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Top 10 trends in payments 2018 (Infographic)
The universe of banking and payments is ever evolving. 2017 has seen a number of significant changes in the payments industry, thanks to advances in technology. Consumers now have access to a myriad of ways to pay. As a result, payment and shopping habits change. e-Commerce and m-Commerce methods such as in-app and one-click commerce are becoming increasingly popular. In addition, the exponential growth of IoT, one can foresee a wealth of new payment use-cases over the next few months. In this infographic, we present 10 key trends that will shape the payments industry in 2018.
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20 free books to get started with Artificial Intelligence
Are you searching for some best books to get acquainted with the basics of AI? Here is our list!
1. A Course in Machine Learning
Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
2. Simply Logical: Intelligent Reasoning by Example
An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material. A unique advantage to this work is the combination of AI, Prolog and Logic. Each technique is accompanied by a program implementing it. Seeks to simplify the basic concepts of logic programming. Contains exercises and authentic examples to help facilitate the understanding of difficult concepts.
3. Logic for Computer Science: Foundations of Automatic Theorem Proving
Covers the mathematical logic necessary to computer science, emphasising algorithmic methods for solving proofs. Treatment is self-contained, with all required mathematics contained in Chapter 2 and the appendix. Provides readable, inductive definitions and offers a unified framework using Getzen systems.
4. Artificial Intelligence: Foundations of Computational Agents
This textbook, aimed at junior to senior undergraduate students and first-year graduate students, presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture.
5. From Bricks to Brains: The Embodied Cognitive Science of LEGO Robots
From Bricks to Brains introduces embodied cognitive science and illustrates its foundational ideas through the construction and observation of LEGO Mindstorms robots. Discussing the characteristics that distinguish embodied cognitive science from classical cognitive science, the book places a renewed emphasis on sensing and acting, the importance of embodiment, the exploration of distributed notions of control, and the development of theories by synthesising simple systems and exploring their behavior.
6. Practical Artificial Intelligence Programming in Java
This book has been written for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical AI programming techniques. In the style of a “cook book”, the chapters in this book can be studied in any order. Each chapter follows the same pattern: a motivation for learning a technique, some theory for the technique, and a Java example program that you can experiment with.
7. An Introduction to Logic Programming Through Prolog
This is one of the few texts that combines three essential theses in the study of logic programming: the logic that gives logic programs their unique character: the practice of programming effectively using the logic; and the efficient implementation of logic programming on computers.
8. Essentials of Metaheuristics
The book covers a wide range of algorithms, representations, selection and modification operators, and related topics, and includes 70 figures and 133 algorithms great and small.
9. A Quick and Gentle Guide to Constraint Logic Programming
Introductory and down-to-earth presentation of Constraint Logic Programming, an exciting software paradigm, more and more popular for solving combinatorial as well as continuous constraint satisfaction problems and constraint optimisation problems.
10. Clever Algorithms: Nature-Inspired Programming Recipes
This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralised manner. These standardised descriptions were carefully designed to be accessible, usable, and understandable.
11. Clever Algorithms: Nature-Inspired Programming Recipes
Covers the mathematical logic necessary to computer science, emphasizing algorithmic methods for solving proofs. Provides readable, inductive definitions and offers a unified framework using Getzen systems. Offers unique coverage of congruence, and contains an entire chapter devoted to SLD resolution and logic programming (PROLOG).
12. Common LISP: A Gentle Introduction to Symbolic Computation
This highly accessible introduction to Lisp is suitable both for novices approaching their first programming language and experienced programmers interested in exploring a key tool for artificial intelligence research.
13. Bio-Inspired Computational Algorithms and Their Applications
This book integrates contrasting techniques of genetic algorithms, artificial immune systems, particle swarm optimisation, and hybrid models to solve many real-world problems. The works presented in this book give insights into the creation of innovative improvements over algorithm performance, potential applications on various practical tasks, and combination of different techniques.
14. The Quest for Artificial Intelligence
This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers.
Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications.
16. Virtual Reality – Human Computer Interaction
At present, the virtual reality has impact on information organisation and management and even changes design principle of information systems, which will make it adapt to application requirements. The book aims to provide a broader perspective of virtual reality on development and application.
This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field.
18. Machine Learning, Neural and Statistical Classification
This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems.
