R is a powerful programming language and software environment for statistical computing and graphics. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is now maintained by the R Development Core Team. R provides a wide variety of statistical and graphical techniques, and is widely used by statisticians and data scientists for data analysis, data visualization, and predictive modeling.

Top Use Cases of R

• Data Analysis: R is commonly used for data analysis tasks such as data cleaning, data manipulation, and data visualization. Its extensive library of statistical functions and packages make it a popular choice for analyzing and interpreting data.

• Machine Learning: R has a rich ecosystem of packages for machine learning, including popular libraries like caret, random Forest, and glmnet. These packages provide implementations of various machine learning algorithms, making it easy to build predictive models and perform tasks like classification, regression, and clustering.

• Statistical Modeling: R is widely used for statistical modeling, including linear regression, logistic regression, time series analysis, and more. Its built-in functions and packages make it easy to fit models to data and perform statistical inference.

• Data Visualization: R provides powerful tools for creating visualizations, including bar plots, scatter plots, line plots, and more. Its ggplot2 package is particularly popular for creating publication-quality graphics.

Features of R

• Open Source: R is an open-source language, which means that it is freely available and can be modified and distributed by anyone. This has led to a large and active community of developers who contribute to the language and create new packages.
• Extensive Library: R has a vast library of packages and functions for various purposes, such as data manipulation, statistical analysis, machine learning, and more. These packages can be easily installed and loaded into R, providing additional functionality and making it easy to perform complex tasks.
• Reproducibility: R promotes reproducible research by providing tools for documenting and sharing code and results. This allows others to easily reproduce and verify your analysis, increasing transparency and trust in the research process.

Workflow of R

The workflow of R typically involves several steps:

1. Data Import: The first step is to import your data into R. This can be done using functions like read.csv() or read.table() for reading data from files, or using packages like dplyr or tidyr for importing data from databases or other sources.
2. Data Cleaning and Manipulation: Once the data is imported, you may need to clean and manipulate it to prepare it for analysis. R provides a wide range of functions and packages for performing tasks like removing missing values, transforming variables, and creating new variables.
3. Data Analysis: After the data is cleaned and prepared, you can perform various statistical analyses using R’s built-in functions or packages. This may involve fitting models, performing hypothesis tests, or calculating summary statistics.
4. Data Visualization: R provides powerful tools for creating visualizations to explore and communicate your data. This can be done using functions like plot() or with packages like ggplot2, which allows for more advanced and customizable graphics.
5. Reporting and Sharing: Finally, you can generate reports or presentations of your analysis using R Markdown or other tools. This allows you to combine code, text, and visualizations in a single document, making it easy to share your work with others.

How R Works & Architecture

R is an interpreted language, which means that code is executed line by line without the need for compilation. When you run an R script or command, the R interpreter reads the code, evaluates it, and produces the desired output.

The architecture of R consists of several components:

• R Console: This is the interface where you interact with R. You can type commands directly into the console and see the results immediately.
• R Scripts: R scripts are files that contain a series of R commands. They can be saved and executed as a batch, making it easy to automate repetitive tasks or perform complex analyses.
• R Packages: R packages are collections of functions, data, and documentation that extend the functionality of R. They can be installed and loaded into R using the install.packages() and library() functions, respectively.
• R Environment: The R environment is where objects like data, functions, and variables are stored during an R session. You can create, modify, and manipulate these objects to perform your analysis.

How to Install and Configure R

To install R, you can visit the official R website (https://www.r-project.org/) and download the appropriate version for your operating system. Once downloaded, you can follow the installation instructions provided on the website.

After installing R, you may also want to install an integrated development environment (IDE) for a better coding experience. Some popular IDEs for R include RStudio, Visual Studio Code, and Jupyter Notebook.

Once you have R and an IDE installed, you can start writing and executing R code.

Step by Step Tutorials for R – Hello World Program

To get started with R, you can follow these step-by-step tutorials to write a simple “Hello World” program:

1. Open your preferred IDE and create a new R script.
2. In the script, type the following code:

# Print "Hello, World!" to the console
print("Hello, World!")

3. Save the script with a .R file extension, such as hello_world.R.
4. Run the script by clicking the “Run” or “Execute” button in your IDE. The output “Hello, World!” should be displayed in the console.

Congratulations! You have successfully written and executed your first R program.

Remember, learning R is a journey, and there is always more to explore and discover. Have fun exploring the world of statistical computing and data analysis with R!

