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Big data is the driving force behind more business decisions than ever before. As the amount of data being produced in real time continues to grow, so does the demand for people with the skill sets to help business analyze and manage all this data effectively. What does this mean for you if you’re deciding on a career path or a career move?

Big data equals big opportunities.

According to IBM, the market demand for data scientists will increase 28% by 2020. It is expected that the number of jobs for big data professionals will reach 2,720,000 in just two short years. What can you do now to get ahead of the trend and land a career with a great outlook, and a median starting salary of $120,000 a year? It starts with a great education.

Why you need a big data certification

Big data analytics has moved beyond the status of being just a trend or a buzzword. Businesses everywhere are realizing that big data analytics is crucial for success in industries that are constantly changing and learning to adapt.

Anyone with an invested interest in analytics, from the data engineer to the CEO, can only benefit from expanding their knowledge in this more important than ever field. Research these top programs and discover what a big data certification can do for you and your business.

Top universities around the world have responded to the rising need for talented and skilled data scientists by developing programs that meet the demand. Here are 10 universities that are at the top of their game with big data certifications.

Duke – Integrated Program In Big Data And Data Science

Duke University’s Big Data and Science Program, offered through the Office of Continuing Studies and Summer Session, is perfect for professionals interested in entering or boosting, a career in the field of big data analytics. This program was developed for those interested in developing and broadening their areas of expertise in the data science field.

The success of this program can be tied to an in-depth focus on the industry recommended learning path that includes Data Science with R, Big Data Hadoop and Spark Developer, Tableau Desktop, Data Science with Python and Machine Learning.

Gaining experience and expertise in these multiple data skills is essential to adapting and growing as a professional in the ever-changing field of big data analytics.

The Duke program is best suited for professionals who are looking to further their careers in

• Analytics
• Software Development
• Business and Data Analytics
• Data Warehousing
• Project Management

Stanford Executive Education – Big Data, Strategic Decisions: Analysis to Action

Stanford Business School offers a continuing executive education program in big data called Big Data, Strategic Decisions: Analysis to Action. According to the Stanford description, this program will allow professionals to “Harness the power of data analytics to improve decisions, gain a competitive edge, and enhance your company’s performance, products, and processes.”

Senior level professionals and major decision makers in every business can use this course to become more competitive in their fields and drive their business forward with an increased knowledge of data analytics.

Stanford’s Big Data program focuses less on concrete learning of technologies, as with Duke’s program, but instead leans toward the how and why of big data. Seminar topics include Why Big Data Matters, Using AI to Understand and Influence People, and Using Data to Make Better Marketing Decisions. Along with this, participants gain practical experience by working as part of a team with a Stanford Data Scientist in data simulation projects.

When it’s all said and done, what do you walk away with after completing the Big Data, Strategic Decisions: Analysis to Action program?

• You will have the ability to apply what you have learned to form big data solutions that offer real value to your business.
• You will have the ability to see patterns and trends and to recognize connections through analytical expertise.
• You will have a solid understanding of the future of big data, AI and machine learning, and what it means for your company, and for you as a professional.
• You will gain invaluable experience and a fresh perspective from working one on one with data scientists and peers from diverse industries.

Northwestern Kellogg University – Leading With Big Data And Analytics: From Insight To Action

Northwestern Kellogg University takes the leadership approach to big data learning in their program Leading with Big Data and Analytics: From Insight to Action. The focus of this executive-level program is to help leaders make better decisions by providing them with the practical leadership tools of big data analytics. The strong theme here is real-world applications.

This program contains courses that help company and industry leaders understand their unique roles in big data and analytics. Some of the courses that participants will develop these skills through include:

• Why Analytics Is Every Leader’s Problem
• How a Working Knowledge of Data Science Drives Business Value: A Primer on Big Data and Analytics Concepts
• How to Build Organizational Muscle in Analytics

And possibly, the most important course of all:

• Applying Analytics to Your Business

As a bonus, this program also offers optional tutoring sessions in key big data technologies, such as Tableau.

