Data Science Online Gambling

• Data Science Online Gambling Games
• Data Science Online Gambling Sites
• Data Science Online Gambling Websites
• Data Science Online Gambling Course

Top 5 Online Casinos. Discover loads of curious Casino Guides! Live Casino Reviews; Top 5 Live Casinos. Discover loads of curious Casino. But most of this data is disregarded despite its value to the gaming industry. This course will show you how to store and analyse data effectively and gain insights into game users’ actions and behaviours. You’ll find out about the different models of data, such as tabular data, atomic data, and relational data. UWF offers an online master of science in computer science that prepares students for doctoral studies or careers in software development and data analytics. Students can choose between two concentrations: software engineering and data science. UWF's online master's in computer science degree requires 30 credits.

Welcome to Academic Press Network website!

Have you ever heard about Big Data, Data Science, Machine Learning, Artificial Intelligence, Natural Language Processing Technologies, Data or Text Mining? How do these strange and intellectual words can help players? This may definitely surprise you.

Data Science Online Gambling Games

The Apnet team has deep knowledge of this science. We analyze the available data in the field of iGaming and are happy to share our outcomes with players around the globe. Every person interested in online gambling will find useful information here for sure that can be used in the future.

Mission / Project goal

is to carry out an analysis of iGaming topics in order to identify trust casinos with generous bonuses applying the updated intellectual methods of information processing covered in reputed academic press.

Data Science Online Gambling Sites

Tasks to solve:

• Sentiment analysis of relevant content as well as mathematical modeling of aggregated reviews;
• Simulation technique to analyze RTP indicators based on empirical academic press;
• Drawing of mathematically proven strategies for the most popular iGaming selection;
• Preparation of multi-regional iGaming guide considering cultural and regional specifics of gambling in each country.

Objects of analysis

News portals and forums, review & ratings sites on slot machines and online casinos, data of software providers, as well as academic press in the field of iGaming.

Data and research background:

numerical and textual information regularly published in academic press (scientific journals) covering gambling topics.

Our team:

Data Science Online Gambling Websites

The team consists of mathematicians, analysts, marketing experts with solid experience and academic background in statistical modelling, machine learning, pure mathematics and simulation of macro- and microeconomic processes. We regularly receive new knowledge both from the practical experience and study new theoretical trends from international academic press. For this project iGaming market was chosen as an object of research.

Can Academic Press Really Help Gamblers?

By means of up-to-date scientific methods and models, we will help you not only to choose the most suitable casino games for your needs and style, but also tell you how to increase winning odds based on fundamental academic knowledge. The information processing methods will help you to form an opinion about the proposed selection on the ground of mathematically accumulated feedback, which is the result of balanced aggregation of available reviews and ratings at free access.

Personal data has recently become more public, albeit anonymized. When was the last time you saw statistical summary in the news? Any information, whether it’s anthropometric data or the sum of money which he or she spends on clothes, food, academic press and entertainments is subject to storage and processing. Even people who are far from applied mathematics are facing concepts such as big data, data science, data mining, machine learning, artificial intelligence, NLP technologies. The Apnet team will help you understand these concepts, as well as teach you how to benefit from academic press.

We don’t want to fill up heads of our readers with complex terms and calculations, so this article will only reveal the most applied aspects. Just use ready-made and free solutions. Furthermore, let some information still remain confidential, perhaps we will reveal some of the secrets in coming papers.

Best Tips We Have Learned From Academic Press and Now Share

100% honest & unbiased online casino reviews

In order to make their own opinion before testing, most people read reviews. Before going to the cinema, we watch trailers, read feedback of critics and people who have already taken in a movie. Before you buy any book or academic press in online store, you also primarily refer to the reviews as a rule.

But how can you trust them?

What is the probability that those two or three positive reviews at the top of the page are credible and not paid? Users rarely scroll through reviews feed, because it takes a lot of time, besides it’s quite time-consuming task to keep array of information in mind.

How Machine Learning helps to publish honest reviews?

Data and Text Mining in line with Natural Language Processing Technologies step in here, taking the key place among popular topics in academic press over the past five years.

You may have heard that a group of researchers analyzed tweets about the release of Star Wars movie and get real how the audience liked the movie based on reviews tone, as well as which characters caused negative emotions. Similar technologies allow the team of Apnet.com generate true online reviews on iGaming topic.