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Analytics Insight Presents the list of Top Free Online Courses in Statistics and Data Analysis

Would you like to understand data science statistics without undergoing a time-consuming and pricey class? There’s great news! Using solely free online resources, you may understand basic topics such as probability, Bayesian thinking, and statistical deep learning.

This article will show you the statistical thinking skills you’ll need for data science along with the top free online courses for Statistics and Data Analysis. It will give you a tremendous leg up on other budding data scientists who are attempting to get by without it. After all, after you’ve understood how to programme, it can be appealing to jump right into using machine learning programmes. It’s fine if you want to start with real-world projects at first. However, you should never, ever disregard statistics and probability concepts. It’s necessary if you want to advance as a data scientist.

Statistics Needed for Data Science

Statistics is a vast field with several applications in a variety of fields. The science of the collection, analysis, interpretation, presentation and organising of data is statistics, according to Encyclopaedia. As a result, it should come as no shock that data scientists require statistical knowledge.

Data analysis, for particular, necessitates at the very least descriptive statistics and probability theory. These ideas will assist you in making better company decisions based on data. Probability distributions, statistical significance, hypothesis testing, and regression are all important issues.

Artificial learning also necessitates knowledge of Bayesian thinking. The act of upgrading beliefs when new evidence is gathered is known as Bayesian thinking, and it’s at the heart of many machine learning frameworks. Conditional probability, priors and posteriors, and maximum probability are all important topics. Wouldn’t fret if those terms seem like jargon to you. When you get your hands grimy and actually learn, everything will sound familiar.

How to Learn Statistics for Data Science?

You’ve surely observed that “the self-starter route to learning X” frequently includes skipping classroom instruction in favour of “doing stuff.”

It’s no different when it comes to mastering statistics for data science.

In fact, we’ll be tackling important statistical ideas by programming them with coding! This will be a lot of fun, we promise. If you don’t have a formal math background, you’ll find that this method is far more natural than having a hard time figuring out difficult equations. It works by stimulating through each calculation’s logical phases. If you have a strong math background, this method will assist you in putting theory into practice while also providing some enjoyable programming difficulties.

You’ll be prepared to undertake harder machine learning issues and popular real-world data science applications after finishing these three levels. The three steps to studying the statistics and probability needed for data science are as follows:

Step 1: Core Statistics Concepts

It’s a good idea to start learning statistics for data science by examining how it will be applied.

Now let us look at some real-world studies or implementations that you might encounter as a data scientist:

1. Experimental design: Your firm is launching a new line of products, but it will only be available in brick-and-mortar locations. You’ll need to create an A/B test that accounts for geographic variances. You’ll also have to figure out just how many outlets you’ll need to test in order to get statistically relevant findings.

2. Regression modelling: Your enterprise needs to be able to forecast consumption for certain product lines in its outlets more accurately. Both understocking and overstocking are costly. You’re thinking of creating a set of regularized regression models.

3. Data transformation: You’re evaluating a number of machine learning model options. Several of them include assumptions about input data probability distributions, and you must be able to spot them so that you can either convert the data correctly or determine when the presumptions may be eased.

Step 2: Bayesian Thinking

The disagreement between Bayesians and frequentists is one of the philosophical arguments in statistics. While mastering statistics for data science, the Bayesian side is more important.

Frequentists, in an essence, solely employ probability to model sampling processes. This means that they only allocate probability to data that they’ve already gathered.

Step 3: Intro to Statistical Machine Learning

After you’ve grasped essential principles and Bayesian thinking, there’s no better way to learn statistics for data science than by experimenting with statistical machine learning models.

The sciences of statistics and machine learning are inextricably intertwined, and “statistical” machine learning is the predominant method of current machine learning.

In this stage, you’ll create a few machine learning models from the ground up. This will assist you in gaining a genuine knowledge of their dynamics.

Top Free Courses

1. Coursera (Duke University): Statistics with R Specialisation

• Time Period: 10 weeks
• Background knowledge: No prior programming expertise is necessary; just simple mathematics skills are required.

2.  Udacity (Stanford University): Intro to Statistics

• Time Period: 8 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

3.  Stanford University: Statistical Learning

• Time Period: 10 weeks
• Background knowledge: A basic understanding of statistics, linear algebra and computing are necessary.

4. Leada: Introduction to R

• Time Period: Self-Paced
• Background knowledge: No prior experience is necessary; an introductory course is required

5. Udacity (San Jose State University): Statistics: The Science of Decisions

• Time Period: Self-Paced; approximately 4 months
• Background knowledge: Basic proportions (fractions, decimals, and percentages), negative values, fundamental algebra (solving equations), and exponential and square roots.

