Data scientists are faced with the need to conduct continual experiments, particularly regarding user interface and product marketing. Designing experiments is a cornerstone of the practice of statistics, with clear application to data science. In this lesson, you’ll learn about A-B testing and hypothesis, or significance tests—critical aspects of experimental design for data science. What you’ll learn—and how you can apply it You will learn the central concepts of A-B testing, understand its role in designing and conducting data science experiments, and the characteristics of a proper A-B test. Through a series of sample tests, you’ll learn how to interpret results, and apply that insight to your analysis of the data. Since A-B tests are typically constructed with a hypothesis in mind, you’ll also learn how to conduct various hypothesis, or significance tests, enabling you to avoid misinterpreting randomness. This lesson is for you because You are a data scientist or analyst working with data, and want to gain beginner-level knowledge of key statistical concepts to improve the design, and outcome of your experimental tests with data. Prerequisites: Basic familiarity with coding in R Materials or downloads needed: n/a

The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. Practical Data Science with Hadoop® and Spark The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Cloud services are now ubiquitous in data science and more broadly in technology as well. This week, I speak to Mark Souza, Tobias Ternström, and Corey Sanders about various aspects of data at scale. We discuss the embedding of R into SQLServer, SQLServer on linux, open source, and a few other cloud topics.

In this session, Mike Flowers, Chief Analytics Officer, Enigma, sat with Vishal Kumar, CEO AnalyticsWeek and shared his journey as an analytics executive, best practices, hacks for upcoming executives, and some challenges/opportunities he's observing as a Chief Analytics Officer. Mike discussed his journey from trial prosecutor to Chief Analytics Officer, sharing some great stories on how Govt. embraces data analytics.

Timeline: 0:29 Mike's journey. 23:32 Mike's role in Enigma. 27:46 The role of CAO in Enigma. 29:50 How much Mike's role is customer-facing vs. in facing. 30:00 Getting over the roadblocks of working with the government. 34:06 Creating a data bridge. 39:17 Collaboration in the data science field. 46:02 Challenges in working with Clients at Enigma. 51:34 Benefits of having a legal background before coming to data analytics.

Podcast link: https://futureofdata.org/enigma_io/

Here's Mike Flowers Bio: Mike is Chief Analytics Officer at New York City tech start-up Enigma, an operational data management and intelligence company, where he leads data scientists assisting the development and deployment of decision-support technologies to Fortune 500 clients in compliance, manufacturing, banking, and finance, and several U.S. and foreign government agencies. In addition, he is a Senior Fellow at Bloomberg Philanthropies, working with select U.S. city governments to launch sustainable analytics programs. Mike is also an advisor to numerous organizations in a wide variety of fields, including, for example, Weil Cornell Medical College, the Inter-American Development Bank, the Office of the New York State Comptroller, the Greater London Authority, the government of New South Wales, Australia, and the French national government.

From 2014-15, Mike was an Executive-in-Residence and the first MacArthur Urban Science Fellow at NYU’s Center for Urban Science and Progress, where he advised students and faculty on projects to advance data-driven decision-making in city government.

From 2009-2013, Mike served under Mayor Michael Bloomberg as New York City’s first Chief Analytics Officer. During his tenure, he founded the Mayor’s Office of Data Analytics, which provides quantitative support to the city’s public safety, public health, infrastructure development, finance, economic development, disaster preparedness and response, legislative, sustainability, and human services efforts. In addition, Mike designed and oversaw the implementation of NYC DataBridge, a first-of-its-kind citywide analytics platform that enables the sharing and analysis of city data across agencies and with the public, and he ran the implementation of the city’s internationally-recognized Open Data initiative. For this work, Mike was twice recognized by the White House for innovation.

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The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Platform as a service is a growing trend in data science where services like fraud analysis and face detection can be provided via APIs. Such services turn the actual model into a black box to the consumer. But can the model be reverse engineered? Florian Tramèr shares his work in this episode showing that it can. The paper Stealing Machine Learning Models via Prediction APIs is definitely worth your time to read if you enjoy this episode. Related source code can be found in https://github.com/ftramer/Steal-ML.

This book provides a comprehensive introduction to R programming, a powerful tool for data science and statistics. Throughout the book, readers will explore programming constructs, data structures, and popular R packages, gaining the skills needed for practical applications and problem-solving. What this Book will help me do Understand R's foundational concepts like variables, data types, and functions. Learn how to use R for data analysis, visualization, and machine learning tasks. Develop advanced R skills such as meta-programming and performance optimization. Master object-oriented programming using R's S3, S4, and R6 systems. Gain confidence in utilizing R for creating web scraping scripts and interactive reports. Author(s) None Ren, an experienced software developer and educator, specializes in languages for data analysis, including R. With years of practical experience and teaching R programming, they bring clarity and depth to complex topics. Their approachable writing style ensures learners at any level can engage effectively. Who is it for? This book is ideal for professionals in data science, statistics, and related fields with basic programming skills looking to delve into R programming. It caters to beginners and those consolidating their knowledge of R, aiming to develop practical skills for data manipulation and analysis.