Ambient Intelligence has attracted much attention from multidisciplinary research areas and there are still open issues in most of them. In this book a selection of unsolved problems which are considered key for ambient intelligence to become a reality, is analysed and studied in depth.
20. The World and Mind of Computation and Complexity
With the increase in development of technology, there is research going into the development of human-like artificial intelligence that can be self-aware and act just like humans. This book explores the possibilities of artificial intelligence and how we may be close to developing a true artificially intelligent being.
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Top 5 best data visualization techniques for 2018
People are able to process visual information much faster than textual content. For this reason, data visualization is an essential model of contemporary business intelligence. There are hundreds of techniques to present business-related information and sometimes it might appear challenging to find the best one for you.
In case you are running out of time, you should understand the basic data visualization tactics and keep in mind the catalog of charts, graphics, and diagrams. Luckily enough, you can count on our help in that regard. In this article, we will describe the top 5 best data visualization techniques.
Best Data Visualization Tactics
Data visualization can communicate complex information in a way that is easier to interpret by turning information into visually engaging images and stories. It enables you to highlight the most relevant conclusions from what would otherwise be considered a huge pile of worthless documents.
But what is the way to approach data visualization? What are the most efficient techniques? Let’s take a look at the 5 best models here:
- Get to know the audience
The age of IT and Internet as we know it seems to last forever but don’t forget that it is still in its 20s. On the other hand, data visualization is even younger than that. A lot of people – even entrepreneurs – don’t know how to read more than a simple chart or pie. That’s why you need to understand the target audience and adapt the presentation so as to match their IT literacy.
Lucille Neely, a digital marketing specialist at Best Dissertation, explained it concisely: “If you are dealing with inexperienced clients, stay away from advanced solutions. But if you are meeting highly skilled professionals, going beyond pies and charts is mandatory”. Therefore, you must get to know the audience you face and give them materials they can digest successfully.
- Think about the content
What you want to present is as important as who you are showing it to. There are 4 basic ways to approach data visualization:
- Relationships: Shows the connections and mutual impact between specific elements (such as education level and average income). Scatter plot is the best choice in this case.
- Timeframe: Line graphics suits perfectly if you want to show how certain phenomenon is developing over time.
- Composition: This technique is developed to reveal the structure of a single unit, showing its constitutive elements. A pie chart is the simplest way to do this but if you want a more distinguished data visualization, go for the 100% stacked horizontal bar graph or a slope graph.
- Comparisons: Bar charts are the usual suspect if you want to compare two or more values.
- Mind the colors
Although it seems irrelevant, the colors you choose will strongly impact the overall effectiveness of your data visualization model. You should keep 2 things in mind here: color consistency throughout the documents and the contrast.
First of all, you should make contrasts between the opposing elements, emphasizing the differences among these features. People mostly use red, green, blue, and yellow because they can be recognized and distinguished easily.
Secondly, you should not mix the colors too much because it creates confusion among viewers and interferes with already established patterns. For example, if you used red to mark negative trends and green to highlight positive outcomes, don’t change the style throughout the document.
- Use interactive maps
Data visualization can become a source of valuable digital content, which demands adding interactive elements to the presentation. Interactive maps play the major role in that regard because they allow users to engage and look only for information that they really need.
Interactive maps enable users to wander around the chart, zoom in and out, identify special elements upon click, get a 360-degree overview, and many other interesting features. Creating such maps is a highly complex process but it will definitely leave a great impression on your clients or customers.
As the matter of fact, interactive maps had already become a standard technique for the vast majority of companies and websites, with the likes of Google, Booking.com, or National Geographic setting a good example for data visualization community.
- Use digital tools
There are dozens of incredible data visualization tools available online. We strongly suggest you use some of these programs to create custom tables because it’s the only way to impress your clients in 2018. Here are our recommendations:
- Microsoft Power BI: This app is easy to use and extremely intuitive. It is available in free version, so you can give it a try before deciding whether to conduct the purchase.
- Zoho Reports: The tool has tons of beginner-friendly features but also a lot of advanced possibilities, which makes it suitable for all levels of expertise.
- Chartio: Chartio is difficult to learn but very good for data visualization professionals.
Conclusion
Data visualization can help you to create better and more appealing business reports, maximizing the potential of your analysis. If you want the attention of your clients and colleagues, you need to learn modern data visualization models to improve the quality of your presentation.
In this article, we showed you top 5 best data visualization techniques for 2018. Give our tips a try and don’t hesitate to leave us a comment if you have more suggestions to share with our followers.