Data Science Online Gambling Course

We analyze a large number of texts and keep on going over determining the overall tone of reviews. Advanced methods gained from up-to-date academic press allows us to generate a balanced review that takes into account the most important and referenced facts about casino, as well as true assessment of real players.

One-Shot Casino Review

Reading one review provided by the team of Apnet.com, you get a balanced set of all available reviews at free access, while receiving relevant and high-quality information which can assist you forming your own opinion.

Your science-based guide by country

Tips on choosing gambling places, slots or other games cannot be common for each country for the following reason – games selection and RTP indicators are based on the psychosomatic and cultural characteristics of the population in each country, risk loving or quiet game’s preference. Here is pure win – win solution for all participants. Thus, APnet team has compiled casino guides for the next countries:

Mathematical Fundamentals of Online Gambling

In order to have a realistic view of winning odds in online casinos, it’s necessary to clearly comprehend such concepts as mathematical expectation of winning, mathematical expectation of risk, as well as variance of winning and risk. It should be understood, there are not only games with positive expectation for players, but also with negative:

“If you want measured steady gaming, no losses, no winnings, just to pass the time – your choice should fall on the game with positive expectation and moderate variance,” Linda Jenkins (our math expert) said.

At the same time, understanding of variance gives you the idea how big your win or loss can be. In part, you can learn new information by familiarizing yourself with risk matrices. So you need to understand first what you want.

“If you are risky person, then we recommend to point out games with negative mathematical expectation, but large variance,” data analyst Anders Lindström commented out.

What Do We Have In Sum?

It’s dispersion variance that provides you with tickling nerves and possibility of big win. In addition, you need to understand the type of chosen game to determine the game’s strategy: with dependent or independent random variables. Games with dependent random variables require more concentration and attention, but they are also more controlled.

Safety & Right Choice of Online Casinos and Slot Machines

1. First of all, before you start playing make sure that your choice fell on certified online casino. You may trust the certificates provided by companies such as Technical Systems Testing (TST), Price Waterhouse Coopers (PWC) and eCOGRA.
2. The certificate has to provide RTP values and confirm seed values of RNG.
3. Moreover, you should not trust everything you see. For instance, certificate located on homepage may be faked, as any image is easy to falsify. If this certificate is genuine, you will be redirected to the company’s website that issued the license. If the chosen operator is certified properly, you should consider RTP in details.
4. Another good way to make sure that you are using a safe online casino is to check if it holds a license issued by a well-known government agency like the UKGC in the United Kingdom.
5. Check if the brand’s website is secure. Visit their website and have a look at the address bar. If you see the address start with Hyper Text Transfer Protocol Secure or HTTPS:// then it is all well and secure. If you see HTTP:// then it is best to avoid playing there.
6. Does the casino use a trusted software provider? Consider checking which software the brand uses. Some of the most trusted software providers are NetEnt, Playtech, and Novomatic.

Understanding RTP Calculus Will Save You From Learning Robinson-Sсhensted Algorithm (Not Us)

RTP is generally accepted parameter and the certificate contains RTP value for each game of the online casino you selected. Choosing slots game, you should study the available information about it, including RTP value, due to its frequent correlation with variance. Higher RTP value means the lower variance of winning, so player will receive small payouts on a more frequent basis.

The most common strategy of playing slots with low variance is to play for “short distances” – short time gaming at maximum rates. However, if you have sufficient margin of initial capital and patience, you should pay your attention to slots with lower RTP value.

Even though the expected return is lower due to higher variance after short series of losses, you have opportunity to get a jackpot or short series of big wins that will outweigh the cost of your waiting. These findings were obtained from the research conducted by APNet team.

First of all, having studied the academic press related to the law of large numbers [10], it was decided to:

• – make simulation modeling in order to select those casinos that follow the established parameters [11],
• – reveal if RTP coincides with established values of software providers,
• – identify the relationship between RTP values and variance of winnings.

The Monte Carlo method was chosen as simulation method, which is widely represented in authoritative academic press. At the first stage, we have considered online casinos and their slots with high RTP values and low dispersion in order to save the budget. To achieve this, sufficient number of users were registered in the framework of simulation making chaotic bets. Results on the winnings, number and size of bets were received in the simulation model for further analysis.

Mathematical features of Blackjack

Blackjack is a game with dependent random events and is one of the most controlled from player’s side. Here luck isn’t so important as correct calculations and skill of the player [5]. Moreover, this game has the greatest expectation of winning for players.