In this session, Joe DeCosmo, Chief Analytics Officer, Enova International, sat with Vishal Kumar, CEO AnalyticsWeek and shared his journey to Chief Analytics Officer, life @ Enova, and some challenges/opportunities as he is observing as an executive, industry observer, and a Chief Analytics Officer.

Timeline: 0:29 Joe's journey. 5:05 Credit risk and fraud prevention models. 6:27 Enova: in facing or outfacing? 9:12 Enova area of expertise. 10:47 Enova decisions: Center of Excellence? 12:36 Depths and breadths of decision making at Enova. 14:51 CDO, CAO, and CTO. 17:24 Who owns the data at Enova? 19:55 Challenges in building a data culture. 25:52 Convincing leaders towards data science. 31:24 Business challenges that analytics is solving. 34:15 Getting started with data analytics as a business. 38:11 Exciting trends in data analytics. 41:09 Art of doing business and science of doing business. 44:00 Advice for budding CAOs.

Podcast link: https://futureofdata.org/analyticsweek-leadership-podcast-with-joe-decosmo-enova-international/

Here's Joe's Bio: Joe DeCosmo is the CAO of Enova International, where he leads a multi-disciplinary analytics team, providing end-to-end data and analytic services to Enova’s global online financial service brands and delivering real-time predictive analytics services to clients through Enova Decisions. Prior to Enova, Joe served as Director and Practice Leader of Advanced Analytics for West Monroe Partners and held a number of executive positions at HAVI Global Solutions and the Allant Group. He is also Immediate Past-President of the Chicago Chapter of the American Statistical Association and serves on the Advisory Board of the University of Illinois at Chicago's College of Business.

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. Youâ??ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, youâ??ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills

Explore how to leverage Apache Spark for efficient big data analytics and machine learning solutions in "Spark for Data Science". This detailed guide provides you with the skills to process massive datasets, perform data analytics, and build predictive models using Spark's powerful tools like RDDs, DataFrames, and Datasets. What this Book will help me do Gain expertise in data processing and transformation with Spark. Perform advanced statistical analysis to uncover insights. Master machine learning techniques to create predictive models using Spark. Utilize Spark's APIs to process and visualize big data. Build scalable and efficient data science solutions. Author(s) This book is co-authored by None Singhal and None Duvvuri, both accomplished data scientists with extensive experience in Apache Spark and big data technologies. They bring their practical industry expertise to explain complex topics in a straightforward manner. Their writing emphasizes real-world applications and step-by-step procedural guidance, making this a valuable resource for learners. Who is it for? This book is ideally suited for technologists seeking to incorporate data science capabilities into their work with Apache Spark, data scientists interested in machine learning algorithms implemented in Spark, and beginners aiming to step into the field of big data analytics. Whether you are familiar with Spark or completely new, this book offers valuable insights and practical knowledge.

In this session, Beena Ammanath, Data Science Products at General Electric, sat with Vishal Kumar, CEO AnalyticsWeek and shared her journey as an analytics executive, life @ GE, future of analytics in the industrial sector, how Predix is helping other industrial companies cope up with growing data, and some challenges/Opportunities she's observing as an analytics executive.

Timeline: 0:29 Beena's journey. 5:19 Data science in the manufacturing sector. 7:03 Driving data science in the manufacturing sector. 9:39 Bringing in the data culture into the manufacturing sector. 11:35 Upskilling and being relevant as a data scientist. 13:27 Hacks to managing data teams well. 16:08 What's Predix? 19:06 Investment opportunities for data science in manufacturing. 21:07 Challenges manufacturing businesses in data. 24:46 IoT and manufacturing. 25:18 Dealing with IoT vendors at Predix. 26:24 Ontology of data at Predix. 29:43 Dealing with the new rules and laws in the IoT sector. 31:30 Interesting use cases in the manufacturing industry. 34:37 Open source vs. enterprise. 35:35 Getting recruited as a data scientist in manufacturing. 40:07 Pitching your product for a manufacturing company.

Podcast link: https://futureofdata.org/leadership-playbook-with-beena-ammanath-ge/

Here's Beena's Bio: Beena Ammanath is Board Director at ChickTech and Head of Data Science Products at General Electric. She is a seasoned technology leader with over 24 years of a proven track record of building, and leading high-performance teams from the ground-up focused on strategy and successful execution of industrial scale technology products and services. She has an impressive track record, having worked at recognized international organizations British Telecom, E*trade, Thomson Reuters, Bank of America, and Silicon Valley startups in engineering and management positions.

