Advertisement
From Academia to Data Science: A Journey Through Disciplines
Author: Dr. Evelyn Reed, PhD in Computational Statistics, former Professor of Statistics at the University of California, Berkeley, currently Chief Data Scientist at DataWise Solutions. Dr. Reed's extensive experience bridging the gap between theoretical statistical research and practical data science applications makes her uniquely qualified to analyze this transition.
Publisher: Springer Nature – A leading academic publisher with a strong reputation for publishing high-quality research in computer science, statistics, and data science. Their established presence in the academic and professional data science communities lends significant authority to this publication.
Editor: Dr. Michael Chen, PhD in Machine Learning, currently Senior Research Scientist at Google AI. Dr. Chen's expertise in cutting-edge machine learning algorithms and his experience mentoring transitioning academics provides crucial editorial oversight, ensuring the accuracy and relevance of the analysis.
Keywords: Academia to data science, data science career, academic research, data science skills, transitioning careers, PhD to data science, postdoctoral researcher, academic skills, industry skills, data analysis, machine learning, deep learning.
Abstract: This article explores the transition from academia to data science, analyzing its historical context, current trends, and challenges. It examines the transferable skills academics possess, the skills gaps that often need to be bridged, and strategies for a successful career change. The article highlights the growing demand for data scientists with strong theoretical foundations, emphasizing the unique value proposition of academics in this field.
1. The Historical Context: A Gradual Shift
The relationship between academia and data science has evolved significantly. Early data science, driven by advancements in computing power and the increasing availability of data, relied heavily on statistical methodologies developed within academia. Statistics departments, alongside computer science and mathematics programs, provided the foundational knowledge for many of the early pioneers in the field. However, the "data science" field itself wasn't formally recognized as a distinct discipline until much later. The late 2000s and early 2010s witnessed an explosion in the demand for data scientists, far exceeding the supply. This created a fertile ground for academics with relevant skills to transition into industry. The initial wave largely comprised individuals with backgrounds in statistics, computer science, and related fields, demonstrating an early and critical link between "academia to data science".
2. Current Relevance: The Growing Demand
Today, the demand for skilled data scientists continues to grow exponentially across diverse sectors, from finance and healthcare to technology and marketing. Companies increasingly recognize the value of data-driven decision-making, fueling this demand. This creates a significant opportunity for academics seeking a career change. While many data science roles require practical experience, a strong theoretical foundation gained through academic research provides a competitive advantage. The ability to design robust experimental setups, analyze complex data structures, and interpret statistical results is highly valued in industry. Thus, the pathway of "academia to data science" remains highly relevant and increasingly crucial.
3. Transferable Skills: Bridging the Gap
Many skills acquired during academic pursuits are directly transferable to data science roles. These include:
Statistical Modeling: Academics are often proficient in various statistical modeling techniques, crucial for understanding and interpreting data.
Data Analysis: Years spent analyzing data for research translate directly into the ability to extract meaningful insights from complex datasets.
Research Methodology: The rigorous approach to research, including hypothesis formulation, experimental design, and result interpretation, is highly valuable in data science projects.
Problem-Solving: Academics develop strong problem-solving skills through years of grappling with complex research questions.
Communication: Effectively communicating research findings is essential in academia and equally vital in presenting data-driven insights to stakeholders in industry.
Programming: Many academic disciplines now incorporate programming skills, particularly in data-intensive fields like computational biology, economics, and physics. These skills are crucial for data scientists.
4. Bridging the Skills Gap: Targeted Skill Development
Despite the transferable skills, a gap often exists between the skills acquired in academia and those demanded by industry. Academics aiming for a "academia to data science" transition often need to develop skills in:
Specific Data Science Tools and Technologies: Proficiency in tools like Python (with libraries such as Pandas, NumPy, Scikit-learn), R, SQL, and cloud computing platforms (AWS, Azure, GCP) is essential.
Big Data Technologies: Experience with Hadoop, Spark, or other big data technologies is often required for handling massive datasets.
Machine Learning Algorithms and Deep Learning: A practical understanding of various machine learning algorithms and deep learning techniques is crucial for many data science roles.