From the mathematical point of view, player should keep bust probability in mind for the current amount of points. Below we present short statistical summary based on probability determination according to the classical probabilistic space:

The presented empirical function of bust probability distribution takes place under the assumption that the game is played by one card deck.

Mathematical features of roulette

If your favorite game is roulette, you will be interested in the information below. Choosing between American or European roulette, preference should be given to European variant. Since the mathematical expectation of zero for European roulette is 1/37, which can be interpreted as casino takes 2.7% of all bets. According to the game theory, mathematical expectation of zero for American roulette is 2/38 or substantially 5.26% of all bets goes to the house.

“Despite the fact that roulette is very exciting game with millions of fans around the world, at the same time it’s pure mathematical system with 49×49 winning probability. Mixed strategies are usually applied, and if you delve into this area, you will learn a lot of new and useful information that will help you gaming in the future,”our marketing expert Paul explained.

However, considering roulette strategies it’s necessary to debunk the established myth of Martingale strategy, which was fully described in a number of articles at academic press few years ago. This strategy doesn’t work due to the limited financial and time resources of player, as well as due to the fact that most casinos have clear limits of maximum bets. Thus, adhering to pure Martingale strategy, you will put yourself into a situation where it’s impossible to win [9].

In addition, roulette is one of the few games in terms of winnings where mathematical expectation takes negative values (considering bet values), so it belongs to the game type for “short distances”. In case of “red or black” bets, variance isn’t clear cut. The method of fictitious play may be applied here. But it’s worth counting on when hunting for a win, as it compensates for negative expectation of the player.

Glossary

RTP(return to player) is an estimate of mathematical expectation of player’s win ratio related to casino. If RTP value for a particular slot is 0.95, it means that according to the law of large numbers, player will lose no more than 5% of amount deposited. However, if player has insufficient number of sessions, this ratio can vary enormously, both sides.

RNG(random number generator) – a truly random value, but iGaming online operators use PRNG – pseudo-random number generatorsas a rule. PRNG uses single initial value at the start of algorithm; and here its pseudorandomness appears, while RNG always forms a random number usually based on different sources of entropy [1,2]. In fact, RNG = PRNG + source of informational (physical, mathematical) entropy [3,4]. Currently a lot of academic press presents high-tech algorithms that can guarantee reliability, fraud protection in terms of algorithm’s hacking, such basic methods as linear congruent aren’t applied, while preference is given to methods of cryptographic encryption.

Sample space(space of elementary events) – a set consisting of all possible elementary outcomes. An elementary outcome is the result of random experiment. Let’s explain it in practice, for example – take dice. We have premise that two dice are thrown. Each dice has six faces, with values from 1 to 6. Then sample space is limited to 36 (number of all unique pairs), and elementary outcomes mean a set of unique pairs {(1,1),(1,2)…(6,5),(6,6)}.

Expected value– average of a random variable when the number of samples or the number of tests goes to infinity. Mathematical expectation is one of the basic concepts of probability theory. From casino’s point of view – expectation is approximate average value of player’s profit or winning probability. Let’s take the example with two dice again. If player makes a single bet, this implies one elementary outcome as winning, then success probability will be calculated as ratio of the number of favorable elementary outcomes to the total number of outcomes in this case. So our example shows that probability of success is 1/36, and mathematical value of win is 1/36 multiplied by amount of bet.

Dispersion (variance) of random variable– nominal measure of random variables spread to its mathematical expectation. The square root of dispersion is called standard deviation. An important mathematical calculation is that according to Chebyshev’s inequality, probability which values of random variable are disposed from its mathematical expectation by more than k standard deviations is less than . Dispersion is an objective factor, which undoubtedly must be considered, because it provides player with a big win or loss.

Independent and dependent random variables – mathematical definition of event independence sounds like: two events are independent if appearance of one event does not affect the probability of second event’s appearance. In other words, to understand whether your chosen game has dependent or independent outcomes, you need to answer the simple question: does the previous value of elementary outcome affect the current one? If the answer is yes, then you are dealing with dependent elementary outcomes in such games like blackjack, for example. In the classic version, the game is played with 8 decks of 52 cards each, but as the game progresses, probability of getting the right card changes, as the implemented space of elementary outcomes changes. Classic games with independent random variables are roulette and dice.

Empirical Distribution Function(EDF) is a function that defines probability that subsequent values will not exceed each value in the set.

Data mining– intellectual data analysis, the term was introduced in 1989 and is used to refer to a set of methods for knowledge detection necessary for decision-making in various spheres of human activity in some data previously unknown, non-trivial, practically useful and of accessible interpretation.

