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!

The post What is R and How R Works & Architecture ? appeared first on Artificial Intelligence.

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.

The post TOP 10 FREE ONLINE BOOKS TO LEARN R-CODE AND DATA SCIENCE appeared first on Artificial Intelligence.

Frost & Sullivan’s recent analysis, Global Big Data Analytics Market Fueling Artificial Intelligence, 2020, finds that the data security is a prime concern across sectors with the increasing deployment of the Internet of Things and proliferation of devices, which create copious amounts of data.

Globally, the BDA market is estimated to grow 4.5 times, garnering a revenue of $68.09 billion by 2025 from $14.85 billion in 2019, up at a staggering compound annual growth rate (CAGR) of 28.9%.

Additionally, amid the COVID-19 uncertainty, BDA continues to be a top deployment priority for enterprises as its use will help them remain competitive while accelerating innovation, especially in the healthcare sector to fight the coronavirus.

“Between the two major segments of the BDA market—data discovery and visualization (DDV) and advanced analytics (AA)—DDV is expected to become more mainstream as organizations realize the importance of data prepping, data management, and data visualization as the foundational building blocks for advanced analytics,” said Deviki Gupta, Information & Communication Technologies Senior Industry Analyst at Frost & Sullivan. “Going forward, AA’s growth is expected to rise dramatically after 2020 as use cases increase and customers grow more comfortable with data analytics overall.”

Gupta added: “From a regional perspective, North America and Latin America (NALA), led by North America, continue to be the largest contributors in the BDA market, followed by Europe, the Middle East, and Africa (EMEA), whereas Asia-Pacific (APAC) is the fastest-growing regional market for BDA. Further, from a vertical perspective, banking and financial services (BFS), government and intelligence (G&I), and retail segments that are focused on risk reduction, security, and drawing intelligence are the biggest revenue-contributing verticals.”As the market competition increases, BDA vendors look to diversify their product portfolios by offering edge analytics with the benefits of low latency and quick insights. This presents immense growth prospects:

• Package solutions to address industry-specific use cases such as defect detection and predictive maintenance analytics will be key to add value.
• Market participants should gain government support, which is a necessary foundational step for a lifelong learning record to exist.
• Professional development and customer support will be vital to driving the demand for BDA in healthcare.
• APAC, particularly China, is leading the manufacturing sector as well as the adoption of IoT devices. Thus, China should be a target for BDA vendors looking to expand in this market.
• Vendors must provide consultative services to help customers understand the correct software and hardware combinations to solve business problems.

Global Big Data Analytics Market Fueling Artificial Intelligence, 2020 is the latest addition to Frost & Sullivan’s Information & Communication Technologies research and analyses available through the Frost & Sullivan Leadership Council, which helps organizations identify a continuous flow of growth opportunities to succeed in an unpredictable future.

The post Global big data analytics market ‘to grow 4.5 times by 2025’ appeared first on Artificial Intelligence.

The demand for data scientists is rising exponentially every day. This is because data scientists are believed to have profound knowledge and expertise in fields like machine learning, statistics, mathematics, computing science, data visualization, and communication. Moreover, as companies witness the proliferation of data, they need to tap this resource for extracting value that shall help them boost business and help in adapting to the changing technologies in the market. This is why companies need to hire the right people with reliable data science skills. These data scientists can help manipulate vast amounts of data with sophisticated statistical and visualization techniques and predict potential outcomes and possible threats. Also, as demand increases, it presents promising career prospects for students and existing professionals.

On a typical day, a data scientist’s job includes data mining by using APIs or building ETL pipelines, data cleaning using programming languages like R or Python. She explores disparate and disconnected data sources look for better ways to analyze information. Most of the data scientists have the ability to assist businesses to interpret and manage data and solve intricate problems using expertise in a variety of data niches with correct datasets and variables. They also build models and design algorithms to mine stores of big data, to recognize patterns and trends. Later they communicate these findings to stakeholders using tools like visualization. Currently, the ‘data scientist’ is deemed as one of the sexiest jobs of the 21st century.