6. Saylor: Introduction to Probability Theory

• Time Period: Self-Paced
• Background knowledge: Topics in single-variable and multivariate calculus, numerical analysis, and differential equations, or equivalents, must be completed.

7. EDX (Columbia University): Statistical Thinking for Data Science and Analytics

• Time Period: 5 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

8. EDX (University of Texas): Statistics Using R

• Time Period: 6 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

9. Caltech: Learning from Data

• Time Period: Self-Paced
• Background knowledge: No prior experience is necessary; an introductory course is required

Conclusion

We hope that we were able to provide you with the best free courses for Statistics and Data Analysis. They are ranked from 1 to 9 with short details which will help you pick your courses according to your convenience. So, hurry up and get yourself a course now!

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Quantum computing has the capability to revolutionize computing by being the solution for previously unsolvable problems.

There’s a reason Google, Microsoft, IBM, and governments all over the world continue to invest heavily in quantum computing: they believe it will transform the world by solving issues that traditional computers can’t solve.

Every industry is slowly being disrupted by quantum computers. They’re revolutionizing the way we do business and the security we have in place to protect data, as well as how we battle illness and develop innovations, as well as how we solve health and climate issues.

According to the report, yet the “quantum jungle” of available devices and protocols remains hard to navigate, and researchers still need to work on identifying the most promising paths to quantum technologies that can be societally useful. However, a collaboration between two teams at the University of Arizona—one led by Zheshen Zhang and the other by Quntao Zhuang—shows that quantum entanglement can provide a quantifiable advantage to data classification, which is important in imaging and navigation.

The team identifies data from a network of entangled sensors using a machine-learning algorithm. They demonstrate that entanglement can improve both the accuracy and the speed of classification by comparing their scheme to one that uses traditional data processing. The research sets the door for a wide range of quantum-enhanced classification methods to be made possible by near-future quantum technologies.

Zhang, Zhuang, and colleagues investigate a path for quantum-enhanced data processing that stems from the marriage of quantum machine learning with the most well-established quantum technologies: quantum sensing and metrology.

“The work of Zhang’s and Zhuang’s teams focuses on a particularly intriguing case—a network of quantum sensors. The use of quantum information processing techniques to combine and analyze the quantum outputs of multiple sensors holds tremendous promise for realizing a quantum advantage. The potential gain stems from a fundamental feature of quantum metrology: The advantage provided by the coherent processing of sensor data scales as the square root of the dimension of the so-called Hilbert space that represents the quantum states sensed by the network. Since the dimension of that Hilbert space scales exponentially with the number of analyzed states, the quantum advantage for a quantum sensor network scales exponentially with the number of sensors.”

Zhang, Zhuang, and their colleagues decided to explore the quantum jungle by taking a route that crosses the line between quantum sensing and quantum machine learning. Whatever they found along the way proves that this path is worth exploring further.

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The new data science title “Hands-On Exploratory Data Analysis with Python,” by Suresh Kumar Mukhiya and Usman Ahmed from Packt Publshing is a welcome addition to the growing list of books directed to help newbie data scientists improve their skills. I’m always on the lookout for texts that can help my students find their way along the challenging path toward becoming a data scientist. I think this book fills a void for Exploratory Data Analysis (EDA) learning resources. But as I’ll discuss, the book goes beyond just EDA, and is maybe mistitled – it’s really an introduction to data science and machine learning using the Python language.

The book includes important EDA topics like Descriptive Statistics (Chapter 5), Grouping Datasets (Chapter 6), Correlation (Chapter 7), Time Series Analysis (Chapter 8), and Hypothesis Testing (first part of Chapter 9). These are all critical pieces of the data science process, and lucid discussions along with clear and simple code examples help the reader get moving. The publisher provides all the Python code from the book so the reader can hit the ground running.

My favorite part of the book is Chapter 4 on Data Transformation (aka data munging, or data wrangling). This is a very important area that often accounts for a majority of a project’s time and cost budget, and the examples provided in this chapter cover the most commonly needed tasks for a typical data science project (e.g. missing data handling, discretization, random sampling, etc.). Interestingly, data transformation isn’t really part of EDA, but I welcome the discussion as it broadens the scope of the book.

Chapter 2 on data visualization is a nice adjunct to the EDA discussions, because these two areas typically go hand-in-hand. Chapter 3 offers up an interesting use-case for demonstrating data access, data transformation, EDA, and data viz. The example centers around reading in all the emails from your Google account and performing a useful data analysis on the data. Nice touch!