Rochester Institute Of Technology – Advanced Certificate In Big Data Analytics

With the Advanced Certificate in Big Data Analytics program at RIT, we move away from the executive leadership role in big data and focus in on the individual professional who is looking to forward their career by building their expertise in the field of analytics.

This advanced certificate program is geared towards professional with a BS in some fields where an understanding of data analytics is essential. This is the place for people with careers in computing, engineering, retail, manufacturing, and finance, that are looking to become formally qualified and increase their professional value with an important and relevant skill. All without the need to commit years to a graduate level degree program.

Program highlights include:

• Introduction to Big Data
• Big Data Analytics

Plus, a choice of program electives in

• Secure Data Management
• Data Warehousing
• Data Driven Knowledge Discovery
• Foundations of Parallel Computing

Columbia Business School – Business Analytics: Identifying And Capturing Value Through Data

The 3-day program Business Analytics: Identifying and Capturing Value Through Data is laser-focused on helping business professionals of all levels understand the many ways in which big data analytics can provide valuable insights for critical thought and decision making.

Columbia Business School is ranked No. 2 by analytically focused journals due to their innovative thought leadership and research. It is obvious that Columbia showcased these qualities in developing this business analytics program.

Course structure includes:

• Predictive Analytics, which focuses on analyzing prediction quality and quantifying the economic tradeoffs involved in decision making.
• Prescriptive Analytics, where participants learn to leverage data and optimize the decision-making process within the binding constraints of business management.
• Analytic Lifecycle, the culmination of the program where participants put it all together by learning how to diagnose inefficiencies, create solutions through an analytical approach and how to build and implement a decision support system.

University Of Washington – Big Data Technologies: Design And Build Big Data Systems

The Big Data Technologies program from the University of Washington was developed for professionals with a working knowledge of programming and working with big data sets.

With the list of admission requirements mentioning skills such as knowledge of SQL, experience with programming languages such as Java, Python or C3, experience with Google Compute Engine and knowledge of basic system management and configuration skills, this program is meant for those who are in the deep waters of analytics, more so than business executives.

The aim of this program is to enhance knowledge of the concepts involved in big data engineering, streaming applications, and distributed data storage. With a deepening knowledge of data engineering tools, participants will discover how new data technologies can be optimized to solve the challenges and frustrations of the data engineering field.

The program is split up into three sections that include:

• An Introduction to Data Engineering
• Building the Data Pipeline
• Engineering Technologies in Big Data

The cumulation of which is a certificate that will ignite your career in the field of big data technologies.

Harvard University

The Principles of Big Data Processing course from Harvard is for those who have some experience in the field of Big Data. After taking this course, students will be able to understand the basic rules of creating distributed and easily accessible processing systems.

Using these systems will help in analyzing large volumes of historical and real-time data. The course focuses on data processing stages that are common to real-world systems. People taking this course are required to be able to work comfortably with at least one programming language like Java or Python and must also be familiar with cloud environments like AWS or Docker.

The course includes:

• Scaling Concepts and Parallel Processing
• Distributed Data Persistence with HDFS
• Batch Tier: Processing and Batch Views
• DevOps in the Distributed World

The course is available both online and offline, so students from all over the world can access it.

University of California, Irvine (UCI)

The Big Data course by UCI is for professionals and individuals who want to learn how to manage and analyze large volumes of data. This Big Data certificate gives individuals the skills required to gather and sort huge volumes of data effectively as well as make data-driven analysis and use algorithms to predict and extract competitive intelligence for their organizations.

Program Benefits

• Convert large, structured and unstructured data sets into useful information
• Extract valuable insight to solve business issues
• Use common analytic tools such as Hadoop, Knime, Statistica, R, Python and Crystal Ball
• Create skills to gather Big Data and make data-driven discoveries
• Frame Big Data architectural strategies for your organization
• Enhance business efficiency and improve customer satisfaction
• Familiarize yourself with data architecture software like Hadoop and Aster as well as related tools (Java, SQL, and MYSQL)

University of Toronto

With this Management of Enterprise Data Analytics course, students will be equipped with the skills necessary to become qualified managers in the field of predictive analysis. This course looks at the management perspective and weaves managerial practices into statistical and technological domains.

