a black gloss body and a 16 inches display screen. Also, the screen contains narrow bezels with a webcam in the middle. This Apple laptop has a 9th generation processor of MAC and 512GB storage. And the MacBook Pro is 16 inches in all.
Its screen is 16 inches with an eye retina display. The high definition display gives bright white colors and profound black color results. Also, the IPS panel display helps to protect your eyes from irritation. Its brightness is also 500 astounding nits.
This 16 inches device comes with a scissors tactile keyboard. Also, its keys have a long travel distance. These keys are quick and respond finely. You will also find a touch bar at the top center of the keyboard. It helps you get shortcuts. Moreover, you will also get a fingerprint secret log in here.
The audio quality is also excellent on this laptop, as I said earlier. There are six speakers audio system on the computer. You will enjoy thrilling sound in high volume. Also, you will enjoy lower work as well.
What’s the difference between Artificial Intelligence, Machine Learning, and Data Science?
In the global human sense, AI is the broadest term. It includes both scientific theories and specific technological practices for creating programs close to human intelligence.
AI section actively applied in practice. Today, when it comes to using AI in business or manufacturing, Machine Learning is most often meant.
ML algorithms usually work on the principle of a learning mathematical model that performs analysis based on a large amount of data, while conclusions are drawn without following rigidly defined rules.
The most common type of task in machine learning is supervised learning. To solve this kind of problems, training is used on an array of data, for which the answer is known in advance (see below).
The science and practice of analyzing large amounts of data using all kinds of mathematical methods, including machine learning, as well as solving related problems related to the collection, storage and processing of data arrays.
Data Scientists are data scientists, in particular, analyzing using machine learning.
One of the Machine Learning methods. An algorithm inspired by the structure of the human brain, which is based on neurons and the connections between them. In the process of training, the connections between neurons are adjusted in such a way as to minimize the errors of the entire network.
A feature of neural networks is the presence of architectures suitable for almost any data format: convolutional neural networks for analyzing pictures, recurrent neural networks for analyzing texts and sequences, autoencoders for data compression, generative neural networks for creating new objects, etc.
At the same time, almost all neural networks have a significant limitation – they need a large amount of data to train them (orders of magnitude more than the number of connections between neurons in this network). Due to the fact that recently the volume of data ready for analysis has grown significantly, the scope of application is also growing. With the help of neural networks, today, for example, image recognition problems are solved, such as determining the age and gender of a person from video, or the presence of a helmet at a worker.
Artificial intelligence is a technology, or rather a direction of modern science, which studies ways to train a computer, robotic technology, and an analytical system to think intelligently like a person. Actually, the dream of intelligent robotic assistants arose long before the invention of the first computers. Artificial intelligence (AI), machine learning, and neural networks are terms used to describe powerful machine learning-based technologies that can solve many real-world problems. While computers originally lacked functions such as thinking and making informed decisions, in recent years, several important discoveries have been made in the field of AI technology and related algorithms. An important role is played by the increasing number of large samples of various data available for training AI – Big Data.
AI technology overlaps with many other fields, including mathematics, statistics, probability theory, physics, signal processing, machine learning, blockchain, computer vision, psychology, linguistics, and brain science. Issues related to social responsibility and the ethics of AI creation attract interested people in philosophy. The motivation for the development of AI technologies is that tasks that depend on many variable factors require very complex solutions that are difficult to understand and difficult to manually algorithmize. Modern machine learning and AI technologies, coupled with properly selected and prepared “training” data for systems, can allow us to teach computers to “think” for us – to program, compose music, analyze data and make independent decisions on their basis.
Artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is often applied to a project for the development of systems endowed with human-specific intellectual processes, such as the ability to reason, generalize, or learn from past experiences. In simple terms, AI is a crude mapping of neurons in the brain. Signals are transmitted from neuron to neuron and, finally, are output – a numerical, categorical or generative result is obtained.