Finally, the book also enters the realm of supervised machine learning, starting with the last part of Chapter 9 on regression models. Then Chapter 10 is a short introduction to various machine learning techniques. This chapter, however, is too brief to be a standalone learning resource, but it does kick-start the reader into thinking about this important topic.

The presumed goal of the last chapter, Chapter 11, is to offer a comprehensive data science example using the well-known Wine Quality data set from the UCI Machine Learning Repository. I’ve used this data set in my own class materials many times, and it’s well-suite for this purpose. My only caveat about this chapter is that it’s too simplistic and too short. But it does give a correct feel for the steps in the data science process, culminating in the use of a number of common ML algorithms and their interpretation.

I would say Hands-On Exploratory Data Analysis with Python is a good addition to the library of a newbie data scientist as it contains many of the most common techniques for putting together a solid machine learning solution. I will be adding this title to my data science bibliography given out to my Introduction to Data Science students.

The post Book Review: Hands-On Exploratory Data Analysis with Python appeared first on Artificial Intelligence.

Data scientists come in numerous flavors with various qualities that may suit various kinds of companies relying upon the sorts of issues or projects

Data Scientists have consistently been around – it is only that nobody realized that the work that these individuals are doing is called data science. Data Science as a field has emerged distinctly over the recent few years yet individuals have been working in the data science field as analysts, mathematicians,learning and actuarial scientists, business analytic practitioners, digital analytic consultants, quality analysts and spatial data scientists. Individuals working under these jobs are well furnished with data scientist skills and they are most demanded in the business.

Data science has quickly developed as a challenging, lucrative and highly rewarding career. While developed nations got comfortable with it part of the way through the last decade, data science has received consideration on a worldwide scale after the exponential development of e-commerce in developing economies, particularly India and China. In the previous decade, there has been a significant change in perspective in the way the world shops, books holidays, makes transactions and basically everything else.

Not all data scientists are made equal, particularly now that few “generations” of data scientists have entered and left organizations. Today, data scientists come in numerous flavors with various qualities that may suit various kinds of companies relying upon the sorts of issues or projects they are taking a shot at. Not to state that one sort is better or worse over another kind of data scientist — everything relies upon what a business is looking for.

Management Consultant

This classification traverses the junior business analyst and the ex McKinsey consultant. They share a common enthusiasm for Excel and their capacity to flaunt v-lookups and fancy formulas even to plan their house move. They are additionally the ones who have more passion for the business issue. For them, business comes first, data after.

They needed to learn Python or R by need, not on the grounds that they enjoyed programming. They actually try to abstain from coding as much as possible and their code is by and large as re-usable as a single-use napkin.

They have great instincts for the nuts and bolts of statistics however, they needed to learn concepts like p-worth or t-test the most difficult way possible. They are good at data science projects that bolster decision making, business-oriented processes, one-off projects.

Statisticians

This is data analysis in the conventional sense. The field of statistics has consistently been about number crunching. A solid statistical base qualifies you to extrapolate your enthusiasm for various data scientist areas. Hypothesis testing, confidence intervals, Analysis of Variance (ANOVA), data visualization and quantitative research are some of the important skills possessed by statisticians which can be extrapolated to pick up expertise in explicit data scientist fields.

Statistics knowledge, when clubbed with domain knowledge, (for example, marketing, risk, actuarial science) is the ideal blend to land a statistician’s work profile. They can create statistical models from big data analysis, complete experimental design and apply theories of sampling, clustering and predictive modelling to information to decide future corporate activities.

Data Science for People

The consumers of the yield are leaders like chiefs, product managers, designers, or clinicians. They need to reach inferences from data so as to settle on decisions, for example, which content to license, which sales lead to follow, which medication is less inclined to cause a hypersensitive response, which site page design will prompt greater engagement or more buys, which email will yield higher income, or which explicit aspect of a product user experience is suboptimal and needs attention. These data scientists design, define, and implement metrics, run and interpret experiments, create dashboards, draw causal inferences, and generate recommendations from modeling and measurement.

Academia Data Scientist

They often have a PhD and originated from a research background. They examined hardcore math and statistics and they could talk hours about the philosophical contrasts between the Bayesian and frequentist methods.

They are typically alright at coding, as long as they don’t need to propel themselves a lot into the boundaries of data engineers. A test-driven programming approach may be a stretch for them. However, they are presumably acceptable at lower-level projects, for example, C++, which could come helpful for applications at large scale or deep learning.

What they in general need is business thinking. Building up a product is most likely the ultimate objective for them since they perceive that as the equivalent of publishing a paper in academia.

