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| columbia university masters in data science: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| columbia university masters in data science: Recent Advances in Information Systems and Technologies Álvaro Rocha, Ana Maria Correia, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, 2017-03-28 This book presents a selection of papers from the 2017 World Conference on Information Systems and Technologies (WorldCIST'17), held between the 11st and 13th of April 2017 at Porto Santo Island, Madeira, Portugal. WorldCIST is a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges involved in modern Information Systems and Technologies research, together with technological developments and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Software and Systems Modeling; Software Systems, Architectures, Applications and Tools; Multimedia Systems and Applications; Computer Networks, Mobility and Pervasive Systems; Intelligent and Decision Support Systems; Big Data Analytics and Applications; Human–Computer Interaction; Ethics, Computers & Security; Health Informatics; Information Technologies in Education; and Information Technologies in Radiocommunications. |
| columbia university masters in data science: Data Scientist Diploma (master's level) - City of London College of Economics - 6 months - 100% online / self-paced City of London College of Economics, Overview This diploma course covers all aspects you need to know to become a successful Data Scientist. Content - Getting Started with Data Science - Data Analytic Thinking - Business Problems and Data Science Solutions - Introduction to Predictive Modeling: From Correlation to Supervised Segmentation - Fitting a Model to Data - Overfitting and Its Avoidance - Similarity, Neighbors, and Clusters Decision Analytic Thinking I: What Is a Good Model? - Visualizing Model Performance - Evidence and Probabilities - Representing and Mining Text - Decision Analytic Thinking II: Toward Analytical Engineering - Other Data Science Tasks and Techniques - Data Science and Business Strategy - Machine Learning: Learning from Data with Your Machine. - And much more Duration 6 months Assessment The assessment will take place on the basis of one assignment at the end of the course. Tell us when you feel ready to take the exam and we’ll send you the assignment questions. Study material The study material will be provided in separate files by email / download link. |
| columbia university masters in data science: Data Science in Context Alfred Z. Spector, Peter Norvig, Chris Wiggins, Jeannette M. Wing, 2022-10-20 Four leading experts convey the promise of data science and examine challenges in achieving its benefits and mitigating some harms. |
| columbia university masters in data science: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| columbia university masters in data science: Practical Python Data Wrangling and Data Quality Susan E. McGregor, 2021-12-03 There are awesome discoveries to be made and valuable stories to be told in datasets--and this book will help you uncover them. Whether you already work with data or just want to understand its possibilities, the techniques and advice in this practical book will help you learn how to better clean, evaluate, and analyze data to generate meaningful insights and compelling visualizations. Through foundational concepts and worked examples, author Susan McGregor provides the concepts and tools you need to evaluate and analyze all kinds of data and communicate your findings effectively. This book provides a methodical, jargon-free way for practitioners of all levels to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sources Understand and use programming basics in Python to wrangle data at scale Organize, document, and structure your code using best practices Complete exercises either on your own machine or on the web Collect data from structured data files, web pages, and APIs Perform basic statistical analysis to make meaning from data sets Visualize and present data in clear and compelling ways. |
| columbia university masters in data science: Computational Statistical Methodologies and Modeling for Artificial Intelligence Priyanka Harjule, Azizur Rahman, Basant Agarwal, Vinita Tiwari, 2023-03-31 This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence |
| columbia university masters in data science: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| columbia university masters in data science: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering. |
| columbia university masters in data science: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| columbia university masters in data science: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| columbia university masters in data science: Fundamentals of Statistical Inference , 1977 |
| columbia university masters in data science: Leadership in Statistics and Data Science Amanda L. Golbeck, 2021-03-22 This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture. |
| columbia university masters in data science: Pandas for Everyone Daniel Y. Chen, 2022-11-24 Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set. New features to the second edition include: Extended coverage of plotting and the seaborn data visualization library Expanded examples and resources Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries Online bonus material on geopandas, Dask, and creating interactive graphics with Altair Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine data sets and handle missing data Reshape, tidy, and clean data sets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large data sets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” one Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning |
| columbia university masters in data science: Quarterly Review of Distance Education Michael Simonson, Charles Schlosser, 2018-11-01 The Quarterly Review of Distance Education is a rigorously refereed journal publishing articles, research briefs, reviews, and editorials dealing with the theories, research, and practices of distance education. The Quarterly Review publishes articles that utilize various methodologies that permit generalizable results which help guide the practice of the field of distance education in the public and private sectors. The Quarterly Review publishes full-length manuscripts as well as research briefs, editorials, reviews of programs and scholarly works, and columns. The Quarterly Review defines distance education as institutionally-based formal education in which the learning group is separated and interactive technologies are used to unite the learning group. |
| columbia university masters in data science: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| columbia university masters in data science: Turning Data into Insight with IBM Machine Learning for z/OS Samantha Buhler, Guanjun Cai, John Goodyear, Edrian Irizarry, Nora Kissari, Zhuo Ling, Nicholas Marion, Aleksandr Petrov, Junfei Shen, Wanting Wang, He Sheng Yang, Dai Yi, Xavier Yuen, Hao Zhang, IBM Redbooks, 2018-09-11 The exponential growth in data over the last decade coupled with a drastic drop in cost of storage has enabled organizations to amass a large amount of data. This vast data becomes the new natural resource that these organizations must tap in to innovate and stay ahead of the competition, and they must do so in a secure environment that protects the data throughout its lifecyle and data access in real time at any time. When it comes to security, nothing can rival IBM® Z, the multi-workload transactional platform that powers the core business processes of the majority of the Fortune 500 enterprises with unmatched security, availability, reliability, and scalability. With core transactions and data originating on IBM Z, it simply makes sense for analytics to exist and run on the same platform. For years, some businesses chose to move their sensitive data off IBM Z to platforms that include data lakes, Hadoop, and warehouses for analytics processing. However, the massive growth of digital data, the punishing cost of security exposures as well as the unprecedented demand for instant actionable intelligence from data in real time have convinced them to rethink that decision and, instead, embrace the strategy of data gravity for analytics. At the core of data gravity is the conviction that analytics must exist and run where the data resides. An IBM client eloquently compares this change in analytics strategy to a shift from moving the ocean to the boat to moving the boat to the ocean, where the boat is the analytics and the ocean is the data. IBM respects and invests heavily on data gravity because it recognizes the tremendous benefits that data gravity can deliver to you, including reduced cost and minimized security risks. IBM Machine Learning for z/OS® is one of the offerings that decidedly move analytics to Z where your mission-critical data resides. In the inherently secure Z environment, your machine learning scoring services can co-exist with your transactional applications and data, supporting high throughput and minimizing response time while delivering consistent service level agreements (SLAs). This book introduces Machine Learning for z/OS version 1.1.0 and describes its unique value proposition. It provides step-by-step guidance for you to get started with the program, including best practices for capacity planning, installation and configuration, administration and operation. Through a retail example, the book shows how you can use the versatile and intuitive web user interface to quickly train, build, evaluate, and deploy a model. Most importantly, it examines use cases across industries to illustrate how you can easily turn your massive data into valuable insights with Machine Learning for z/OS. |
| columbia university masters in data science: ECKM 2023 24th European Conference on Knowledge Management Vol 2 Alvaro Rosa, 2023-09-07 These proceedings represent the work of contributors to the 24th European Conference on Knowledge Management (ECKM 2023), hosted by Iscte – Instituto Universitário de Lisboa, Portugal on 7-8 September 2023. The Conference Chair is Prof Florinda Matos, and the Programme Chair is Prof Álvaro Rosa, both from Iscte Business School, Iscte – Instituto Universitário de Lisboa, Portugal. ECKM is now a well-established event on the academic research calendar and now in its 24th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them. The scope of papers will ensure an interesting two days. The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research. The opening keynote presentation is given by Professor Leif Edvinsson, on the topic of Intellectual Capital as a Missed Value. The second day of the conference will open with an address by Professor Noboru Konno from Tama Graduate School and Keio University, Japan who will talk about Society 5.0, Knowledge and Conceptual Capability, and Professor Jay Liebowitz, who will talk about Digital Transformation for the University of the Future. With an initial submission of 350 abstracts, after the double blind, peer review process there are 184 Academic research papers, 11 PhD research papers, 1 Masters Research paper, 4 Non-Academic papers and 11 work-in-progress papers published in these Conference Proceedings. These papers represent research from Australia, Austria, Brazil, Bulgaria, Canada, Chile, China, Colombia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, India, Iran, Iraq, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Kuwait, Latvia, Lithuania, Malaysia, México, Morocco, Netherlands, Norway, Palestine, Peru, Philippines, Poland, Portugal, Romania, South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Tunisia, UK, United Arab Emirates and the USA. |