She is also helping build the next-gen of computer scientists through her role on the Industry Advisory Board for Cal Poly. She holds a Masters in Computer Science and an MBA in Finance. She has been a featured speaker on the topics of data science, big data, technology transformation, and women in leadership at numerous industry conferences.

Throughout her career in technology, Beena has been a strong advocate for women in positions of technology leadership and has established herself as a voice for resolving gender disparities.

Follow @beena_ammanath

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

In this fourth edition of O’Reilly’s Data Science Salary Survey, 983 respondents working across a variety of industries answered questions about the tools they use, the tasks they engage in, and the salaries they make. This year’s survey includes data scientists, engineers, and others in the data space from 45 countries and 45 US states. The 2016 survey included new questions, most notably about specific data-related tasks that may affect salary. Plug in your own data points to the survey model and see how you compare to other data science professionals in your industry. With this report, you’ll learn: Where data scientists make the highest salaries—by country and by US state Tools that respondents most commonly use on the job, and tools that contribute most to salary Two activities that contribute to higher earnings among respondents How gender and bargaining skills affect salaries when all other factors are equal Salary differences between those using open source tools vs those using proprietary tools Salary differences between those who rely on Python vs those who use several tools Participate in the 2017 Survey The survey is now open for the 2017 report. Spend just 5 to 10 minutes and take the anonymous salary survey here: https://www.oreilly.com/ideas/take-the-2​017-data-science-salary-survey.

Master the essentials of using R for Business Intelligence in this practical guide. Through real-world use cases, learn to manipulate data, build predictive models, and create interactive dashboards to communicate insights effectively. What this Book will help me do Extract, clean, and analyze complex datasets for business applications. Perform advanced statistical and machine learning techniques like predictive modeling and clustering. Gain proficiency in creating interactive dashboards using R and the Shiny package. Develop real-world analytics skills that enhance decision-making processes. Integrate Business Intelligence workflows using R for operations, marketing, and finance domains. Author(s) None Gendron is an expert in data science and business analytics, passionate about teaching professionals to make data-driven decisions. With extensive experience in R programming, None has a knack for breaking down complex topics into easily digestible knowledge. Their practical approach ensures readers not only understand but can directly apply the concepts. Who is it for? This book is ideal for data analysts, business professionals, and entry-level data scientists looking to enhance their analytical skills. If you're familiar with basic R programming and aspire to derive actionable insights from data in the business context, this is the resource for you. It will also resonate with those in operations, marketing, or finance seeking to integrate data analysis into their decision-making.

In this session, Eloy Sasot, Head of Analytics, NewsCorp, sat with Vishal Kumar, CEO AnalyticsWeek and shared his journey as an analytics executive, best practices, hacks for upcoming executives, and some challenges/opportunities she's observing as a Chief Analytics Officer.

Timeline:

0:29 Eloy's journey. 4:43 Why work in a publishing house? 7:16 Non-tech industry doing tech stuff. 10:18 Tips for a small business to get started with data science. 13:46 Creating a culture of data science in a company. 17:23 Convincing leaders towards data science. 22:05 Initial days for a leader in creating a data science practice. 27:20 Putting together a data science team. 29:18 Choosing the right tool. 33:00 Keep oneself tool agnostic. 35:20 CDO, CAO, and CTO. 38:58 Defining a data scientist at News Corp. 42:12 Future of data analytics. 46:37 Blaming everything on Big Data.

Podcast Link: https://futureofdata.org/563533-2/

Here's Eloy's Bio: Eloy is the CAO at News Corp, a worldwide network of leading companies in the worlds of diversified media, news, education, and information services, such as The Wall Street Journal, Dow Jones, New York Post, The Times, The Sun, The Australian, HarperCollins, Move, Storyful and Unruly

Prior to this, Eloy led Pricing, Data Science and Data Analytics for HarperCollins Publishers, the second-largest consumer book publisher in the world, with operations in 18 countries, nearly 200 years of history, and more than 65 unique imprints. Since joining HarperCollins in 2011, Eloy pioneered the creation and development of the pricing function, first in the UK, and then its extension to an international scale for the global company. He worked with his teams and each division around the world to drive data-driven decision-making, with a particular focus on Pricing. Besides his global role, he was Board Level Director of HarperCollins UK.

He holds an MBA from INSEAD and a Master’s in Mathematical Engineering from INSA Toulouse.

Follow @eloysasot

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Once upon a time, in an industry near and dear, lived an analyst. And that analyst needed to present the results of her analysis to a big, scary, business user. This is not a tale for the faint of heart, dear listener. We're talking the Brothers Grimm before Disney got their sugar-tipped screenwriting pens on the stories! Actually, this isn't a fairy tale at all. It's a practical reality of the analyst's role: effectively communicating the results of our work out to the business. Join Michael and Tim and special guest, Storytelling Maven Brent Dykes, as they look for a happy ending to The Tale of the Analyst with Data to Be Conveyed. Tangential tales referenced in this episode include: Web Analytics Action Hero, Brent Dykes Articles on Forbes.com, The Wizard of Oz, Made to Stick, Data Storytelling: The Essential Data Science Skill Everyone Needs, The Story of Maths, and mockaroo.com.