They are good at complex ML ventures at the front edge of development. They can push boundaries, go through a lot of research papers to pick and implement the best thoughts. A deep-tech organization would presumably require a small bunch of those profiles.

Actuarial Science

Actuarial Science has been around for quite a while. Banks and financial establishments depend a lot on actuarial science to anticipate the economic situations and decide the future salary, income, profits/losses from these mathematical algorithms.

It is possible to be an actuarial scientist without taking up any data science training. However, a data scientist will have an awesome handle over the mathematical and statistical algorithms that are required for actuarial science. A ton of organizations are currently speeding up the cycle by employing CFAs to accomplish the work of an actuarial researcher.

This is a specific position which requires data science experts to apply mathematical and statistical models to BFSI (Banking, Financial Services and Insurance) and other related professions. One must have a globally defined range of abilities and exhibit it by passing a progression of expert assessments before going after this position. Preliminary necessity is to know various interrelated mathematical subjects, for example, probability, statistics, finance, economics, financial engineering and computer programming.

Data science for Machines

Here the customers of the yield are computers which devour information through training data, models, and algorithms. Instances of the work these data scientists are: recommendation systems which suggest what shirt a client may like or what medication a doctor ought to consider prescribing depending on a designed optimization function. For example, enhancing for customer clicks or for minimizing readmission rates to the medical clinic. Contingent upon the engineering foundation of these data scientists, these work items are either conveyed legitimately to the production system, or if they are prototypes they are given off to software engineers to help implement, optimize and scale them.

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You don’t need to work in the marketing department of Facebook or Google to understand the importance of large-scale data analytics when it comes to driving the modern economy. As the primary force behind everything from targeted advertising campaigns to self-driving cars, data analysis stands at the heart of today’s most important and exciting technologies and innovations.

The Deep Learning & Data Analysis Certification Bundle will help you take your analytical skills to the next level so you can land the best and most lucrative positions in your field, and it’s available today for over 95% off at just $39.99.

With eight courses and 30 hours of instruction led by the renowned data scientist Minerva Singh, this bundle will get you up to speed with the latest platforms and methodologies in the interconnected worlds of data analysis, visualization, statistics, deep learning, and more.

Through easy-to-follow lessons that utilize real-world examples, the training courses will walk you through the fundamentals and more advanced elements of YouTube analytics and Google Ads, R programming in the context of machine learning, algorithms that can help you break down data frameworks, statistical models that will allow you to predict future trends, and more.

This training bundle also takes a deep dive into the emerging worlds of artificial neural networks and deep learning platforms, which are being used by major tech companies in order to develop some of the world’s most powerful computing frameworks and technologies.

Land your ideal job in an increasingly data-driven world with help from the Deep Learning & Data Analysis Certification Bundle while it’s on sale for just $39.99—over 95% off its usual price.

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Why has data analytics become so critical in the current times, particularly for the payments industry?

Data analytics help tapping the power of data by using tools and technologies to find patterns that yield insights. It’s these insights that businesses and governments find truly actionable, especially in uncharted times when there is no rear view or past trends. Its problem-solving prowess becomes an essential navigational tool.

The payments industry generates lots of data that is helpful and important for governments and businesses to make informed decisions for fraud prevention, risk exposure assessment, improved customer service, better customer targeting and top channel performance. In the current data-driven era, analytics can support businesses optimise, streamline and grow, as well as deliver value to consumers during and post-Covid.

How is Mastercard all set to leverage big data analytics for business in a post-Covid world?

At Mastercard, our analytics platforms enable organisations globally to make faster and better business decisions based on real-time, anonymised and aggregated transaction data and proprietary analysis.

We generate insights through a comprehensive array of software platforms and services that provide solutions to the core challenges faced by the businesses today. We enable customers to innovate strategically and distil insights from this data through software platforms and industry expertise, driving strong consumer connections.

Today, we are working closely with various banks, governments and businesses by making insight-driven tools available to them to give a timely snapshot of economic performance and help them make informed decisions critical to the long-term success of companies and communities around the world.

To cite an example, when a quick-service restaurant chain in Asia Pacific was at a financial loss, data based insights allowed the restaurant chain to focus resources on those outlets that had a better chance of rebounding with aligned timings and menu options of work-from-home habits and tailored promotions for larger transaction volumes.

In another instance, we leveraged our data driven proprietary platforms to help an international airline that saw bookings drop 60 percent and continued to fall precipitously toward an overall drop of 90 percent across the entire travel and hospitality sector in the airline’s country of origin. With relevant insights on price reduction, trip duration, the immediacy of departure and fare type, the airline saw a potential 30 percent increase in international bookings resulting in a 25 percent increase in revenue on economy routes over control routes. With a better understanding of how fare conditions influence uptake, the airline is now manoeuvring further as more travel routes open.