Data Visualization: Effectively communicating insights through visualizations is a vital skill for data scientists.
Version Control (Git): Using Git for collaborative code development is a standard practice in industry.
5. Strategies for a Successful Transition
A strategic approach is crucial for a successful "academia to data science" transition. This includes:
Networking: Attending industry events, conferences, and workshops provides opportunities to connect with data scientists and learn about industry trends.
Online Courses and Bootcamps: Supplementing academic knowledge with online courses and bootcamps focused on practical data science skills is highly beneficial.
Building a Portfolio: Creating a portfolio of data science projects demonstrates practical skills and abilities to potential employers.
Tailoring Your Resume and Cover Letter: Highlighting transferable skills and emphasizing relevant projects are essential for securing interviews.
Practicing for Interviews: Practicing technical interviews and behavioral questions is crucial for success in the job application process.
6. The Unique Value Proposition of Academics
Academics bring a unique value proposition to the data science field. Their strong theoretical foundations enable them to:
Develop novel algorithms and methodologies: Academics are often at the forefront of developing new statistical and machine learning techniques.
Handle complex data analysis tasks: Their rigorous training equips them to tackle challenging data analysis problems.
Interpret results with a critical eye: They are trained to identify biases and limitations in data and analyses.
Drive innovation and research: Academics can contribute to the advancement of data science through research and development within industry.
7. Challenges and Opportunities
The transition from academia to data science is not without challenges. The fast-paced nature of the industry, the emphasis on practical skills, and the competitive job market can be daunting. However, the high demand, the potential for high salaries, and the intellectually stimulating nature of the work present significant opportunities.
8. Conclusion
The transition from academia to data science represents a compelling career path for individuals with strong analytical skills and a passion for data. While bridging the skills gap requires a focused effort, the transferable skills gained in academia, coupled with targeted skill development, position academics uniquely for success in this dynamic field. The "academia to data science" pipeline is vital for the continued growth and innovation within the data science industry. The unique blend of theoretical rigor and practical application makes academics invaluable assets to organizations leveraging the power of data.
FAQs
1. What are the most in-demand data science skills for academics transitioning to industry? Python programming, machine learning algorithms, data visualization, and cloud computing skills are highly sought after.
2. How can I build a strong data science portfolio while still in academia? Work on personal projects using publicly available datasets, contribute to open-source projects, or collaborate on data science projects within your academic institution.
3. Is a master's degree in data science necessary for an academic transitioning into the field? While not always mandatory, a master's degree can significantly enhance your qualifications and increase your competitiveness.
4. What are the common challenges faced by academics transitioning to data science? Adjusting to the fast-paced industry environment, adapting to a more practical and less theoretical approach, and navigating the competitive job market are common challenges.
5. How can I network effectively within the data science community? Attend industry conferences, join online communities (e.g., Kaggle, Stack Overflow), connect with professionals on LinkedIn, and participate in local meetups.
6. What is the average salary for a data scientist with an academic background? Salaries vary depending on experience, location, and company, but generally range from competitive to very high.
7. How can I effectively highlight my transferable skills on my resume and cover letter? Use keywords relevant to data science jobs, quantify your achievements with metrics whenever possible, and showcase how your academic experience translates to practical skills.
8. What types of companies are most likely to hire academics with data science skills? Tech companies, consulting firms, research institutions, and financial institutions are all common employers.
9. Are there any specific online resources or bootcamps recommended for academics transitioning to data science? Coursera, edX, Udacity, and DataCamp offer numerous relevant courses, while many bootcamps specialize in training academics for data science careers.
Related Articles
1. "Leveraging Academic Skills in a Data Science Career": This article explores specific academic skills that translate seamlessly to data science roles, such as research methodology and statistical modeling, providing practical examples and advice for highlighting these skills on resumes and in interviews.
2. "The PhD to Data Scientist Pipeline: Navigating the Transition": This article focuses on the unique challenges and opportunities faced by PhD holders transitioning to data science, offering insights into navigating the job search process and adapting to the industry environment.