| columbia university masters in data science: ECKM 2023 24th European Conference on Knowledge Managemen Vol 1 , 2023-09-07 |
| columbia university masters in data science: Getting a Coding Job For Dummies Nikhil Abraham, 2015-08-03 Your friendly guide to getting a job in coding Getting a Coding Job For Dummies explains how a coder works in (or out of) an organization, the key skills any job requires, the basics of the technologies a coding pro will encounter, and how to find formal or informal ways to build your skills. Plus, it paints a picture of the world a coder lives in, outlines how to build a resume to land a coding job, and so much more. Coding is one of the most in-demand skills in today's job market, yet there seems to be an ongoing deficit of candidates qualified to take these jobs. Getting a Coding Job For Dummies provides a road map for students, post-grads, career switchers, and anyone else interested in starting a career in coding. Inside this friendly guide, you'll find the steps needed to learn the hard and soft skills of coding—and the world of programming at large. Along the way, you'll set a clear career path based on your goals and discover the resources that can best help you build your coding skills to make you a suitable job candidate. Covers the breadth of job opportunities as a coder Includes tips on educational resources for coders and ways to build a positive reputation Shows you how to research potential employers and impress interviewers Offers access to online video, articles, and sample resume templates If you're interested in pursuing a job in coding, but don't know the best way to get there, Getting a Coding Job For Dummies is your compass! |
| columbia university masters in data science: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Segall, Richard S., Niu, Gao, 2022-01-07 During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines. |
| columbia university masters in data science: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| columbia university masters in data science: The Wiley Handbook of Cognition and Assessment Andre A. Rupp, Jacqueline P. Leighton, 2016-11-14 This state-of-the-art resource brings together the most innovative scholars and thinkers in the field of testing to capture the changing conceptual, methodological, and applied landscape of cognitively-grounded educational assessments. Offers a methodologically-rigorous review of cognitive and learning sciences models for testing purposes, as well as the latest statistical and technological know-how for designing, scoring, and interpreting results Written by an international team of contributors at the cutting-edge of cognitive psychology and educational measurement under the editorship of a research director at the Educational Testing Service and an esteemed professor of educational psychology at the University of Alberta as well as supported by an expert advisory board Covers conceptual frameworks, modern methodologies, and applied topics, in a style and at a level of technical detail that will appeal to a wide range of readers from both applied and scientific backgrounds Considers emerging topics in cognitively-grounded assessment, including applications of emerging socio-cognitive models, cognitive models for human and automated scoring, and various innovative virtual performance assessments |
| columbia university masters in data science: Nurses and Midwives in the Digital Age M. Honey, C. Ronquillo, T.-T. Lee, 2022-02-25 Nurses and midwives must increasingly work in multi-disciplinary teams and engage with patients to navigate the data and information central to today’s digitally-driven healthcare system. This book presents the proceedings of NI 2021, the 15th International Congress in Nursing Informatics. Originally planned to be held in 2020, the international year of the nurse and midwife, but postponed due to the SARS-CoV-2 pandemic, the conference, with the theme of nursing and midwifery in the digital age, was eventually held as a virtual event from 23 August to 2 September 2021, and the organizers made the decision to take advantage of its virtual nature and extend it to 9 days, with each day focusing on a different theme. This is reflected in the organization of the book, and the 212 papers included here, of which 67 are full papers with the remainder being workshop, panel, case study, and poster abstracts, are grouped into 9 sections. The papers were selected from a total of 437 submissions from more than 40 countries and cover: data analytics and the use of nursing data; digital health nursing workforce development; health policy, service delivery and ethics; informatics in clinical care; innovation and entrepreneurship in nursing; integrated and connected care; nursing information systems; patient participation and citizen involvement; and systems implementation and digital workplaces. Highlighting developments in nursing and midwifery and the way in which nurses and midwives are embracing the digital age, the book will be of interest to all those involved in healthcare today. |