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Once financially beneficial data is turning on companies that collect it
There was a time when data collection was a lucrative practice for companies. That was due to the understanding that the more a brand knows about its target market, the easier it is to address those people and meet their needs. However, recent cyberattacks have made companies think more carefully about whether it’s worthwhile to gather data after all.
High-Profile Data Breaches Put Companies at Risk
Hackers’ methods are increasingly sophisticated, and even when companies have cybersecurity strategies in place, it’s still often possible for criminals to find weak points, infiltrate a system and grab data, often going for months or even longer without detection.
Also, some high-profile data breaches are so substantial, they make people wary of ever doing business with a brand again. A few years ago, a Yahoo data breach victimized all of its three billion users.
More recently, Equifax, a company responsible for keeping data about consumer credit scores, revealed cybercriminals snatched records associated with 143 million people. Most of those individuals had their account details taken, but about 200,000 of them had credit card information and social security numbers compromised, too.
These are just two of the numerous substantial data breaches of recent times. Both caused negative press, tarnished opinions and resultant reputational damage.
Most of the Data Is Valuable to Hackers
Data security company Gemalto publishes a Breach Level Index that tracks statistics of all data breaches occurring since 2013. Alarmingly, it found that only four percent of data taken during those attacks was encrypted, making the material useless to the people who took it.
Not surprisingly, the stolen data represents significant financial losses for targeted companies, too. Statistics from the Ponemon Institute indicate each stolen or lost record costs a company $158.
Those vast financial stakes make it necessary for companies to invest in what’s known as sustainable security solutions, or those that proactively evolve to conquer new challenges. However, some aren’t willing to do so due to the initial costs involved.
The Expense of Meeting Regulatory Requirements
When companies collect data, they have to meet associated regulations in their home countries as well as the places where their worldwide customers reside.
For example, in May of 2018, the European Union will enact the General Data Protection Regulation (GDPR), which requires companies to report data breaches within 72 hours or face massive fines.
It affects most U.S.-based companies, too. If those entities collect data from EU subjects residing in EU member states at the time of collection, they have to comply with the GDPR as long as they’re actively targeting those European consumers. Analysts say accepting a foreign currency or having dedicated websites for visitors from Europe both qualify.
A PwC poll of multinational companies found 77 percent planned to spend $1 million or more to become compliant. However, a survey carried out by Vanson Bourne and published in September 2017 found 37 percent of global respondents were not sure if they needed to comply with the GDPR. Also, 28 percent believed they did not need to comply at all.
The maximum fine imposed by noncompliance with the GDPR is €20 million or four percent of a company’s annual worldwide turnover, whichever is greater. Some people speculate how often fines will be imposed and how severe they’ll be for an infraction that is not so egregious.
Data Breaches of Any Size Can Be Detrimental
Businesses typically want to do all they can to achieve compliance and keep data safe from hackers because the price for failing to do so is simply too great. The data breaches mentioned above — as well as most others — dominate headlines.
As a result, consumers are more aware of them than they once were and often look for ways to opt out of data collection procedures a company uses.
Additionally, it doesn’t matter how big the data breach is. It will still have a negative impact on the company involved. Once the news breaks about an attack, people quickly conclude that the affected business was lax in keeping its networks secure.
That also means winning back trust is a time-consuming and often monetarily costly exercise. Sometimes, the efforts made to do so only make matters worse.
For instance, Equifax allowed people to go online to check whether or not they had possibly been affected — but that process often left people with more questions than answers.
It’s not hard to see why many companies realize the cost of collecting data and holding onto it could be more expensive than not having that material in the first place. Therefore, the near future may show businesses deciding to readjust their practices.
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The difference between data scientists, data engineers, statisticians and software engineers
Finding out the difference between data scientists, data engineers, software engineers, and statisticians can be confusing and complicated. While all of them are linked to data in a way, there is an underlying difference between the work they do and manage.
The growth of data and its usage across the industry is hidden from none. During the last decade in general, and the last couple of years in particular, we have seen a major distinction in the roles tasked with crafting and managing data.
Data Science is without a doubt a really growing field. Organizations and even countries from across the globe have experienced a drastic rise in their data collection endeavors. With numerous complications associated with collecting and managing data, this field is now host to a wide array of jobs and designations. We now have data scientists who are grouped into more specific tasks of data engineers, data statisticians, and software engineers. But other than the difference in their names, how many of us can comprehend the diversity in the work they do?