What are your key implementations and how is it contributing in providing seamless services to your customers?

As I mentioned above, our analytics platforms enable organisations globally to make faster and better business decisions based on real-time, anonymised and aggregated transaction data and proprietary analysis by:
• Providing customers across industries and geographies with a tailored portfolio of solutions to address pain points across their businesses
• Harnessing the power of anonymized and aggregated transaction data, analytics and expertise to create global, actionable insights, enable more intelligent decisions and drive predictive capabilities
• Prioritising customer-centricity and the user experience by delivering convenience: speed, ease of use and personalisation

To cite an example; our application Intelligent Targeting helps boost efficiency, effectiveness and ease of acquisition by leveraging insights and expertise to design, execute and optimise acquisition campaigns for high-value customers and Business Locator provides the most accurate, up-to-date view of Mastercard-accepting merchants open for business on any given day.

Similarly, Acquirer Intelligence Center enables pre-defined analytics on portfolio performance across volume, fraud, and authorisation, compared to custom benchmarks for a full view of business performance and actionable insights.

In the last few months, we have been committed to leverage this expertise and help retailers, restaurants, consumer brands and many others navigate the challenges of the pandemic. Our customers need our services more than they ever needed as they need to act fast while they are taking multiple recovery actions across the globe. Since cross border travel has been hit the hardest in this pandemic, we are trying to help the customers on gradually building up domestic solutions as alternatives before the international travel across the world resumes.

For instance, we used data analytics on transactional data to help a financial institution count and counter the Covid-19 impact with a report outlining year-over-year shifts in spending broken down by day relevant subcategories was provided along with a quantitative financial assessment on clients’ businesses. This was followed by drawing up likely scenarios in terms of mitigating actions and growth opportunities.

We recently introduced a portal, www.shopopenings.com in UK to help people and businesses manage the transition from the lockdown by providing searchable information about merchant establishments that have re-opened. This is based on successful Mastercard card transactions at the relevant stores within the last 48 hours and up to the previous seven days.

Another instance, when an Asian country wanted to understand the impact of the lockdown on its economy it chose to analyse transaction data. Changing consumption patterns indicated the sectors that had been most affected.

Please share how you are setting high benchmarks in terms of data security and safety ? If you can give some examples.

We live in an increasingly interconnected world. On an average, a household has around 8 connected devices and an organisation has 100s. The attack surface that a cybercriminal can exploit grows with every device and so does the complexity of managing cyber security.

Mastercard strives to deliver best in class user experience that is safe, secure and fits well with customer’s needs. We have been leveraging latest mobile and AI tools to build an environment with highest levels of security. Our latest acquisition, RiskRecon, can monitor the cybersecurity performance of organisations using open-source intelligence by deploying passive, non-interfering techniques to discover organisation’s public systems and to analyse the cybersecurity risk posture of those systems. These scans can be completed easily without any technical help without any access to data or integration with existing systems. This helps in increasing the frequency of the scans without any disruption to business. We believe that these capabilities will help organisation identify gaps before the criminals and also address risks with the (aforementioned) expanding scope of cyber security.

Your views / any other significant factor.

We established ‘Data & Services Centre of Excellence’ in India, in 2013, to support Mastercard foreign group entities. The aforesaid support includes providing of data insights and strategic payment solutions by identifying spending trends derived from the billions of anonymous transactions processed by Mastercard every year.

Our Centre has recruited qualified analytics talent with payments industry, retail, technology and media experience to provide hands-on support to Mastercard foreign group entities on custom analytics.

The COE provides data insights by analysing spending trends derived from the 73 billion anonymous transactions processed by Mastercard and specific customer shared data elements, every year. Along with insights and analytics, this group also supports in data driven consulting.

New-age technology is witnessing a steady growth in India. Keeping that in mind, we (under the guidance and instructions of the Mastercard foreign group entities) established Artificial Intelligence Garage at the start of last year with an aim to enhance existing solutions as well as create new ones. With this, we embarked on an expansion and reskilling programme focused on AI where we are hiring experienced AI professionals as well as fresh computer science graduates and training them in this new-age technology.

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By learning about R and Data science, humans are provided with ample of opportunities in the world of data.

The education about data science is not enough. The more we read and learn about data science, the more we become fascinated about the intricate learning data science has to offer. Since data science is the new hype and will continue to remain so in the future, here are top 10 free online books that are coherent and comprehensive to understand R/Data science.