An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in artificial neural networks. Artificial neural networks were created as a mathematical model of the human brain. To do this, scientists Warren McCulloch and Walter Pitts had to develop a theory of the activity of the human brain. In it, individual neurons are living cells with a complex structure. Each neuron has dendrites – branched processes that can exchange signals with other neurons through synapses, as well as one axon – a larger process responsible for transmitting impulses from the neuron. Some synapses are responsible for the excitation of a neuron, some for inhibition. The impulses that it will transmit to other neurons will also depend on what signals and through what synaptic connections will come to the “input” of the neuron.
Probably, every systems or business analyst at some stage of his career thinks that it would be nice to get a professional certificate. There is a number of books dedicated to this topic – CBAP book and study materials are probably the best – but in this article I will try to answer the question – is it necessary and why?
There are several organizations in the world that allow business analysts to obtain certification and thereby confirm their professional level. I have looked at the most common organizations and certificates, namely:
International Institute of Business Analysis (IIBA). Offers certifications for analysts of all levels, from beginner ECBA to seasoned CBAP professionals.
Certified Analytics Professional (CAP) also offers two levels of certification.
The Project Management Institute (PMI) is best known for its Project Manager certifications. But they also offer PMI-PBA certification for business analysts.
The International Requirements Engineering Board (IREB) offers multiple levels of CPRE certification for requirements analysts, which is more suitable for IT analysts.
The International Qualification Board for Business Analysis (IQBBA) offers two levels of certification: entry-level analyst and advanced analyst.
Basically, these certificates are positioned for specialists in business analysis, but in our country they are also considered as confirmation of the level for system analysts, requirements analysts, software analyst, etc. There is a huge field for discussion, but I’m not talking about that.
I think that every analyst, having delved into the subject, will be able to find an answer to this question. I have considered three, the most frequently cited reasons, in principle, for any certification:
certified professionals earn more;
preparation for certification helps to organize your knowledge and allows you to identify gaps / gaps in the profession;
certification is such a way to prove to yourself that you are cool)
Let’s take a closer look. I launched a survey in communities and analyst chats and collected about four dozen responses about what analysts themselves think about this.
I recommend getting certified. But before that, the specialist should evaluate his capabilities and the very need to pass the exam for the standard in the current period of his career. Because it is important to clearly understand your expectations from certification: is this particular certificate suitable, what benefits it will bring in the work both to the specialist himself and to the employer. In addition, it is worth checking if there is an easier and faster way to get the expected benefits, and assess whether it will be possible to allocate time to prepare for certification, if it is still needed.
Certification is useful for consolidating knowledge and improving the quality of your artifacts in real work. In addition, reliance on theory helps to steer the discussion in a constructive direction, be it a speech at a conference or a meeting within the team. Another not obvious advantage of certification is the allocation of dark and light areas in their own competencies. That is, the standard helps to understand what the specialist has already succeeded in, and what needs to be improved, which topics are completely new. He can draw up an individual development plan in accordance with the standard, demonstrate his strengths to the manager and understand what new skills need to be mastered in order to request the corresponding tasks.
But you shouldn’t consider certification as a tool to raise wages in your current job. However, the specialist can agree with the manager about the payment or the allocation of working hours to prepare for the exam. From the point of view of the manager, the presence of a certificate will not be decisive, but it will definitely distinguish the employee among hundreds of others.
Any theory will vanish if it is not worked out in practice. There is no need to memorize anything – it will be useless both for passing the exam and for your development. To prepare, a specialist should consistently study the standard and immediately look for tasks for applying the knowledge gained. And in case of any doubt, whether he is doing the right thing or how best to act in a situation, refer to the standard as a reference, find the necessary information and apply it.
A community of like-minded people also helps with the preparation, who also began to prepare for the certificate you have chosen. If no one is around, then create this movement yourself within the company or region. Lively discussion and outside opinions will have a beneficial effect on both motivation and the end result.