After taking this course, you will be able to use tools and techniques used by leading experts all over the world. The course comprises of case studies, demonstrations, projects, guest lectures from highly experienced instructors and will help you apply what you learn to real-life situations.

What You’ll Learn

• Take a Big Data/predictive analytics initiative in your organization
• Understand the differences between data warehousing, business intelligence, and Big Data
• Create security, privacy and risk-management standards for your organization
• Administer up-to-date procedures, standards, and techniques to an ongoing project

After taking this course, participants will be awarded a certificate that is offered in collaboration with the Faculty of Engineering and Applied Science.

York University

The Certificate in Big Data Analytics course trains you to use and outline key opportunities to help your organization meet its strategic objectives. The course looks to train students on the applications of Big Data principles.

The course is great for specialists in areas such as marketing, insurance, finance, human resources, and policy deal with big data every day.

The course comprises of two certificates:

• Certificate in Big Data Analytics
• Certificate in Advanced Data Science and Predictive Analytics

Students can take either one or both certificates. Each certificate is eight weeks long and students can earn their first certificate in six months. The university plans to release a full-time version of the course to allow students to finish in the certificates in 4 months.

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In what has been termed the biggest data theft to have ever occurred, nearly 70 million people from the United States alone have had user data leaked from their Facebook profiles. In total, as much as 87 million accounts around the world have been affected, if not more. And with the growing number of people getting entangled, both the victims and the guilty, this maelstrom doesn’t seem like it will blow over anytime soon. In fact, it has already reached global proportions.

We already know that Cambridge Analytica, the big data firm at the eye of the storm, was using user data harvested from a third-party quiz app, thisisyourdigitallife, on Facebook to build psychographic profiles of millions of American voters. In detailed reports by American newspaper The New York Times and UK’s The Observer, the full extent of the unethical data mining carried out by the company was exposed.

Soon on the heels of the initial reports, a series of under-cover videos by UK’s Channel 4, revealed that the political clientele of this firm was far more out-reaching than one would have imagined. Through the revelations of former employees of the company itself, including the CEO Alexander Nix, the data analytics company had also gathered data from British, Indian and Kenyan voters to name a few.

Through every coverage of these events, Facebook has painted an image of betrayal of trust by Cambridge Analytica. And while the methods with which the data mining firm collected user information was indeed illegal and underhanded, a large portion of the blame falls on Facebook as well.

The firm was only able to collect data from user accounts on due to loopholes present in Facebook’s data privacy policies. These loopholes made it easy for third-party app developers to gain access to the data of not only consented accounts, but their friends’ accounts as well.

The consequences Facebook will face

In the wake of this enormous data privacy leak, the founder and CEO of the social media giant, Mark Zuckerberg, has a lot of questions to answer. He has agreed to testify before the American Congress. And he would soon be facing the music in Capitol Hill on April 10^th and 11^th.

In a statement on the upcoming hearing, top Republican and Democrat representatives said that this hearing will be an important opportunity to shed light on critical consumer data privacy issues and help all Americans better understand what happens to their personal information online.

Several law makers have made it clear that they do not intend to let Facebook off the hook so easily. In the light of the massive breach of data privacy, it is not surprising that the American government, as well as governments of other affected countries, are extremely concerned over the Facebook’s policies for protecting user information. The social media network would most likely to face serious legal repercussions.

Sen. John Kennedy, said on Sunday that he believes the issue is “too big” for Facebook to fix on their own. On channel CBS’s ‘Face the Nation’, he said along with several other law makers have many questions for Zuckerberg. He would be asked to clarify what Facebook’s role in the trend of spreading misinformation. And the company’s policies in protecting user information from third party apps who harvest this data.

The governments of several of the other countries who were affected by the data breach have sent notices to Facebook as well, asking Zuckerberg to answer their own questions. Both the governments of UK as well as India, have asked the CEO to testify in their own countries. But so far Facebook has refused and have put out written announcements instead of testifying in person.

One thing is clear. That Facebook would come out of this ordeal as the strong independent company it was when it first started fourteen years ago. It would not be surprising if events played out the same way as they did twenty-one years ago, when Microsoft was hit with serious regulations for its aggressive monopolizing of the market.