• 1. Advanced R by Hadley Wickham- Aiming at the intermediate and advanced users, the book talks about the fundamentals of R and the data types, and solving wide range of programs using functional programming. This book is a must go if one has to make the R code faster and efficient.

• 2. Introduction to Data Science by Rafael Irizarry- Introducing the concepts and skills for solving data analysis challenges, this book covers the concepts of probability, statistical interference, linear regression and machine learning. Moreover, this book assist in developing skills pertaining to R programming, data wrangling with dplyr, data visualization with ggplot2 and algorithm building with caret amongst others.

• 3. Cookbook for R by Winston Chang- Being a fantastic resource for getting started about plotting with ggplot and more, this book offers answers to lots of coding questions, which arise while making publication quality graphics with R.

• 4. Data Visualization: A practical introduction by Kieran Healy- Offering a hands-on introduction about visualization data using R and Wickham’s ggplot, this book assist in building the visualisations for data science piece by piece, from simple scatter plots to more complex graphics.

• 5. Exploratory Data Analysis with R by Roger D Peng- Based on the courses from John Hopkins Data Science Specialization, this book covers the basics in exploratory analysis, and topics needed for analyzing and visualising high-dimensional or multi-dimensional data like Hierarchial clustering, K-means clustering, and dimensionality reduction techniques-SVD and PCA.

• 6. Text Mining with R: A Tidy approach by Julia Silge and David Robinson- Being a great introductory book to learn about mining text data with R, this book helps in practicing the principles in text datasets. Moreover, using R and tideverse as examples to explore literature, news, social media data, this book is a must go for learning about text and data analysis, specifically for those who are interested in analysing the social media data.

• 7. An Introduction to Statistical and Data Sciences via R by Chester Ismay and Albert Y.Kim- Covering the basics of statistics for data science using R, this book helps in learning about exploring data, basics of statistics for data science and creating data stories using R.

• 8. Introduction to Empirical Bayes: Examples from Baseball Statistics by David Robinson- Introducing the empirical Bayesian approach for estimating credible intervals, A/B testing and mixture models with R code examples, this book illustrates statistical method for estimating click-through rates on ads, and success of experiments amongst others. This book is a must go if one wants to learn about data science and statistics for data science.

• 9. Data Analysis for the Life Sciences with R by Rafael A Irizarry and Michael I Love – Primarily focusing on high throughput data from genomics, the book helps the reader to solve problems with R code and assist in gaining better intuition behind the math theory. The methods described in this book are best suited for modern data science in any domain.

• 10. Modern Data Science for Modern Biology by Susan Holmes- With only 13 chapters, this book is a comprehensive guide for beginners to learn about R code, theory, and great visualization with ggplot 2. This book also covers various aspects of statistics for data science including, Mixture models, clustering, testing, dimensionality reduction techniques like PCA and SVD.

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These days, data drives everything in business. Not only does it inform business decisions, but it can actually be used to predict the future to some extent. Advents like machine learning and deep learning allow you to take the data you’ve gathered and project it into the future with greater accuracy, so you can run your company with additional knowledge and understanding of the factors that impact your business’s bottom line.

Understanding data is an important skill for any entrepreneur, and in the Deep Learning & Data Analysis Certification Bundle, you can go further than merely understanding it. You’ll learn how to actually use it to scale your business.

This eight-course bundle offers 30 hours of training on data analysis and deep learning technologies that will benefit your brand. The courses are taught by Minerva Singh, a Ph.D. graduate from Cambridge University, where she specialized in Tropical Ecology. Now, she’s a data scientist, using tools like R, QGIS, and Python in her daily workflow.

Here, she’ll teach you some of today’s most important tools for utilizing data in business. You’ll take a deep dive into data visualization, reporting, and analytics with Google Data Studio, and explore data analysis in tools like R. From there, you’ll delve into machine learning and deep learning practices. You’ll learn image processing and analysis with Python, regression modeling with R, how to create artificial neural networks in R and Python, and much, much more. Before you know it, you’ll be comfortable working with machine learning technologies to manipulate data and use it to make projections.

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Pandemic is a surprise visitor that sends shock-waves across the globe. When a sudden outbreak stroke, people of different communities started acting in contrasting ways to protect themselves from contracting the disease and tried to keep a balance on their normal life.

Hundreds of quick solutions started emerging from the crisis. The major problem with that is they were not effectively impacting the approach to slow-down the spread. Meanwhile, higher-ups started looking for perfect solutions that involve remote collaboration, contactless transactions, replacement of manual process with automated, robotic and other human-free processes. By bringing in all the changes, humans could be spared from victimization.