Having deep background in data mining, analysts group of Apnet.com gives you opportunity to obtain unique and reliable knowledge without prior familiarization with academic press, and immediately have information aimed at the result [7].

Simulation modelling– a research method where the studied system is replaced with model describing this system with sufficient accuracy, by means of which experiments are being conducted with the aim to obtain information about the system. At the same time, it is possible to repeat the experiment required number of times, while processes occurring in the model are random, which means that with a large number of computational experiment retries, researcher gets a complete picture of the changes taking place in the system and can predict to the specified accuracy how the system will behave in time, as well as extremal states and equilibrium.

Apnet team has sufficient data base and modern technical meansthat allows us to build detailed and accurate models to provide you with up-to-date and verified information [6]. One of the most authoritative academic press devoted to simulation modeling is the International Journal of Modeling and Simulation [12].

Natural Language Processing– general trend of artificial intelligence and mathematical linguistics. It studies the problems of computer analysis and synthesis of natural languages. Appling to artificial intelligence, analysis means the language understanding, and synthesis means generating a smart text. Solution of these problems allows to create the most convenient form of interaction between computer and human, and also provides high-speed analysis of large array of available text information [8].

Artificial intelligence – from the point of view of science and technology it’s creation of intelligent machines, in particular intelligent computer programs; in terms of properties – the property of intelligent systems to perform creative functions that are traditionally considered prerogative of man, writing texts in particular.

Perhaps here we’ll stop. Undoubtedly, we could explain other terms like game matrix, Nash Equilibrium, strategy space, fuzzy sets theory, Gaussian distribution ant etc.; but we want to focus on ready-made solutions that will be worthwhile to our users. After all, this great work has been done for you!

Academic Press Network – Apnet team want to thank these books and websites that inspired us to create such a cool project – Sources:

1. Donald Knuth, The Art of Computer Programming (Vol. 2 – Seminumerical Algorithms).
2. Bruce Schneier, Applied Cryptography (Chapter 16).
3. William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery. Numerical Recipes in C: The Art of Scientific Computing. – 2nd ed. – Cambridge University Press, 1992. ISBN 0–521–43108–5.
4. N.G. Bardis, A.P. Markovskyi, N. Doukas, N. V. Karadimas.True Random Number Generation Based on Environmental Noise Measurements for Military Applications// Proceedings of 8th WSEAS International Conference on Signal Processing, Robotics and Automation. – 2009. – ISBN 978-960-474-054-3. –ISSN1790-5117.
5. Mathematic academic press: https://www.elsevier.com/physical-sciences-and-engineering/mathematics/journals.
6. Eric Winsberg (2003), Simulated Experiments: Methodology for a Virtual World.
7. Ian Goodfellow Deep Learning (Adaptive Computation and Machine Learning): https://www.deeplearningbook.org.
8. http://www.mlyearning.org.
9. http://www.storytellingwithdata.com/book/.
10. https://www.sciencedirect.com/science/article/pii/S0888613X10001040.
11. https://www.sciencedirect.com/book/9780444515759/exploring-monte-carlo-methods.
12. https://www.tandfonline.com/loi/tjms20.

Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance.

Here is a list of data science use cases in banking area which we have combined to give you an idea how can you work with your significant amounts of data and how to use it effectively.

Fraud detection

Machine learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providing security to customers and employees. The sooner a bank detects fraud, the faster it can restrict account activity to minimize loses. By implementing a series of fraud detection schemes banks can achieve necessary protection and avoid significant loses.

The key steps to fraud detection include:

• Obtaining data samplings for model estimation and preliminary testing

• Model estimation

• Testing stage and deployment.

Since every data set is different, each requires individual training and fine-tuning by data scientists. Transforming the deep theoretical knowledge into practical applications demands expertise ins why risk modeling appears extremely substantial for banks and is best assessed with more information in hand and data science tools in reserve. Now, through the power of Big Data, innovators in the industry are leveraging new technology for effective risk modeling and therefore better data-driven decisions.

Personalized marketing

The key to success in marketing is to make a customized offer that suits the particular client’s needs and preferences. Data analytics enables us to create personalized marketing that offers the right product to the right person at the right time on the right device. Data mining is widely used for target selection to identify the potential customers for a new product.

Data scientists utilize the behavioral, demographic, and historical purchase data to build a model that predicts the probability of a customer’s response to a promotion or an offer. Therefore, banks can make an efficient, personalized outreach and improve their relationships with customers.