While it is common and fundamental to have experience in Github, R, Python, Cloud computing, machine learning, knowledge of multivariable calculus, probability and statistics, SQL, Tensorflow, Big data, and soft skills like data storytelling, good communication, business acumen, with critical thinking, there are few skills that can set one apart in this highly competitive domain. Some of them are:

Data Wrangling: Data sets can be messy and chaotic, with database fields ill-defined, valueless, used for various purposes in the same field, be full of outliers that no-one can explain, and so on. Hence it is a must to transform, standardize, normalize, and clean them undertaking any real modeling work to extract insights. Data wrangling is the process of transforming data from one format to another. And for this, patience is a must, as no amount of time and knowledge can make up for a poorly represented dataset. E.g., Python Data Wrangling

Web Analytics: As the audience, i.e., the customer is increasingly moving towards social media platforms like Facebook, Twitter, Instagram, etc. these sites act as a storehouse of untapped data that can be used to improve customer services with personalized experiences and enhance products and services offered by a brand. Therefore, it is crucial to deploy web analytics algorithms to collect online data and use it to understand the target customers better. Some common web analytic tools include Kissmetrics, Mixpanel, and Google Analytics, which let companies track and analyze website traffic.

Visualization and Storytelling: While this forms an essential part of a data science job, recruiters may not pay much attention to this skill while hiring. However, through data visualization, one can showcase the results coming from a machine learning algorithm. As mentioned above, it lets data scientists describe and communicate their findings to technical and non-technical audiences. Some useful tools for data visualization are Matplotlib, d3.js, Tableau, ggplot. One can also use eye-catching, high-quality charts, and graphs to present the findings clearly and concisely.

Along with that, a data scientist must have a creative mind to important to increase data storytelling skills. This helps in engaging with stakeholders and gaining their support when required.

The post UNIQUE SKILLS THAT CAN SET DATA SCIENTISTS APART FROM OTHERS IN THEIR FIELD appeared first on Artificial Intelligence.

One curious side-effect of the work to digitize books and historical texts is the ability to search these databases for words, when they first appeared and how their frequency of use has changed over time.

The Google Books n-gram corpus is a good example (an n-gram is a sequence of n words). Enter a word or phrase and it’ll show you its relative usage frequency since 1800. For example, the word “Frankenstein” first appeared in the late 1810s and has grown in popularity ever since.

By contrast, the phrase “Harry Potter” appeared in the late 1990s, gained quickly in popularity but never overtook Frankenstein — or Dracula, for that matter. That may be something of surprise given the unprecedented global popularity of J.K. Rowling’s teenage wizard.

And therein lies the problem with a database founded on an old-fashioned, paper-based technology. The Google Books corpus records “Harry Potter” once for each novel, article and text in which it appears, not for the millions of times it is printed and sold. There is no way to account for this level of fame or how it leaves others in the shade.

Today that changes, thanks to the work of Thayer Alshaabi at the Computational Story Lab at the University of Vermont and a number of colleagues. This team has created a searchable database of over 100 billion tweets in more than 150 languages containing over a trillion 1-grams, 2-grams and 3-grams. That’s about 10 per cent of all Twitter messages since September 2008.

Data Visualization

The team has also developed a data visualization tool called Storywrangler that reveals the popularity of any words or phrases based on the number of times they have been tweeted and retweeted. The database shows how this popularity waxes and wanes over time.

“In building Storywrangler, our primary goal has been to curate and share a rich, language-based ecology of interconnected n-gram time series derived from Twitter,” say Alshaabi and co.

Storywrangler immediately reveals the “story” associated with a wide range of events, individuals and phenomenon. For example, it shows the annual popularity of words associated with religious festivals such as Christmas and Easter. It tells how phrases associated with new films burst into Twittersphere and then fade away, while TV series tend to live on, at least throughout the series’ lifetime. And it reveals the emergence of politico-social movements such as Brexit, Occupy #MeToo and Black Lives Matter.

The storylines can also be compared with other databases to provide more fine-grained insight and analysis. For example, the popularity of film titles on Twitter can be compared with the film’s takings at the box office; the emergence of words associated with disease can be compared with the number of infections recorded by official sources; and words associated with political unrest can be compared with incidents of civil disobedience.

That’s useful because this kind of analysis provides a new way to study society, potentially with predictive results. Indeed, computer scientists have long suggested that social media can be used to predict the future.

Cultural Significance

These storylines have social and cultural significance too. “Our collective memory lies in our recordings — in our written texts, artworks, photographs, audio and video — and in our retellings and reinterpretations of that which becomes history,” say Alshaabi and colleagues.

Now anyone can study it with Storywrangler. Try it, it’s interesting.