What is Facebook doing about it?

Senator Kennedy’s fears of that this may be too big for Facebook to handle could be well founded. As it turns out the scandal with Cambridge Analytica was not a one-time incident. Another data analytics firm has been suspended from Facebook’s platform under suspicion of misleading users and unethically collecting their data. CubeYou is another data firm, which used similar methods to Cambridge Analytica to collect user information, in the form of quizzes. They informed users that the quizzes were part of “non-profit academic research”. The data, which was collected by researchers of the Psychometric Lab at Cambridge University, was being sold to marketers. Channel CNBC discovered the dupe and sent notifications to Facebook. The social media giant then suspended the firm for further investigation. They have stated that if CubeYou refuses or fails the audit, their apps would be banned from Facebook.

Earlier, in a public Facebook post, Mark Zuckerberg claimed responsibility for the massive breach of privacy. And promised to work on improving Facebook’s policies and fixing the loopholes that exist.

Starting from today, Facebook also rolled out a notification process which would alert users if their accounts had been breached, and information stolen. A link would appear at the top of an affected individuals News Feed, which would share details on the information which was stolen, and a list of apps and websites installed through Facebook. Options to delete individual apps will also be available.

#Deletefacebook Movement

Despite Facebook’s attempts at reassuring that they have learnt from their mistakes, the recent events delivered a serious hit to the company’s user base.

Many of Facebook’s users have expressed their displeasure to social network’s careless attitude to data privacy. The hashtag #Deletefacebook began trending on Twitter, with many notable personalities and organizations deleting their Facebook accounts and pages.

Among the list people who have turned their backs on Facebook is Elon Musk. In a response to a question tweeted at him, he removed the pages of both his companies, Space X and Tesla. Mozilla, the maker of the internet browser Firefox as well, announced that they have stopped any further advertising on the social networking website.

Millions of Facebook users have deleted or deactivated their accounts in anger as well.

Data privacy is the need of the hour

While deleting your Facebook account would help ensure that your own data would not fall prey of unethical data mining, it does not really solve the issue of data privacy. The case of Facebook and Cambridge Analytica is not the first time that user information was stolen and used for malpractices. And sadly, it would not be the last time.

But every cloud has its silver lining. The idea of data privacy and frightening lack of it is a discussion which had been stewing on the back burner for way too long. Due to Facebook’s massive slip-up, it has now been brought to the forefront of a global discussion. Hopefully organizations and governments who are capable and responsible for protecting internet rights of their people would be able to come up with better solutions. Already the EU has introduced the “General Data Protection Regulation” (GDPR) bill, which due to come into effect in May.

The world has already entered an era where one cannot live disconnected from everyone else. Unless one wants to live completely off the grind and in isolation. However, for every other individual who wants to stay in touch with the world, then the easiest way to do it is through the internet.

Hopefully, better data privacy laws would soon be in place, not only in Europe, but in the rest of the globe as well. And the knife wound which Facebook plunged into our backs, would soon be healed.

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The value of data-driven Customer Value Management or CVM cannot be underrated. Data and other algorithms/analytics that shape data are an imperative part of customer value management in a telecom company. With enhanced customer expectations, it is up to the ability of telecom companies to provide customers with a seamless experience and to also ensure that they help boost revenue in the process.

To understand this concept in a more functional manner, I recently interviewed the chief of CVM at Mahindra Comviva, Amit Sanyal. With so much on hand to discuss, I got to the crux of the matter straightaway and asked Amit about the pillars he considered to be important for a customer value management program being driven by analytics.

The prodigy responded to my questions by commenting that all methods of CVM being driven by the force of analytics should be dedicated towards these three pillars.

1. Analytics themselves have an important part to play, which is why they form the first pillar in this regard. Understanding consumer behavior is not child’s play, so it is indeed profitable for a telecom company if the analytics are spot on in their methodology.
2. The second pillar pertaining to efficiency in this regard points towards context in analytics. Analytics should attend to a derived need of consumers, and should be able to determine the channel of communication understanding the customer’s ‘sense’ is key here.
3. The third pillar is that of real time communication. While secondary data collection is of immense importance itself, real-time primary communication cannot be understated based on the role it plays in understanding customers. Real-time means ‘as good as it gets in time’ – there is no merit in expecting customers to remember and act on propositions if not presented right when it is relevant, and more importantly ‘useful’.