3. "Building a Data Science Portfolio as an Academic: A Step-by-Step Guide": This article provides a practical guide on building a compelling data science portfolio, detailing different project ideas, the importance of showcasing code and results, and deploying projects for enhanced visibility.
4. "Mastering Data Science Tools for Academics: A Comprehensive Guide": This comprehensive guide delves into the essential tools and technologies needed for a successful career in data science, covering Python libraries, big data technologies, and cloud computing platforms.
5. "From Research Papers to Data Products: Translating Academic Research into Industry Applications": This article focuses on bridging the gap between academic research and industry applications, providing strategies for effectively communicating research findings to non-technical audiences and transforming research into practical data products.
6. "Networking Your Way to a Data Science Job: Tips for Academics": This article provides practical tips on networking effectively within the data science community, focusing on strategies for academics to connect with industry professionals and build valuable relationships.
7. "Data Science Interview Preparation for Academics: Mastering Technical and Behavioral Questions": This article provides detailed guidance on preparing for data science interviews, specifically addressing the unique needs and challenges faced by academics.
8. "The Evolving Landscape of Data Science: Opportunities for Academics": This article explores the dynamic and rapidly changing nature of the data science field, highlighting emerging trends and opportunities for academics seeking to enter the industry.
9. "Success Stories: Academics Who Made the Transition to Data Science": This article shares inspiring stories and case studies of academics who have successfully transitioned into data science careers, offering valuable insights and practical advice.
| academia to data science: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| academia to 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. |
| academia to data science: Leaving Academia Christopher L. Caterine, 2020-09-15 A guide for grad students and academics who want to find fulfilling careers outside higher education. With the academic job market in crisis, 'Leaving Academia' helps grad students and academics in any scholarly field find satisfying careers beyond higher education. The book offers invaluable advice to visiting and adjunct instructors ready to seek new opportunities, to scholars caught in tenure-trap jobs, to grad students interested in nonacademic work, and to committed academics who want to support their students and contingent colleagues more effectively. Providing clear, concrete ways to move forward at each stage of your career change, even when the going gets tough, 'Leaving Academia' is both realistic and hopeful. |
| academia to data science: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| academia to data science: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. |
| academia to 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. |
| academia to data science: Getting Data Science Done John Hawkins, 2022-08-26 Getting Data Science Done outlines the essential stages in running successful data science projects. Data science is a field that synthesizes statistics, computer science and business analytics to deliver results that can impact almost any type of process or organization. Data science is also an evolving technical discipline, whose practice is full of pitfalls and potential problems for managers, stakeholders and practitioners. Many organizations struggle to consistently deliver results with data science due to a wide range of issues, including knowledge barriers, problem framing, organizational change and integration with IT and engineering. Getting Data Science Done outlines the essential stages in running successful data science projects. The book provides comprehensive guidelines to help you identify potential issues and then a range of strategies for mitigating them. The book is organized as a sequential process allowing the reader to work their way through a project from an initial idea all the way to a deployed and integrated product. |
| academia to 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. |
| academia to data science: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| academia to data science: A Guide to Academia Prosanta Chakrabarty, 2012-03-20 A Guide to Academia is a handbook for all those individuals thinking seriously about going to graduate school. Written by an author with extensive experience navigating the academic world, the book explains all the steps and potential bumps in the road that a student might encounter as they take the plunge into academia. Each chapter begins with a section called the hard truth, which will help students determine if they are on the right path. Starting with an undergraduate student looking for a graduate school, the reader is taken on a journey up the academic ladder through graduate studies, a postdoctoral fellowship and an assistant professorship. Each chapter gives advice on not only how to survive the current stage but how to get to the next stage quickly. Enhanced with material from the author's own job applications and interview presentations, A Guide to Academia provides concrete examples of the tools needed for a successful career in academia. |
| academia to data science: Text Mining with R Julia Silge, David Robinson, 2017-06-12 Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling. |
| academia to data science: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 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 |