| columbia university masters in data science: ECML PKDD 2020 Workshops Irena Koprinska, Michael Kamp, Annalisa Appice, Corrado Loglisci, Luiza Antonie, Albrecht Zimmermann, Riccardo Guidotti, Özlem Özgöbek, Rita P. Ribeiro, Ricard Gavaldà, João Gama, Linara Adilova, Yamuna Krishnamurthy, Pedro M. Ferreira, Donato Malerba, Ibéria Medeiros, Michelangelo Ceci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras, Peter Christen, Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Anna Monreale, Przemyslaw Biecek, Salvatore Rinzivillo, Benjamin Kille, Andreas Lommatzsch, Jon Atle Gulla, 2021-02-01 This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License. |
| columbia university masters in data science: Data Analytics and Management in Data Intensive Domains Alexander Elizarov, Boris Novikov, Sergey Stupnikov, 2020-07-13 This book constitutes the post-conference proceedings of the 21st International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2019, held in Kazan, Russia, in October 2019. The 11 revised full papers presented together with four invited papers were carefully reviewed and selected from 52 submissions. The papers are organized in the following topical sections: advanced data analysis methods; data infrastructures and integrated information systems; models, ontologies and applications; data analysis in astronomy; information extraction from text; distributed computing; data science for education. |
| columbia university masters in data science: An Introduction to Nursing Informatics, Evolution, and Innovation, 2nd Edition Susan M. Houston, Tina Dieckhaus, Bob Kircher, Michelle Lardner, 2018-11-09 Nursing informatics (NI) is the specialty that integrates nursing science with information management and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice. Nursing Informatics supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in a wide variety of roles and settings to achieve desired outcomes. This is accomplished through the use of information structures, information processes, and information technology. An Introduction to Nursing Informatics, Evolution and Innovation, 2nd Edition is the ideal gateway to all the professional possibilities this continuously evolving discipline has to offer. Describing the evolution of nursing informatics from its origins to current practice in today’s complex, diverse healthcare environment, this book offers the next generation of nurse informaticists an understanding of the discipline, best practices, and its scope of influence in healthcare. The book also explores Nursing Informatics as it is evolving into the future, including technology creation and implementation and the development of influential policies and best practices. Special features include descriptions of the ‘a day in the life’ from informatics nurses in multiple roles and fields of influence, including academia, research, clinical settings, the executive suite, consulting, and government, as well as an Appendix featuring case profiles. This new edition updates the content to better align with the current state of nursing informatics and expand on additional roles. New to this edition is a chapter providing tips and advice for those trying to find their first nursing informatics job or are changing their careers. Another new chapter covers healthcare analytics and how it fits into the nursing informatics role. An Introduction to Nursing Informatics, Evolution and Innovation, 2nd Edition is the ideal resource for nursing students and as a reference guide and pint of inspiration for nurses currently in the field. |
| columbia university masters in data science: 4D Hyperlocal Lucy Bullivant, 2017-03-13 4D Hyperlocal: A Cultural Tool Kit for the Open-source City The evolution of digital tools is revolutionising urban design, planning and community engagement. This is enabling a new ‘hyperlocal’ mode of design made possible by geolocation technologies and GPS-enabled mobile devices that support connectivity through open-source applications. Real-time analysis of environments and individuals’ input and feedback bring a new immediacy and responsiveness. Established linear design methods are being replaced by adaptable mapping processes, real-time data streams and experiential means, fostering more dynamic spatial analysis and public feedback. This shifts the emphasis in urban design from the creation of objects and spaces to collaboration with users, and from centralised to distributed participatory systems. Hyperlocal tools foster dynamic relational spatial analysis, making their deployment in urban and rural contexts challenged by transformation particularly significant. How can hyperlocal methods, solutions – including enterprise-driven uses of technology for bioclimatic design – and contexts influence each other and support the evolution of participatory architectural design? What issues, for example, arise from using real-time data to test scenarios and shape environments through 3D digital visualisation and simulation methods? What are the advantages of using GIS – with its integrative and visualising capacities and relational, flexible definition of scale – with GPS for multi-scalar mapping? Contributors: Saskia Beer, Moritz Behrens, John Bingham-Hall, Mark Burry, Will Gowland and Samantha Lee, Adam Greenfield, Usman Haque, Bess Krietemeyer, Laura Kurgan, Lev Manovich and Agustin Indaco, Claudia Pasquero and Marco Poletto, Raffaele Pe, José Luis de Vicente, Martijn de Waal, Michiel de Lange and Matthijs Bouw, Katharine Willis, and Alejandro Zaera-Polo. Featured architects and designers: AZPML, ecoLogicStudio, Foster + Partners, Interactive Design and Visualization Lab/Syracuse University Center of Excellence for Environmental Energy Systems, Software Studies Initiative/City University of New York (CUNY), Spatial Information Design Lab/Columbia University, Umbrellium, and Universal Assembly Unit. |