As for Harry Potter, Frankenstein and Dracula, the tale that Storywrangler tells is different from the Google Books n-gram corpus. Harry Potter is significantly more popular than his grim-faced predecessors and always has been on Twitter. In 2011, Harry Potter was the 44th most popular term on Twitter while Dracula has never risen higher than 2653rd. Frankenstein’s best rank is 3560th.

The post How Data Mining Visualizes Story Lines in the Twittersphere appeared first on Artificial Intelligence.

Marketing- The way legacy processes perceived it has come along a long way. Today it is all about the advancements in data and how data-driven enterprises talk data in a bid to attract more potential buyers. Data and Analytics are the two buzzwords that have been thrown around marketing for quite some time, but what do marketers assess from these two hefty words unless they are trained to handle the digital disruption that big data brings to even marketing?

As discussed, marketing is slowly changing, driven by data it is the way businesses leverage on multi-data sources to connect at a personal level with the customers. Talk of personalised and more curated offerings suited to their unique needs. Marketing analytics integrates processes and technologies which let marketers evaluate the success of their marketing initiatives by weighing tangible performance metrics like social media likes and followers and so on. The most commonly used metrics for marketing analytics include ROI, marketing attribution and the overall marketing effectiveness. In short marketing, analytics tells exactly anytime how the products and services are performing vying for customer loyalty.

Building a Case for Marketing Analytics Success

Marketing Analytics is a comparatively new term, and marketing managers are still trying to make a way how to deploy data for customer segmentation, targeting and eventual placement of the product or the services in the customer life cycle.

Here are a few steps that will mean to be helpful-

• Data Assessment

Market Analytics professionals need to know the category of data they are working with, whether it is structured or unstructured or live or static. The USP of marketing data being its high levels of dynamism makes assessment more complex and highly indispensable to steer the future course of action.

• Data Pipelines

Data needs to be prepared for analytics and even future analysis by citizen data scientists who may be the marketing analytics professionals themselves. Data pipelines involve building clean and model ready data for marketing chores.

• Data Modelling

What is data without analytics and modelling? This step essentially involves marketing analysts to build and train models that predict customer demands and preferences. Data Modelling step is highly critical for building marketing blueprints.

• Data Visualization

This step involves presentation of the earlier steps to the C-Suite especially the Chief Marketing Technologists who are the decision-makers of an enterprise.

Data Literacy and Marketing Analytics

Marketing analytics is useless without data, and without proper data learning, the entire blueprint goes meaningless. This calls for the push towards data literacy.

When it comes to marketing analytics, the time taken to improve data literacy and take a business to the next level. With better insights, enterprises can ensure that they will have a more customer-centric approach and are able to deliver the right products to the targeted customers at the right time using the correct channels. The clue lies in making data more approachable!

The post WHY SHOULD ENTERPRISES INVEST IN MARKETING ANALYTICS DATA LITERACY? appeared first on Artificial Intelligence.

The field of data science seems to just get bigger and more popular everyday. According to LinkedIn, data science was one of the fastest-growing job fields in 2017 and in 2020 Glassdoor ranked the job of data science as one of the three best jobs within the United States. Given the growing popularity of data science, it’s no surprise that more people are getting interested in the field. Yet what is data science exactly?

Let’s get acquainted with data science, taking some time to define data science, explore how big data and artificial intelligence is changing the field, learn about some common data science tools, and examine some examples of data science.

Defining Data Science

Before we can explore any data science tools or examples, we’ll want to get a concise definition of data science.

Defining “data science” is actually a little tricky, because the term is applied to many different tasks and methods of inquiry and analysis. We can begin by reminding ourselves of what the term “science” means. Science is the systematic study of the physical and natural world through observation and experimentation, aiming to advance human understanding of natural processes. The important words in that definition are “observation” and “understanding”.

If data science is the process of understanding the world from patterns in data, then the responsibility of a data scientist is to transform data, analyze data, and extract patterns from data. In other words, a data scientist is provided with data and they use a number of different tools and techniques to preprocess the data (get it ready for analysis) and then analyze the data for meaningful patterns.

The role of a data scientist is similar to the role of a traditional scientist. Both are concerned with the analysis of data to support or reject hypotheses about how the world operates, trying to make sense of patterns in the data to improve our understanding of the world. Data scientists make use of the same scientific methods that a traditional scientist does. A data scientist starts by gathering observations about some phenomena they would like to study. They then formulate a hypothesis about the phenomenon in question and try to find data that nullifies their hypothesis in some way.