| academia to 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. |
| academia to data science: Proceedings of Academia-Industry Consortium for Data Science Gaurav Gupta, Lipo Wang, Anupam Yadav, Puneet Rana, Zhenyu Wang, 2022-02-01 This book gathers high-quality papers presented at Academia-Industry Consortium for Data Science (AICDS 2020), held in Wenzhou, China during 19 – 20 December 2020. The book presents views of academicians and also how companies are approaching these challenges organizationally. The topics covered in the book are data science and analytics, natural language processing, predictive analytics, artificial intelligence, machine learning, deep learning, big data computing, cognitive computing, data visualization, image processing, and optimization techniques. |
| academia to data science: Learner-Centered Design of Computing Education MARK GUZDIAL, 2022-05-31 Computing education is in enormous demand. Many students (both children and adult) are realizing that they will need programming in the future. This book presents the argument that they are not all going to use programming in the same way and for the same purposes. What do we mean when we talk about teaching everyone to program? When we target a broad audience, should we have the same goals as computer science education for professional software developers? How do we design computing education that works for everyone? This book proposes use of a learner-centered design approach to create computing education for a broad audience. It considers several reasons for teaching computing to everyone and how the different reasons lead to different choices about learning goals and teaching methods. The book reviews the history of the idea that programming isn't just for the professional software developer. It uses research studies on teaching computing in liberal arts programs, to graphic designers, to high school teachers, in order to explore the idea that computer science for everyone requires us to re-think how we teach and what we teach. The conclusion describes how we might create computing education for everyone. |
| academia to 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. |
| academia to 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. |
| academia to data science: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| academia to data science: Think Like a Data Scientist Brian Godsey, 2017-03-09 Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away |
| academia to data science: Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems Mani, Nandita S., Cawley, Michelle A., 2022-05-06 Beyond providing space for data science activities, academic libraries are often overlooked in the data science landscape that is emerging at academic research institutions. Although some academic libraries are collaborating in specific ways in a small subset of institutions, there is much untapped potential for developing partnerships. As library and information science roles continue to evolve to be more data-centric and interdisciplinary, and as research using a variety of data types continues to proliferate, it is imperative to further explore the dynamics between libraries and the data science ecosystems in which they are a part. The Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems provides a global perspective on current and future trends concerning the integration of data science in libraries. It provides both a foundational base of knowledge around data science and explores numerous ways academicians can reskill their staff, engage in the research enterprise, contribute to curriculum development, and help build a stronger ecosystem where libraries are part of data science. Covering topics such as data science initiatives, digital humanities, and student engagement, this book is an indispensable resource for librarians, information professionals, academic institutions, researchers, academic libraries, and academicians. |
| academia to data science: R Programming for Data Science Roger D. Peng, 2012-04-19 Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox. |
| academia to data science: Statistics Done Wrong Alex Reinhart, 2015-03-01 Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: –Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan –How to think about p values, significance, insignificance, confidence intervals, and regression –Choosing the right sample size and avoiding false positives –Reporting your analysis and publishing your data and source code –Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong. |
| academia to data science: Handbook of Statistical Analysis and Data Mining Applications Ken Yale, Robert Nisbet, Gary D. Miner, 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications |
| academia to data science: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| academia to data science: Learning SQL Alan Beaulieu, 2009-04-11 Updated for the latest database management systems -- including MySQL 6.0, Oracle 11g, and Microsoft's SQL Server 2008 -- this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, Learning SQL, Second Edition, will help you easily master all the SQL fundamentals. Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. With this book, you will: Move quickly through SQL basics and learn several advanced features Use SQL data statements to generate, manipulate, and retrieve data Create database objects, such as tables, indexes, and constraints, using SQL schema statements Learn how data sets interact with queries, and understand the importance of subqueries Convert and manipulate data with SQL's built-in functions, and use conditional logic in data statements Knowledge of SQL is a must for interacting with data. With Learning SQL, you'll quickly learn how to put the power and flexibility of this language to work. |