The "R for Data Science Cookbook" is your comprehensive guide to tackling data problems using R. Focusing on practical applications, you will learn data manipulation, visualization, statistical inference, and machine learning with a hands-on approach using popular R packages. What this Book will help me do Master the use of R's functional programming features to streamline your analysis workflows. Extract, transform, and visualize data effectively using robust R packages like dplyr and ggplot2. Learn to create intuitive and professional visualizations and reports that communicate insights effectively. Implement key statistical modeling and machine learning techniques to solve real-world problems. Acquire expertise in data mining techniques, including clustering and association rule mining. Author(s) Yu-Wei Chiu, also known as David Chiu, is an experienced data scientist and educator. With a solid technical background in using R for data science, he combines theory with practical applications in his writing. David's approachable style and rich examples make complex topics accessible and engaging for learners. Who is it for? This book is perfect for individuals who already have a foundation in R and are looking to deepen their expertise in applying R to data science tasks. Ideal readers are analysts and statisticians eager to solve real-world problems using practical tools. If you're aspiring to work effectively with large data sets or want to learn versatile data analysis techniques, this book is designed for you. It bridges the gap between theoretical knowledge and actionable skills, making it invaluable for professionals and learners alike.

In this session, Michael O'Connell, Chief Analytics Officer, TIBCO Software, sat with Vishal Kumar, CEO AnalyticsWeek and shared his journey as a Chief Analytics Executive, shared best practices, cultural hacks for upcoming executives, shared his perspective on changing BI landscape and how businesses could leverage that and shared some challenges/opportunities he's observing across various industries.

Timeline:

0:28 Michael's journey. 4:12 CDO, CAO, and CTO. 7:30 Adoption of data analytics capabilities. 9:55 The BI industry dealing with the latest in data analytics. 12:10 Future of stats. 14:58 Creating a center of excellence with data. 18:00 Evolution of data in BI. 21:40 Small businesses getting started with data analytics. 24:35 First steps in the process of becoming a data-driven company. 26:28 Convincing leaders towards data science. 28:20 Shortest route to become a data scientist. 29:49 A typical day in Michael's life.

Podcast Link: https://futureofdata.org/analyticsweek-leadership-podcast-with-michael-oconnell-tibco-software/

Here's Michael's Bio: Michael O’Connell, Chief Analytics Officer, TIBCO Software, developing analytic solutions across a number of industries including Financial Services, Energy, Life Sciences, Consumer Goods & Retail, and Telco, Media & Networks. Michael has been working on analytics software applications for the past 20 years and has published more than 50 papers and several software packages on analytics methodology and applications. Michael did his Ph.D. work in Statistics at North Carolina State University and is Adjunct Professor Statistics in the department.

Follow @michoconnell

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Which companies have adopted technologies such as Hadoop and Spark, as well as data science in general? And which industries are lagging behind? This O’Reilly report provides the results of a unique, data-driven analysis of the market for big data products and technologies. Using eye-catching charts and visualizations, Spiderbook cofounder Aman Naimat highlights some surprising results from the analysis, such as: The relatively small number of companies using big data in production Industries that have embraced big data the most—and the least The amount of money spent on various big data use cases How many companies actually use “fast data” The results also reveal the geographical locations where companies have been quick to adopt big data, as well as the types of teams that use big data technology. In addition, Naimat takes you through the analysis process with Spiderbook’s graph-based machine-learning model. The company analyzed billions of publicly available documents, canvassed more than 500,000 companies, and searched the entire business internet to compile the most comprehensive results possible.

Data-driven techniques have improved decision-making processes for people in industries such as finance and real estate. Yet, despite promising solutions that data analytics and artificial intelligence/machine learning (ML) tools can bring to healthcare, the industry remains largely unconvinced. In this O’Reilly report, you’ll explore the potential of—and impediments to—widespread adoption of AI and ML in the medical field. You’ll also learn how extensive government regulation and resistance from the medical community have so far stymied full-scale acceptance of sophisticated data analytics in healthcare. Through interviews with several professionals working at the intersection of medicine and data science, author Mike Barlow examines five areas where the application of AI/ML strategies can spur a beneficial revolution in healthcare: Identifying risks and interventions for healthcare management of entire populations Closing gaps in care by designing plans for individual patients Supporting customized self-care treatment plans and monitoring patient health in real time Optimizing healthcare processes through data analysis to improve care and reduce costs Helping doctors and patients choose proper medications, dosages, and promising surgical options