Amit also outlined that one of the key challenges facing telecom companies globally is a drop in revenue. The drop in revenue is because of numerous reasons that are making growth a very difficult option to undertake for all protagonists involved in the market. While all telecom operators are looking out for newer options in the form of fresh customers, it is imperative to note here that fresh customers are rarely found. Most geographies have network connections than the people living in it or very close to that, so there is a real shortfall of new customers coming in for new connections. Other than the shortfall in garnering fresh customers, Amit also highlighted how the revenues from current customers were decreasing. These revenues have been decreasing steadily for a while now, due to the high amount of competition between the firms present. Most over the top or data content services are free. Margins have significantly dropped, since operators cannot risk selling at expensive rates considering how there are other operators selling at reduced rates.

The solution to this problem lies in reaching out to customers in a seamless manner. Since revenues in the market can only be increased through acquiring fresh customers or earning more revenue through current customers, reaching out to the customers and understanding their data is an inevitable outcome that needs to be followed.

Achieving Revenue

Since it has been mentioned above that there are limited fresh customers in the market, growth can only be achieved by bringing in customers from other operators. Simply put, you need to acquire customers from somewhere else to show your growth.

Besides bringing in new customers, you can also increase revenues from existing customers by understanding the economic concepts of elasticity and inelasticity. Operators need to know just what customers will be willing to spend their dollars on. Match the products and services they want with a price tag that gets customers to buy them.

Moreover, you can also increase the quality of your service. Subscribers tend to stay longer with an operator who offers quality. Not only will they stay longer, but they will also bring in new customers from other brands by telling them about the quality of your service.

To do all of this, you need to know just what the consumers are looking for. This is where the concept of machine learning and real time analytics come in. You should comprehend how your typical consumer behaves, and should also have a basic understanding of their preferences. You can use big data in the network systems, and implement methods such as predictive analytics and targeted communications to get the data that helps you understand them. By understanding their behavior and their preferences through real time data visualization, you can know just what will be perfect for your customers. Implementation of this method could open doors to data-driven customer value management and machine learning. Some of the stats pointing in favor of these changes are:

1. Organizations that had real time data visualization enjoyed an increase of 26 per cent in their new identified pipeline accounts.
2. Organizations that had implemented real time data visualization saw an increase of 15 per cent in the cash generated through operational activities.
3. Engaging with customers is not just a beneficial tool for getting to know them but can eventually increase revenues and profitability. It has been found that if you engage with your customers, you will be able to generate 40 per cent more revenue per customer.
4. Your marketing expenditure on personalization will not go to waste as it has been found that the tactic can increase your return on investment or ROI up to 5-6 times.
5. A negative customer experience is nothing less than a cardinal sin. It takes more than 12 positive experiences to negate one negative impression that the customer must have developed through an experience.
6. 70 per cent of all purchases are based on how the customer feels that they are being treated by the organization.
7. 67 per cent of all customers leaving your organization could be stopped if you resolve their issue during the first engagement.
8. Increasing customer retention by 2 percent is as beneficial as reducing costs by 10 per cent.

Data-driven marketing is key for enhancing the customer experience. Data driven marketing can help connect data points and link them together to create a more actionable context. Cases that highlight this are:

• Customers don’t want to be told what to do. If a customer with a 4G phone is using 3G, they wouldn’t like the customer representative to tell them that they should switch to 4G. However, if the customer representative has sufficient data to see that they are using 3G since the last 3-4 months and also consumed most of the ‘data quota’ each month then he/she can recommend to them a 3G package with a higher band to increase satisfaction.
• Using favorite and maximum recharge denominations data to get an indication of average revenue per user (ARPU). Telecom companies should study consumer data and know how much more they can spend and then offer them a feasible plan. Someone with an ARPU of $5 should not be given a $2,000 plan, instead they should be shown a $10 plan with hopes that it is within their extent of purchase.
• All offers given to customers should be contextual. If a customer is spending time on international calls, then the offer given to them should be based on that and not driven by time.

