Aim/Purpose: The primary objective of this research is to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole. Background: The framework can help students grasp new mathematical applications by comparing them to a common reference model. Methodology: In this research, we measure the most frequent words used in a sample of Math and Computer Science books. We combine these words with those obtained in an earlier study, from which we constructed our original Computational Math scale. Contribution: The enhanced framework improves the Computational Math scale by integrating selected concepts from the field of Data Science. Findings: The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer implementations. Future Research: We want to empirically test our enhanced Applied and Computational Math framework in a classroom setting. Our goal is to measure how effective the use of this framework is in improving students’ understanding of newly introduced Math concepts.

Aim/Purpose: The growing complexity of the business environment and business processes as well as the Big Data phenomenon has an impact on every area of human activity nowadays. This new reality challenges the effectiveness of traditional narrowly oriented professional education. New areas of competences emerged as a synergy of multiple knowledge areas – transdisciplines. Informing Science and Data Science are just the first two such new areas we may identify as transdisciplines. Universities are facing the challenge to educate students for those new realities. Background: The purpose of the paper is to share the authors’ experience in designing curriculum for training bachelor students in Informing Science as a concentration within an Information Brokerage major, and a master program on Data Science. Methodology: Designing curriculum for transdisciplines requires diverse expertise obtained by both academia and industries and passed through several stages - identifying objectives, conceptualizing curriculum models, identifying content, and development pedagogical priorities. Contribution: Sharing our experience acquired in designing transdiscipline programs will contribute to a transition from a narrow professional education towards addressing 21st-century challenges. Findings: Analytical skills, combined with training in all categories of so-called “soft skills”, are essential in preparing students for a successful career in a transdiciplinary area of activities. Recommendations for Practitioners: Establishing a working environment encouraging not only sharing but close cooperation is essential nowadays. Recommendations for Researchers: There are two aspects of training professionals capable of succeeding in a transdisciplinary environment: encouraging mutual respect and developing out-of-box thinking. Impact on Society: The transition of higher education in a way to meet current challenges. Future Research The next steps in this research are to collect feedback regarding the professional careers of students graduating in these two programs and to adjust the curriculum accordingly.

Improving equity in data science, edited by Colby Tofel-Grehl and Emmanuel Schanzer, is a thought-provoking exploration of how data science education can be transformed to foster equity, especially within K-16 classrooms. The editors advocate for redefining

Leading women data scientists and AI leaders talk about fostering more female candidates in the industry and navigating the challenges associated with it.

Procter & Gamble (P&G) is using advanced data science algorithms to help over 20 lakh kirana stores in the country through a special initiative called ‘Smart Basket’

Sai Sharanya Nalla recognized as an AI expert with over a decade of experience, including key roles at Nike, AWS and Amex.

STEM - Computer Science, Technology, Engineering, and Math Blog.

Miss Marianne M. Pelletier, MBA, is lauded for her work as the founder and managing director of Staupell Analytics Group

Felienne interviews Jeroen Janssens about data science, examining the basic concepts, as well as the skills and tools needed to be(come) a data scientist.

Catherine Nelson, author of the new O’Reilly book, Software Engineering for Data Scientists, discusses the collaboration between data scientists and software engineers -- an increasingly common pairing on machine learning and AI projects. Host Philip Winston speaks with Nelson about the role of a data scientist, the difference between running experiments in notebooks and building an automated pipeline for production, machine learning vs. AI, the typical pipeline steps for machine learning, and the role of software engineering in data science. Brought to you by IEEE Computer Society and IEEE Software magazine.

I have a close circle of clued-on/tech savvy friends whose views I take seriously. For the last few weeks, one of these friends has been sending me emails extolling the merits of something called IOTA – which calls itself as the next generation Blockchain.  At first, I thought of IOTA as yet another cryptocurrency. A [...]

  What if you could learn AI and Data Science based on knowledge you already know? You have an opportunity to accelerate your learning of AI in a unique way through this limited, early bird offer Here is a simple observation: The mathematical foundations of Data Science rest on four elements i.e. Linear Algebra, Probability [...]

The Penn State Institute for Computational and Data Sciences announced two new co-hires: Dana Calacci, assistant professor in the College of Information Sciences and Technology, and Enrico Casella, assistant professor of data science for animal systems in the College of Agricultural Sciences. 

Data science continues to be a pivotal force driving innovation across industries. From enhancing customer experiences to optimizing operational efficiencies, the role of data science is expanding, bringing with it new challenges and opportunities. This article explores the emerging trends and technologies that are shaping the future of data science [...]

The future of data science: 7 emerging trends and technologies to watch was published on SAS Voices by Iain Brown

I am gratified to see the continuing adoption of Forecast Value Added by organizations worldwide. FVA is an easy to understand and easy to apply approach for identifying bad practices in your forecasting process. And I'm particularly gratified to see coverage of FVA in two new books, which the authors [...]

The post Vandeput's Data Science for Supply Chain Forecasting (book excerpt) appeared first on The Business Forecasting Deal.

IEEE / Institute of Electrical and Electronics Engineers Incorporated

The executive programme aims to equip professionals with the essential skills and knowledge to excel in the fields of data science and AI

A passion for data is a prerequisite to pursue a career in data science — datasets should instantly inspire you to infer, analyse and visualise information.

Earn a Master of Professional Studies in Data Analytics online through Penn State World Campus – and you can add in-demand skills to your wheelhouse while you continue to work.

If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.

How can you keep your focus and drive during a global crisis? Take on a 90-day learning challenge for data science and check out this list of books and courses to follow.