As I guessed, not many people can guess the job that these data experts are up to. Many of us eventually come to the conclusion that all of them do the same job and are grouped differently for the sake of it. There is nothing more mistaken then this myth and for this purpose I have turned up as a myth buster today to put an end to the conflict in understanding the role of these jobs present in the data industry. While all of them help propel the movement towards authentic data creation by architecting the growth upwards, there is a major difference in how and why they come into the perspective.
Here I have outlined some of the major attributes of these four subcategories that come in the bigger picture of managing and looking over data. They say ignorance is bliss, but it is always better to know the real picture than to shy away from it.
Statistician
The statistician sits right at the forefront of the whole process and applies statistical theories to solve numerous practical problems pertaining to a plethora of industries. They have the leverage and the independence to determine the method deemed feasible for finding and collecting data.
Since statisticians are deployed to collect data through meaningful methods, they design surveys, questionnaires, experiments, etc., to collect data.
They analyze and interpret the analyses from the data and report all the conclusions that they find through their analyses to their superiors. Statisticians need to boast of analytic skills along with the ability to interpret data and narrate complex concepts in a simple, understandable manner.
Statisticians understand the numbers that are generated through research, and apply these numbers to real life issues.
Software Engineers
A software engineer sits at an important front of the data analytic process and is responsible for building systems and applications. Software engineers will be part of the process of developing and testing/reviewing systems and applications. They are responsible for creating the products that ultimately lead to the creation of the data. Software engineering is probably the oldest one of all these four roles and was an imperative part of society way before the data boom began.
Software engineers are responsible for developing frontend and backend systems that help collect and process data. These web/mobile applications lead to the development of the operation system through a flawless software design. The data that is generated through the apps created by software engineers is then passed on to data engineers and data scientists.
Data Engineer
A data engineer is someone who is dedicated towards developing, constructing, testing, and maintaining architectures, such as a large scale processing system or a database. The main difference between a data engineer and its often confused alternative data scientist is that a data scientist is someone who cleans, organizes, and looks over big data.
You might find the use of the verb “cleans” in the comparison above really exotic and inadvertent, but in fact it has been placed with a purpose that helps reflect the difference between a data engineer and data scientist even more. In general, it can be mentioned that the efforts that both these experts put in are directed towards getting the data in an easy, usable format, but the technicalities and responsibilities that come in between are different for both of them.
Data engineers are responsible for dealing with raw data that is host to numerous machine, human, or instrument errors. The data might contain suspect records and may not even be validated. This data is not only unformatted, but also contains codes that work over specific systems.
This is where data engineers come in. Not only do they come up with methods and techniques to improve data efficiency, quality, and reliability, but they also have to implement these methods. To manage this complication, they will have to employ numerous tools and master a variety of languages. Data engineers actually ensure that the architecture that they work upon is feasible for data scientists to work with. Once they have gone through the initial process, the data engineers will then have to deliver or transfer the data over to the data scientist team.
In simple terminology data engineers ensure the flow of data in an uninterrupted way through servers. They are mainly responsible for the architecture needed by the data.
Data Scientists
We now know that data scientists will get data that has already been worked upon by data engineers. The data has been cleaned and manipulated and can be used by data scientists to feed analytic programs that prepare the data for its use in predictive modeling. To build these models, data scientists need to do extensive research and accumulate high volume data from external and internal sources to answer all business needs.
Once data scientists are done with the initial stage of analysis, they have to ensure that the work they do is automated, and that all insights are duly delivered to all key business stakeholders on a routine basis. It is indeed noticeable that the skill set needed for being a data scientist or a data engineer as a matter of fact is slightly similar. But the two are gradually becoming even more distinct within the industry. Data scientists need to know the intricate details related to stats, machine learning, and math to help build a flawless predictive model. Moreover, the data scientist also needs to know details pertaining to distributed computing. Through distributed computing, the data scientist will be able to access the data processed by the engineering team. The data scientist is also responsible for reporting to all business stakeholders, so a focus on visualization is necessary.
Data scientists use their analytical capabilities to find out meaningful extracts from the data that is being fed to the machine. They report the final results to all the key stakeholders. The field of data is a growing one, and encompasses way more possibilities than what we had imagined before.