The implementation of data-driven marketing calls for a mindset change in telecom operators. Operators need to understand what customers prefer, and then they should reach out to them on a personal level through data. “Everybody loves to talk about data science, it’s a cool thing – but only a few really move towards implementing it” said Amit before concluding the interview.

Originally published here.

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Online security has always been a major concern for individuals on the internet. Whether the general population knows it or not, there are issues here which destroy the goodwill of those who decided to give major online industries the benefit of the doubt. Hot off of the heels of Facebook’s recent drama concerning stored user text message data, this problem is finally seeing some changes within the EU. These changes adopt components that have long been key to the world of cryptocurrencies, specifically those surrounding the technology of blockchain. So, where do these changes come from, who will they effect, and what advantages can the end user hope to see?

Those Pushing for Change

As is often the case with developments in modern online systems and the way in which they integrate with the world, regulations are playing catch-up. The rapid development of technology means that laws and regulations can go out of date quickly. Unfortunately, this means that loopholes and similar security flaws can appear in unpatched systems, potentially exposing both customers and businesses to data theft.

Understand this was the key as to why the EU has decided to enact new regulations on big data. One of these methods comes in the form of the General Data Protection Regulation, or GDPR, which takes effect on May 28, 2018. In general terms, this law aims to require firms to first gain consent on the exact type of data which they will receive from users, and for them to clearly state the ultimate purpose for which this data is being collected.

Those Affected

Since this is regulation passing in the EU, many mistakenly believe that it only applies to those websites stationed in the EU, but this is incorrect. In actuality, these will apply to any organization placed outside of the EU which aims to collect information of people within the EU. While the current understanding is that these changes will cost businesses significant costs in terms of both money and manpower, the idea is finally to take a step on protecting the right to privacy of people over the internet and encourage other nations to follow suit along the way. Exactly how well adopted these series of consumer-level protections will manage to fight against corporate powers and lobbyists remains to be seen.

Why Blockchain?

Blockchain offers several significant advantages, which make it perfect as a means of security. The first of these components is that of decentralization. By removing a single point of ownership as a feature of security, the system instead becomes reliant on the blockchain network. The decentralized nature of this network means that threats and corruption face significantly more hurdles if they wish to take place. As each part of these systems are interconnected, and can tell if the others are being manipulated, it means that data theft or manipulation would require the simultaneous hacking of multiple systems, over multiple locations, in ways which perfectly trip each of the multiple security measures. Not a simple task, even for the most dedicated and professional hackers out there.

With these advantages, it might now seem obvious why blockchain based security is a positive choice but, for further examples, we can look at how other organizations have adopted the use of this technology. The most obvious and widespread example can be seen from the cryptocurrency market, Bitcoin specifically. As this type of transaction comes with such high levels of inherent security, even comparing favorably to fiat currencies in many areas, it has been adopted by many online stores. These include marketplaces like OpenBazaar, hotel bookings with Expedia, and gadgets through Newegg. Furthermore, the presence of Bitcoin and blockchain in the iGaming world has allowed casino websites to offer provably fair gameplay.

Long-term Advantages of Using Blockchain

By putting security power back in the hands of the consumer, the idea is to create a system which is both safer, and which is better able to function without the interference of or reliance on the organizations which have long taken advantage. This means a future with fewer surprises of actions which have hurt the user bases of many websites and services. To use the Facebook example again, with this regulation in place, the data and private information theft would not have been possible in any way. It would have had to agree to the regulations in the first place, setting themselves up for litigation in the case of dishonesty. Following this, they would still have to manipulate the blockchain, which would have been both detected and noticed before any actual damage could occur.

Regulations such as the one we are seeing with the GDPR have been a long time coming. To many, they are an inevitability which comes from the overreach of those with little opposition. By enacting these regulations, and relying on security measures such as blockchain to protect the end users, there are now effective safety measures in place, which means we no longer must rely on trust. With these changes and regulations, now and in the future, our data and information are safer, we are safer, and the online world is all the better off for it.