| columbia university masters in data science: 97 Things About Ethics Everyone in Data Science Should Know Bill Franks, 2020-08-06 Most of the high-profile cases of real or perceived unethical activity in data science arenâ??t matters of bad intent. Rather, they occur because the ethics simply arenâ??t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Conceptâ??Tim Wilson How to Approach Ethical Transparencyâ??Rado Kotorov Unbiased ≠ Fairâ??Doug Hague Rules and Rationalityâ??Christof Wolf Brenner The Truth About AI Biasâ??Cassie Kozyrkov Cautionary Ethics Talesâ??Sherrill Hayes Fairness in the Age of Algorithmsâ??Anna Jacobson The Ethical Data Storytellerâ??Brent Dykes Introducing Ethicizeâ?¢, the Fully AI-Driven Cloud-Based Ethics Solution!â??Brian Oâ??Neill Be Careful with Decisions of the Heartâ??Hugh Watson Understanding Passive Versus Proactive Ethicsâ??Bill Schmarzo |
| columbia university masters in data science: SQL for Data Analytics Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston, 2022-08-29 Take your first steps to becoming a fully qualified data analyst by learning how to explore complex datasets Key Features Master each concept through practical exercises and activities Discover various statistical techniques to analyze your data Implement everything you've learned on a real-world case study to uncover valuable insights Book Description Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional. What you will learn Use SQL to clean, prepare, and combine different datasets Aggregate basic statistics using GROUP BY clauses Perform advanced statistical calculations using a WINDOW function Import data into a database to combine with other tables Export SQL query results into various sources Analyze special data types in SQL, including geospatial, date/time, and JSON data Optimize queries and automate tasks Think about data problems and find answers using SQL Who this book is for If you're a database engineer looking to transition into analytics or a backend engineer who wants to develop a deeper understanding of production data and gain practical SQL knowledge, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL. Basic familiarity with SQL (such as basic SELECT, WHERE, and GROUP BY clauses) as well as a good understanding of linear algebra, statistics, and PostgreSQL 14 are necessary to make the most of this SQL data analytics book. |
| columbia university masters in data science: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| columbia university masters in data science: ChatGPT and Global Higher Education: Using Artificial Intelligence in Teaching and Learning , 2024-04-03 ChatGPT and Global Higher Education: Using Artificial Intelligence in Teaching and Learning |
| columbia university masters in data science: Demystifying Artificial intelligence Prashant Kikani, 2021-01-05 Learn AI & Machine Learning from the first principles. KEY FEATURESÊÊ _ Explore how different industries are using AI and ML for diverse use-cases. _ Learn core concepts of Data Science, Machine Learning, Deep Learning and NLP in an easy and intuitive manner. _ Cutting-edge coverage on use of ML for business products and services. _ Explore how different companies are monetizing AI and ML technologies. _ Learn how you can start your own journey in the AI field from scratch. DESCRIPTION AI and machine learning (ML) are probably the most fascinating technologies of the 21st century. AI is literally in every industry now. From medical to climate change, education to sport, finance to entertainment, AI is disrupting every industry as we know. So, the basic knowledge of AI/ML becomes mandatory for everyone. This book is your first step to start the journey in this field. Along with basic concepts of fields, like machine learning, deep learning and NLP, we will also explore how big companies are using these technologies to deliver greater user experience and earning millions of dollars in profit. Also, we will see how the owners of small- or medium-sized businesses can leverage and integrate these technologies with their products and services. Leveraging AI and ML can become that competitive moat which can differentiate the product from others. In this book, you will learn the root concepts of AI/ML and how these inanimate machines can actually become smarter than the humans at a few tasks, and how companies are using AI and how you can leverage AI to earn profits. WHAT YOU WILL LEARN Ê _ Core concepts of data science, machine learning, deep learning and NLP in simple and intuitive words. _ How you can leverage and integrate AI technologies in your business to differentiate your product in the market. _ The limitations of traditional non-tech businesses and how AI can bridge those gaps to increase revenues and decrease costs. _ How AI can help companies in launching new products, improving existing ones and automating mundane processes. _ Explore how big tech companies are using AI to automate different tasks and providing unique product experiences to their users. WHO THIS BOOK IS FORÊÊ This book is for anyone who is curious about this fascinating technology and how it really works at its core. It is also beneficial to those who want to start their career in AI/ ML. TABLE OF CONTENTSÊ 1. Introduction 2. Going deeper in ML concepts 3. Business perspective of AI 4. How to get started and pitfalls to avoid |