If the hypothesis isn’t contradicted by the data, they might be able to construct a theory, or model, about how the phenomenon works, which they can go on to test again and again by seeing if it holds true for other similar datasets. If a model is sufficiently robust, if it explains patterns well and isn’t nullified during other tests, it can even be used to predict future occurrences of that phenomenon.

A data scientist typically won’t gather their own data through an experiment. They usually won’t design experiments with controls and double-blind trials to discover confounding variables that might interfere with a hypothesis. Most data analyzed by a data scientist will be data gained through observational studies and systems, which is a way in which the job of a data scientist might differ from the job of a traditional scientist, who tends to perform more experiments.

That said, a data scientist might be called on to do a form of experimentation called A/B testing where tweaks are made to a system that gathers data to see how the data patterns change.

Regardless of the techniques and tools used, data science ultimately aims to improve our understanding of the world by making sense out of data, and data is gained through observation and experimentation.  Data science is the process of using algorithms, statistical principles, and various tools and machines to draw insights out of data, insights that help us understand patterns in the world around us.

What Do Data Scientists Do?

You might be seeing that any activity that involves the analysis of data in a scientific manner can be called data science, which is part of what makes defining data science so hard. To make it more clear, let’s explore some of the activities that a data scientist might do on a daily basis.

On any given day, a data scientist might be asked to: create data storage and retrieval schema, create data ETL (extract, transform, load) pipelines and clean up data, employ statistical methods, craft data visualizations and dashboards, implement artificial intelligence and machine learning algorithms, make recommendations for actions based on the data.

Let’s break the tasks listed above down a little.

Data Storage, Retrieval, ETL, and Cleanup

A data scientist may be required to handle the installation of technologies needed to store and retrieve data, paying attention to both hardware and software. The person responsible for this position may also be referred to as “Data Engineer”. However, some companies include these responsibilities under the role of data scientists. A data scientist may also need to create, or assist in the creation of, ETL pipelines. Data very rarely comes formatted just as a data scientist needs. Instead, the data will need to be received in a raw form from the data source, transformed into a usable format, and preprocessed (things like standardizing the data, dropping redundancies, and removing corrupted data).

Statistical Methods

The application of statistics is necessary to turn simply looking at data and interpreting it into an actual science. Statistical methods are used to extract relevant patterns from datasets, and a data scientist needs to be well versed in statistical concepts. They need to be able to discern meaningful correlations from spurious correlations by controlling for confounding variables. They also need to know the right tools to use to determine which features in the dataset are important to their model/have predictive power. A data scientist needs to know when to use a regression approach vs. a classification approach, and when to care about the mean of a sample vs. the median of a sample. A data scientist just wouldn’t be a scientist without these crucial skills.

Data Visualization

A crucial part of a data scientist’s job is communicating their findings to others. If a data scientist can’t effectively communicate their findings to others, than the implications of their findings don’t matter. A data scientist should be an effective story-teller as well. This means producing visualizations that communicate relevant points about the dataset and the patterns discovered within it. There is a large number of different data visualization tools that a data scientist might use, and they may visualize data for the purposes of initial, basic exploration (exploratory data analysis) or visualize the results that a model produces.

Recommendations and Business Applications

A data scientist needs to have some intuition of the requirements and goals of their organization or business. A data scientist needs to understand these things because they need to know what types of variables and features they should be analyzing, exploring patterns that will help their organization achieve its goals. The data scientists need to be aware of the constraints that they are operating under and the assumptions that the organization’s leadership are making.

Machine Learning and AI

Machine learning and other artificial intelligence algorithms and models are tools used by data scientists to analyze data, identify patterns within data, discern relationships between variables, and make predictions about future events.

Traditional Data Science vs. Big Data Science

As data collection methods have gotten more sophisticated and databases larger, a difference has arisen between traditional data science and “big data” science.

Traditional data analytics and data science is done with descriptive and exploratory analytics, aiming to find patterns and analyze the performance results of projects. Traditional data analytics methods often focus on just past data and current data. Data analysts often deal with data that has already been cleaned and standardized, while data scientists often deal with complex and dirty data. More advanced data analytics and data science techniques might be used to predict future behavior, although this is more often done with big data, as predictive models often need large amounts of data to be reliably constructed.

“Big data” refers to data that is too large and complex to be handled with traditional data analytics and science techniques and tools. Big data is often collected through online platforms and advanced data transformation tools are used to make the large volumes of data ready for inspection by data science. As more data is collected all the time, more of a data scientists job involves the analysis of big data.