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How to make hiring algorithms work in your favor
The concept of recruiting and hiring has always come with a certain degree of risk attached to it. All too often, hiring managers are drawn in by an impressive resume, fancy dress clothes, an upbeat and confident demeanor, and a handful of positive interactions.
While the initial meetings and interviews may set high expectations, companies never really know what they are getting with a new hire until months later. A single bad hiring decision can do a lot of damage to a company’s budget and reputation. From a financial standpoint, companies lose an average of $14,900 on every bad hire, according to CareerBuilder.
Fortunately, the rapid advancement and sophistication of big data have made its presence known in the hiring process. The results of this can do a lot to cut down on turnover. However, understanding how to make these algorithms work for your specific organization will require a good deal of time and commitment. Here is how to do it.
Know the data sources
For companies that are new to the whole concept of big data, one of the toughest parts can simply be knowing where to look for the most pertinent information.
In terms of hiring, these algorithms normally work within a relatively narrow scope of information. Many HR departments utilize three major source categories of data. These include:
- Publically available information
- Background information (provided by the applicant)
- Interaction data
Publically available data can come from a wide range of sources. These can include social media, demographic information of the area, pay scale, employment rate, and much, much more.
The background information is what hiring managers see on a resume, or any other credentials submitted by the applicant. These typically relate to skills, qualifications, and experience.
Interaction data refers to the small insights gleaned from how an applicant communicates with a company. These insights come from things like keystrokes, word choice, and answers to questions. The metrics can have a strong correlation with future job performance.
Once you have identified the data sources necessary for the hiring process, your data mining tool will be able to run analyses to find the context you need to make more informed choices.
Understand key variables for each position
Upon finding the ideal sources, one of the biggest data-related challenges companies face is knowing exactly which metrics pertain to their goals, and how to apply them. According to IBM, about 2.5 quintillion bytes of data are created every single day. That being said, locating the right information can seem like finding a needle in a haystack.
Depending on the position you are recruiting for, there will likely be a wide range of data variables that play into the equation. This is one of the areas where there tends to be a high margin of error. Keep in mind, algorithms can only work for you if you have all the information necessary.
Therefore, you need to have a crystal clear objective in mind for the exact variables that pertain to the job, as well as how you can leverage them to eliminate the guesswork. These may include college GPA, certain buzzwords from previous jobs, soft skill proficiency, certain personality traits, etc.
Fortunately, there are plenty of tools to help you with this part of the process. AI-driven “smart” recruiting tools like Harver are designed to automatically screen applications and background information to identify the ones with the strongest correlation to the open position. From here, it runs a number of specialized assessments to gauge the applicant’s interaction data related to problem-solving, communication skills, situation judgment, and more.
Once the candidate has completed the assessments, the system uses smart algorithms to determine the strength of each candidate and how well they fit the mold for not just the open position, but the company as a whole.
Even though big data can work wonders in making smarter hiring decisions, it’s important to remember that there will always be a good amount of human intuition and iteration involved as well. Big data algorithms are simply there to guide you.
Use each interaction as a predictive data point
Big data, in general, can best be described as a constant work in progress. Datasets are continuously building off of each other to become smarter and more precise.
As you begin to develop a bank of data relating to your hiring process, there will almost certainly be a number of patterns that will emerge. These patterns should serve as a reference to how people mesh with your company. For example, in terms of communication, the datasets might show that the best workers in your company were the ones who responded to messages from the hiring managers within one hour. Or, perhaps the ones who sent shorter and more concise emails had a better success rate in the company.
BI tools like Dundas make the concept of predictive analysis simple. The browser-based solution allows you to input any data source and view the trends in customizable, interactive reports.
From here, you can draw on previous datasets to justify decisions for the future. The goal of hiring managers is to stay one step of head of common issues like poor productivity, employee turnover, bad cultural fits, and more. If you use every single interaction as a predictive data point and keep a close eye out for trends, you are in a much better position to avoid mistakes and misjudgments down the road.
Over to you
Turning your company into a data center has many benefits. In regards to the hiring process, managers need to do everything they can to make smarter decisions and avoid the dreaded high turnover rate. In the age of constant-connectedness, a high turnover rate isn’t just bad for your budget; it’s a huge red flag for new talented candidates.
While there are very few guarantees in the business world, one of the safest bets is that big data is here to stay. The sooner you can get the algorithms working for you, the better you will be in the long run. Always remember, a business is only as good as the people it brings on board.
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