| columbia university masters in data science: Business Transformations in the Era of Digitalization Mezghani, Karim, Aloulou, Wassim, 2019-01-22 In order to establish and maintain a successful company in the digital age, managers are digitally transforming their organizations to include such tools as disruptive technologies and digital data to improve performance and efficiencies. As these companies continue to adopt digital technologies to improve their businesses and create new revenues and value-producing opportunities, they must also be aware of the challenges digitalization can present. Business Transformations in the Era of Digitalization is a collection of innovative research on the latest trends, business opportunities, and challenges in the digitalization of businesses. Highlighting a range of topics including business-IT alignment, cloud computing, Internet of Things (IoT), business sustainability, small and medium-sized enterprises, and digital entrepreneurship, this book is ideally designed for managers, professionals, consultants, entrepreneurs, and researchers. |
| columbia university masters in data science: Social and Emotional Learning and Complex Skills Assessment Yuan 'Elle' Wang, Srećko Joksimović, Maria Ofelia Z. San Pedro, Jason D. Way, John Whitmer, 2022-08-24 In this book, we primarily focus on studies that provide objective, unobtrusive, and innovative measures (e.g., indirect measures, content analysis, or analysis of trace data) of SEL skills (e.g., collaboration, creativity, persistence), relying primarily on learning analytics methods and approaches that would potentially allow for expanding the assessment of SEL skills and competencies at scale. What makes the position of learning analytics pivotal in this endeavor to redefine measurement of SEL skills are constant changes and advancements in learning environments and the quality and quantity of data collected about learners and the process of learning. Contemporary learning environments that utilize virtual and augmented reality to enhance learning opportunities accommodate for designing tasks and activities that allow learners to elicit behaviors (either in face-to-face or online context) not being captured in traditional educational settings. Novel insights provided in the book span across diverse types of learning contexts and learner populations. Specifically, the book addresses relevant and emerging theories and frameworks (in various disciplines such as education, psychology, or workforce) that inform assessments of SEL skills and competencies. In so doing, the book maps the landscape of the novel learning analytics methods and approaches, along with their application in the SEL assessment for K-12 learners as well as adult learners. Critical to the notion of the SEL assessment are data sources. In that sense, the book outlines where and how data related to learners' 21st century skills and competencies can be measured and collected. Linking theory to data, the book further discusses tools and methods that are being used to operationalize SEL and link relevant skills and competencies with cognitive assessment. Finally, the book addresses aspects of generalizability and applicability, showing promising approaches for translating research findings into actionable insights that would inform various stakeholders (e.g., learners, instructors, administrators, policy makers). |
| columbia university masters in data science: Strategic Social Media as Activism Adrienne A. Wallace, Regina Luttrell, 2023-08-25 Drawing on a range of theoretical and empirical perspectives, this volume examines the roles strategic communications play in creating social media messaging campaigns designed to engage in digital activism. As social activism and engagement continue to rise, individuals have an opportunity to use their agency as creators and consumers to explore issues of identity, diversity, justice, and action through digital activism. This edited volume situates activism and social justice historically and draws parallels to the work of activists in today’s social movements such as modern-day feminism, Black Lives Matter, #MeToo, Missing Murdered Indigenous Women, and We Are All Khaled Said. Each chapter adds an additional filter of nuance, building a complete account of mounting issues through social media movements and at the same time scaffolding the complicated nature of digital collective action. The book will be a useful supplement to courses in public relations, journalism, social media, sociology, political science, diversity, digital activism, and mass communication at both the undergraduate and graduate level. |
| columbia university masters in data science: AI FOR ABSOLUTE BEGINNERS Oliver Theobald, 2024 |
| columbia university masters in data science: A Field Guide to Grad School Jessica McCrory Calarco, 2020-08-25 Introduction -- Choosing a program -- Building your team -- Deciphering academic jargon -- Reading and writing about other people's research -- Staying on track in your program -- Doing research and finding funding -- Writing about your research -- Publishing and promoting your work -- Talking about your research -- Going to conferences -- Navigating the job market -- Balancing teaching, research, service and life -- Conclusion. |
| columbia university masters in data science: Big Data Analytics in Bioinformatics and Healthcare Wang, Baoying, 2014-10-31 As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information. |
| columbia university masters in data science: The Oxford Handbook of Social Networks Ryan Light, James Moody, 2020-12-04 Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others-- |
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