Data Science Tools

Common data science tools include tools to store data, carry out exploratory data analysis, model data, carry out ETL, and visualize data. Platforms like Amazon Web Services, Microsoft Azure, and Google Cloud all offer tools to help data scientists store, transform, analyze, and model data. There are also standalone data science tools like Airflow (data infrastructure) and Tableau (data visualization and analytics).

In terms of machine learning and artificial intelligence algorithms used to model data, they are often provided through data science modules and platforms like TensorFlow, PyTorch, and the Azure Machine-learning studio. These platforms like data scientists make edits to their datasets, compose machine learning architectures, and train machine learning models.

Other common data science tools and libraries include SAS (for statistical modeling), Apache Spark (for the analysis of streaming data), D3.js (for interactive visualizations in the browser), and Jupyter (for interactive, sharable code blocks and visualizations).

Examples of Data Science

Examples of data science and its applications are everywhere. Data science has applications in everything from food delivery, sports, traffic, and health. Data is everywhere and so data science can be applied to everything.

In terms of food, Uber is investing in an expansion to its ride-sharing system focused on the delivery of food, Uber Eats. Uber Eats needs to get people their food in a timely fashion, while it is still hot and fresh. In order for this to occur, data scientists for the company need to use statistical modeling that takes into account aspects like distance from restaurants to delivery points, holiday rushes, cooking time, and even weather conditions, all considered with the goal of optimizing delivery times.

Sports statistics are used by team managers to determine who the best players are and form strong, reliable teams that will win games. One notable example is the data science documented by Michael Lewis in the book Moneyball, where the general manager of the Oakland Athletics team analyzed a variety of statistics to identify quality players that could be signed to the team at relatively low cost.

The analysis of traffic patterns is critical for the creation of self-driving vehicles. Self-driving vehicles must be able to predict the activity around them and respond to changes in road conditions, like the increased stopping distance required when it is raining, as well as the presence of more cars on the road during rush hour. Beyond self-driving vehicles, apps like Google Maps analyze traffic patterns to tell commuters how long it will take them to get to their destination using various routes and forms of transportation.

In terms of health data science, computer vision is often combined with machine learning and other AI techniques to create image classifiers capable of examining things like X-rays, FMRIs, and ultrasounds to see if there are any potential medical issues that might show up in the scan. These algorithms can be used to help clinicians diagnose disease.

Ultimately, data science covers numerous activities and brings together aspects of different disciplines. However, data science is always concerned with telling compelling, interesting stories from data, and with using data to better understand the world.

The post What is Data Science? appeared first on Artificial Intelligence.

Data science and artificial intelligence are no longer buzz words in the biomedical research community. Physicians and other caregivers are increasingly being encouraged by hospitals and health insurance companies to utilize low-resolution dense biometric data captured using wearable medical devices. However classical healthcare heavily relies on high accuracy sparse datasets, i.e. patients are expected to get a thorough medical checkup once in a while, as opposed to continuous monitoring of a handful of vital parameters. The most significant impact of data science will be in helping physicians extract clinically relevant information from such dense low-quality data sets.

Radiologists and cardiologists are increasingly relying on automated high dimensional image processing algorithms to detect the likelihood of coronary artery disease from non-contrast chest CT scans. Similarly, radiologists and pulmonologists are using similar artificial intelligence based technology to identify clinically relevant structural and functional parameters of the lungs from chest CT scans. Understanding the basics of artificial intelligence will empower physicians to go beyond using these tools as black-boxes and deliver maximum impact for care pathways.

In this article, we have listed five such reasons why physicians and caregivers should learn about emerging technology such as data science and artificial intelligence

1. Diagnose using large volumes of data generated from continuous monitoring

With the advent of wearable medical device companies such as CloudDX, Snap40 and QuasaR clinicians can now look at continuous daily biometric data collected over months. Both primary and advanced data science techniques can be used to derive medically relevant outcomes from these dense data. Basic descriptive statistical results like the average resting heart rate could give you a quick understanding of the overall cardiac health of the patient. More advanced indicators such as stress index or LF/HF ratio of RR distance could be used to predict chances of heart arrhythmias more accurately. Data science will allow physicians to analyze these data sets both at local (days or weeks) and global (months or years) timescales, using a combination of both early warning scores and visual inspection of the data.

The post 5 Reasons Why Doctors Should Learn Data Science appeared first on Artificial Intelligence.