Also: Should Data Scientists Model COVID19 and other Biological Events; 5 Papers on CNNs Every Data Scientist Should Read; 24 Best (and Free) Books To Understand Machine Learning; Mathematics for Machine Learning: The Free eBook; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

Also: Should Data Scientists Model COVID19 and other Biological Events; Learning during a crisis (Data Science 90-day learning challenge); Data Transformation: Standardization vs Normalization; DBSCAN Clustering Algorithm in Machine Learning; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier.

You walk down one aisle of the grocery store to get your favorite cereal. On the dairy aisle, someone sick from COVID-19 coughs. Did your decision to grab your cereal before your milk possibly keep you healthy? How can these unpredictable, near-random choices be included in complex models?

Also: Coronavirus COVID-19 Genome Analysis using Biopython; LSTM for time series prediction; A Concise Course in Statistical Inference: The Free eBook; Exploring the Impact of Geographic Information Systems

As the global economy dwindles, tech companies are hiring en masse. Now is the time to get yourself noticed as a Data Scientist and try to land your dream job.

5 cool Python libraries for Data Science; NLP Recipes: Best Practices and Examples; Deep Learning: The Free eBook; Demystifying the AI Infrastructure Stack; and more.

Tech platforms, telecom companies and governments need to come together at a time like this to work together towards addressing the balance between protecting individual privacy and data sharing that is critical to the public good.

Image-based tracking of medical instruments is an integral part of many surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the methods proposed still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on robustness and generalization capabilities of the methods. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all frames in the videos as well as instance-wise segmentation masks for surgical instruments in more than 10,000 individual frames. The data has successfully been used to organize international competitions in the scope of the Endoscopic Vision Challenges (EndoVis) 2017 and 2019.

Interested in learning how to create an online experiment that helps you better understand your business? This course can help you get up to speed. Instructor Monika Wahi shows learners without a background in experimental design how to build an A/B test for a web page, run the test, analyze the data, and make decisions based on the results of the test. Monika begins by explaining exactly what A/B testing is and under what circumstances it is useful. She then covers potential strategies for increasing conversion rates, as well as how to choose both A and B conditions for testing. Next, she explains how to define conversion rates and develop and document case definitions, conduct a baseline analysis in Excel and, based on the results of the analysis, design an A/B test. Plus, she demonstrates how to conduct a chi-square test in Excel and get a sample size estimate using G*Power.

In this two-part series, I explain how to find project management insights from a GitHub organization and how to create and publish tools to the Python Package Index.

Data is meaningless if you can't process it to gain insights. The field of data science gives you the tools and methods you need to process data sets effectively and so get the most from the data you collect. In this tutorial, you will Get the basics of machine learning, including data engineering, model learning, and operations.

IBM today announced the Integrated Analytics System, a new unified data system designed to give users fast, easy access to advanced data science capabilities and the ability to work with their data across private, public or hybrid cloud environments.

If you use data to make critical business decisions, this book is for you. Whether you’re a data analyst, research scientist, data engineer, ML engineer, data scientist, application developer, or systems developer, this guide helps you broaden your understanding of the modern data science stack, create your own machine learning pipelines, and deploy them to applications at production scale.

Junhui Cai, Avishai Mandelbaum, Chaitra H. Nagaraja, Haipeng Shen, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 669--691.

Abstract:
Statisticians are finding their place in the emerging field of data science. However, many issues considered “new” in data science have long histories in statistics. Examples of using statistical thinking are illustrated, which range from exploratory data analysis to measuring uncertainty to accommodating nonrandom samples. These examples are then applied to service networks, baseball predictions and official statistics.

The Institute for Computational and Data Sciences, in conjunction with several Penn State colleges, awarded more than $725,000 in seed grants to fund 32 new computational and data sciences projects. The 57 researchers involved in the awards represent 12 Penn State colleges and 31 academic departments.

If you think flying commercial is stressful, consider the air cargo industry. Unlike passenger flights, which are often booked and paid for months in advance, cargo shipments are typically booked just 10 days before the planned departure. And customers don’t have to pay until they drop off their shipments. However, even when customers create a Read article >

The post American Airlines Delivers the Goods, with Data Science Workstations appeared first on The Official NVIDIA Blog.

If you think flying commercial is stressful, consider the air cargo industry. Unlike passenger flights, which are often booked and paid for months in advance, cargo shipments are typically booked just 10 days before the planned departure. And customers don’t have to pay until they drop off their shipments. However, even when customers create a Read article >

The post American Airlines Delivers the Goods, with Data Science Workstations appeared first on The Official NVIDIA Blog.

A new global simulation model offers the first long-term look at how urbanization -- the growth of cities and towns -- will unfold in the coming decades. The research team projects the total amount of urban areas on Earth can grow anywhere from 1.8 to 5.9-fold by 2100, building approximately 618,000 square miles.

Data science firm Genomics Plc, which lays claim to “the world’s largest genomic database”, has welcomed Mitchell Harris to the company and its senior leadership team as its Chief Strategy Officer.

Joining from his previous role as Global Head, Emerging Business Lines at Abcam, Harris’ career has given him ample experience in commercial strategy and operations. Prior to his most recent role at Abcam, he acted as the company’s Head of Proteins Portfolio Commercial and Business Development.

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Following in the footsteps of an increasing number of biopharmas that want to use data to get more bang for their buck in R&D, J&J has promoted Najat Khan, Ph.D., to the role of chief data science officer.

Johnson & Johnson taps Khan for chief data role; Icon poaches AstraZeneca vet Buck as CMO; Intellia signs on Lebwohl as CMO.

Singapore : Springer, [2019]

Cham : Springer, c2019.

Cham : Springer, 2019.