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R is considered as the de facto programming language for statistical analysis right? But In this post, I will show you how to easily implement statistical concepts using Python.

I will implement discrete and continuous probability distributions using Python. I won’t get into the mathematical details of these distributions, but I will mention some of the best resources to learn the math concepts involved in these methods.

Before we jump into these probability distributions, I want to give a glimpse of what a random variable is. A random variable quantifies the outcomes of a number.

For example, a random variable for a coin flip can be represented as

X = { 1 heads

2 if tails}

A random variable is a variable that takes on a set of possible values (discrete or continuous) and is subject to randomness. Each possible value the random variable can take on is associated with a probability. The possible values the random variable can take on and the associated probabilities is known as probability distribution.

I encourage you to go through scipy.stats module.

There are two types of probability distributions, discrete and continuous probability distributions.

Discrete probability distributions are also called as probability mass functions. Some examples of discrete probability distributions are Bernoulli distribution, Binomial distribution, Poisson distribution and Geometric distribution.

Continuous probability distributions also known as probability density functions, they are functions that take on continuous values (e.g. values on the real line). Examples include the normal distribution, the exponential distribution and the beta distribution.

To understand more about discrete and continuous random variables, watch Khan academies probability distribution videos.

Binomial Distribution

A random variable X that has a binomial distribution represents the number of successes in a sequence of n independent yes/no trials, each of which yields success with probability p.

E(X) = np, Var(X) = np(1−p)

If you want to know how each function works, you can use help file command in your I python notebook. E(X) is the expected value or mean of the distribution.

Type stats.binom? to know about binom function.

Example of binomial distribution: What is the probability of getting 2 heads out of 10 flips of a fair coin?

In this experiment the probability of getting a head is 0.3, this means that on an average you can expect 3 coin flips to be heads. I define all the possible values the coin flip can take, k = np.arange(0,11), you can observe zero head, one head all the way upto ten heads. I am using stats.binom.pmf to calculate the probability mass function for each observation. It returns a list of 11 elements, these elements represent the probability associated with each observation.

You can simulate a binomial random variable using .rvs. The parameter size specifies how many simulations you want to do. I ask Python to return 10000 binomial random variables with parameters n and p. I am printing the mean and standard deviation of these 10000 random variables. Then I am going to plot the histogram of all the random variables that I simulated.

Poisson Distribution

A random variable X that has a Poisson distribution represents the number of events occurring in a fixed time interval with a rate parameters λ. λ tells you the rate at which the number of events occur. The average and variance is λ.

E(X) = λ, Var(X) = λ

You can notice that the number of accidents peaks around the mean. On an average you can expect lambda number of events. Try different values of lambda and n, then see how shape of the distribution changes.

Now I am going to simulate 1000 random variables from a Poisson distribution.

Normal Distribution

The normal distribution is a continuous distribution or a function that can take on values anywhere on the real line. The normal distribution is parameterized by two parameters: the mean of the distribution μ and the variance σ2.

Normal distribution can take values from minus infinity to plus infinity. You can notice that I am using stats.norm.pdf as normal distribution is a probability density function.

Beta Distribution

The beta distribution is a continuous distribution which can take values between 0 and 1. This distribution is parameterized by two shape parameters α and β.

The shape of beta distribution depends on the values of alpha and beta values. Beta distribution is predominantly used in Bayesian analysis.

Exponential Distribution

The exponential distribution represents a process in which events occur continuously and independently at a constant average rate.

I set the lambda parameter as 0.5 and x in the range of

Then I simulate 1000 random variables from an exponential distribution. scale is the inverse of lambda parameter. ddof in np.std is equal to dividing the standard deviation by n-1.

Conclusion

Distributions are like blue print for building a house, and random variable is summary of what happen in an experiment. I would recommend you to watch the lecture from harvard data science course, professor Joe Blitzstein gives a summary of everything you need to know about statistical models and distributions.

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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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Source: http://bigdata-madesimple.com/top-10-trends-in-payments-2018-infographic/