The cross-course of ideas and solutions by individuals are a major drawback during the raging of a contagion. There is a lack of central coordination which could confuse the situation with pandemic-related infodemics (reliable and unreliable information related to pandemic pilling up and bringing confusion) on social media platforms, scarcity of guidance to public health officials and other relatable sources. At this situation, authoritative data analysis, Artificial intelligence and IoT are the highly effective response to block the pandemic widespread.

The society is placed at an emergency situation to look at more data-driven and organised pandemic preparedness. China has remarkably placed a mile-stone in the pandemic accord. Seven months past the outbreak from its Wuhan city of Hubei province, China has responded rapidly with a unified nationwide approach. Officials are finding ways to access vast resources to save lives, control the infection and guide individuals to testing, treatment, quarantining and contact tracing.

Meanwhile, a lot of other countries like the United States are still struggling to find the exit door. The pitfall is due to old-fashioned approach towards the pandemic and its spread.

Some of the data analysis and other digital tools China has clouted in its response to controlling Covid-19

Online medical services: China is using the technology to provide self-service screening tools to reduce non-essential hospital visits while reducing the chances of spreading from medical bases and workload for health workers. Remote online healthcare programs are provided by Tencent and Alibaba, some of the biggest Silicon Valley player in the country. By availing the service, people can consult with doctors online and conduct self-assessments. They can decide whether to approach medical services further or remain at home.

Close track on virus emergence: Digital community management and community tools aid the residents by allowing volunteer teams of community residents to assist in disinfections and delivery supplies. The service also provides the facility for residents to receive a health QR code which guides them to submit their recent visits to pandemic epicentres. By enabling the service, the technology works to compile the data of close contacts with infected people, provides a three-colour scale that gives a person’s recent virus-related health history.

Long-distance health assistance: China’s 5G network has helped Chinese health professionals to extend their hands to help people at far ends. The network pitches-in the idea to connect hospitals in Wuhan with their counterparts in Beijing to get a consultation. The medical service can be utilised with ultra-high-definition medical images for better clarity.

IoT to scale the need: The same technology of connecting medical services are used in collaboration with the Internet of Things (IoT) to rapidly facilitate manufacturing of healthcare supplies like masks, PPEs and disinfectants. The technology scales the need for necessary products during the pandemic through its connectivity to various forms and alerts the domestic production houses to peep-up the production accordingly.

Other nations’ proactive reply to counter the spread of Covid-19 that are turning to be new normal

Counter-contagion nerve centres: New normalcies for nations are counter-contagion nerve centres that consolidate information to a spreadsheet of national health, immigration, customs, telecommunications and travel database. The consolidated data will be applied to an AI for further predictions. The AI can identify cases, generate real-time alerts and coordinate medical interventions. The facility can be used to do real-time screening at all ports in the country and the tools have the ability to provide information on the health status of the entrant through ports. It also gives guidelines on what category the entrant falls under ranging from a healthy person to quarantine or turn away based on the possibilities of the spread through the entrant.

Take-away for future: When previous pandemics like Spanish flu and Ebola broke out, the world was unaware of the AI technology assistance to contain the transmission. But now, the human race is remarkably noting down learnings for future pandemics. AI-driven predictive tools could be used by public health officials to slow the outbreak and stop community transmission. People will learn new ways to adapt to pandemic daily routine through community contagion dashboards. The AI-driven intelligence will guide the organisations to calculate the spread at the workplace based on the employee’s number and health condition. This could aid the authorities to decide on whether, when and how many employees could be brought back to the office. Proximity sensors will be embedded to the mobile phones that could get personal digital assistants with real-time ambient on crowded conditions to maintain social distancing. The AI-driven technology could also detect the offenders who might spread infection.

Installation of biosensing devices: The biosensing devices will be added to the basic public infrastructure. It could detect viral pathogens in the air, water, soil, surfaces, human and animal tissues. These biosensors could become a wearable by a human to predict the spread of infections from person-person and monitor disease progression among a large group of people. AI-driven service robots interrogate to people at public places to ascertain if they show signs of the virus. The environmental sensing can detect people with the disease even before they could recognise it. Another way of large screening of temperature at amassed places could be through infrared thermal screening.

The vision of new normalcy could be sensed with the presence of advanced technology. AI-driven technology, data analysis through cloud computing and Internet of Things are the major managers of public health infrastructure. Covid-19 has given life lessons and a lot of take-ways to human society. Going hand-in-hand with these technologies could help manage, monitor and contain future pandemic outbreaks.

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