Lifetime value prediction

Customer lifetime value (CLV) is a prediction of all the value a business will derive from their entire relationship with a customer. The importance of this measure is growing fast, as it helps to create and sustain beneficial relationships with selected customers, therefore generating higher profitability and business growth.

Acquiring and retaining profitable customers is an ever-growing challenge for banks. As the competition is getting stronger, banks now need a 360-degree view of each customer to focus their resources efficiently. This is where the data science comes in. First, a large amount of data must be taken into account: such as notions of client’s acquisition and attrition, use of diverse banking products and services, their volume and profitability, as well as other client’s characteristics like geographical, demographic, and market data.

Image source: MYcustomer

This data often needs a lot of cleaning and manipulation to become usable and meaningful. The profiles, products, or services of the bank’s clients vary greatly, and so do their behaviors and expectations. There are many tools and approaches in the data scientists’ arsenal to develop a CLV model such as Generalized linear models (GLM), Stepwise regression, Classification, and regression trees (CART). Building a predictive model to determine the future marketing strategies based on CLV is an invaluable process for maintaining good customer relations during each customer’s lifetime with the company that results in higher profitability and growth.

Real-time and predictive analytics

The growing importance of analytics in banking cannot be underestimated. Machine learning algorithms and data science techniques can significantly improve bank’s analytics strategy since every use case in banking is closely interrelated with analytics. As the availability and variety of information are rapidly increasing, analytics are becoming more sophisticated and accurate.

The potential value of available information is astonishing: the amount of meaningful data indicating actual signals, not just noise, has grown exponentially in the past few years, while the cost and size of data processors have been decreasing. Distinguishing truly relevant data from noise contributes to effective problem solving and smarter strategic decisions. Real-time analytics help to understand the problem that holds back the business, while predictive analytics aid in selecting the right technique to solve it. Significantly better results can be achieved by integrating analytics into the bank workflow to avoid potential problems in advance.

Customer segmentation

Customer segmentation means singling out the groups of customers based on either their behavior (for behavioral segmentation) or specific characteristics (e.g. region, age, income for demographic segmentation). There is a whole bunch of techniques in data scientists’ arsenal such as clustering, decision trees, logistic regression, etc. and, as a result, they help to learn the CLV of every customer segment and discover high-value and low-value segments.

There is no need to prove that such segmentation of clients allows for the effective allocation of marketing resources and the maximization of the point-based approach to each client group as well as selling opportunities. Do not forget that customer segmentation is designed to improve customer service and help in loyalty and retention of customers, which is so necessary for the banking sector.

Recommendation engines

Data science and machine learning tools can create simple algorithms, which analyze and filter user’s activity in order to suggest him the most relevant and accurate items. Such recommendation engines show the items that might interest the user, even before he searched for it himself. To build a recommendation engine, data specialists analyze and process a lot of information, identify customer profiles, and capture data showing their interactions to avoid repeating offers.

Image source: FinTech News

The type of recommendation engines depends on the filtering method of the algorithm. Collaborative filtering methods can be either user-based, or item-based, and work with user behavior to analyze other users’ preferences, then make recommendations to the new user.

The main challenge in collaborative filtering approach is using a huge amount of data that causes computation problems and increased price. Content-based filtering works with more simple algorithms, which recommend similar items to the ones the user engages with referring to a previous activity. These methods can fail in case of complex behaviors or unclear connections. There is also a hybrid type of engines, combining collaborative and content-based filtering.

No method is universal, each of them has some pros and cons, and the right choice depends on your goals and circumstances.

Customer support

Outstanding customer support service is the key to keep a productive long-term relationship with your customers. As a part of customer service, customer support is an important but broad concept in the banking industry. In essence, all banks are service-based businesses, so most of their activities involve elements of service. It includes responding to customers’ questions and complaints in a thorough and timely manner and interacting with customers.

Data science makes this process better automated, more accurate, personal, direct, and productive, and less costly concerning employee time.

Conclusion

To gain competitive advantage, banks must acknowledge the crucial importance of data science, integrate it in their decision-making process, and develop strategies based on the actionable insights from their client’s data. Start with small manageable steps to incorporate Big Data analytics into your operating models, and be ahead of the competition.

This list of use cases can be expanded every day thanks to such a rapidly developing data science field and the ability to apply machine learning models to real data, gaining more and more accurate results. We will be grateful for your comments and your vision of possible options for using data science in banking.