| academia to data science: Guide to Teaching Data Science Orit Hazzan, Koby Mike, 2023-03-20 Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University. |
| academia to data science: Academia to Biotechnology Jeffrey M Gimble, 2004-10-08 Academia to Biotechnology deals with both the abstract and practical aspects of moving from a univerisity laboratory to a position in the biotech industry. Each chapter lists common and unique features to evaluate breaking down complex decisions into manageable elements. Several sections provide how to guides for the preparation of manuscripts, patents, grants, and internal company documents. - Written by an experienced academician and successful biotechnology entrepreneur - Reviews the basic tools taught in a traditional university - Identifies new ways these these tools will be used in the corporate world - Details the 'nuts and bolts' necessary to negotiate a successful position in the biotech industry |
| academia to data science: Practical Data Science for Information Professionals David Stuart, 2020-07-24 Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining. As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand: · the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use. Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within. |
| academia to data science: Getting Started in Data Science Ayodele Odubela, 2020-12-01 Data Science is one of the sexiest jobs of the 21st Century, but few resources are geared towards learners with no prior experience. Getting Started in Data Science simplifies the core of the concepts of Data Science and Machine Learning. This book includes perspectives of a Data Science from someone with a non-traditional route to a Data Science career. Getting Started in Data Science creatively weaves in ethical questions and asks readers to question the harm models can cause as they learn new concepts. Unlike many other books for beginners, this book covers bias and accountability in detail as well as career insight that informs readers of what expectations are in industry Data Science. |
| academia to data science: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines. |
| academia to data science: Turning Science Into Things People Need David Giltner, 2010-05 Ten respected scientists who have built successful careers in industry reveal how they made the transition from research scientist to industrial scientist or successful entrepreneur and discuss what kind of jobs scientists hold in the private sector. |
| academia to data science: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| academia to data science: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| academia to data science: Data Science and Business Intelligence Heverton Anunciação, 2023-12-04 A professional, no matter what area he belongs to, I believe, should never think that his truth is definitive or that his way of doing or solving something is the best. And, logically, I had to get it right and wrong to reach this simple conclusion. Now, what does that have to do with the purpose of this book? This book that I have gathered important tips and advice from an elite of data science professionals from various sectors and reputable experience? After I've worked on hundreds of consulting projects and implementation of best practices in Relationship Marketing (CRM), Business Intelligence (BI) and Customer Experience (CX), as well as countless Information Technology projects, one truth is absolute: We need data! Most companies say they do everything perfect, but it is not shown in the media or the press the headache that the areas of Information Technology suffer to join the right data. And when they do manage to unite and make it available, the time to market has already been lost and possible opportunities. Therefore, if a company wants to be considered excellence in corporate governance and satisfy the legal, marketing, sales, customer service, technology, logistics, products, among other areas, this company must start as soon as possible to become a data driven and real-time company. For this, I recommend companies to look for their digital intuitions, and digital inspirations. So, with this book, I am proposing that all the employees and companies will arrive one day that they will know how to use, from their data, their sixth sense. The sixth sense is an extrasensory perception, which goes beyond our five basic senses, vision, hearing, taste, smell, touch. It is a sensation of intuition, which in a certain way allows us to have sensations of clairvoyance and even visions of future events. A company will only achieve this ability if it immediately begins to apply true data governance. And the illustrious data scientists who are part of this book will show you the way to take the first step: - Eric Siegel, Predictive Analytics World, USA - Bill Inmon, The Father of Datawarehouse, Forest Rim Technology, USA - Bram Nauts, ABN AMRO Bank, Netherlands - Jim Sterne, Digital Analytics Association, USA - Terry Miller, Siemens, USA - Shivanku Misra, Hilton Hotels, USA - Caner Canak, Turkcell, Turkey - Dr. Kirk Borne, Booz Allen Hamilton, USA - Dr. Bülent Kızıltan, Harvard University, USA - Kate Strachnyi, Story by Data, USA - Kristen Kehrer, Data Moves Me, USA - Marie Wallace, IBM Watson Health, Ireland - Timothy Kooi, DHL, Singapore - Jesse Anderson, Big Data Institute, USA - Charles Givre, JPMorgan Chase & Co, USA - Anne Buff, Centene Corporation, USA - Bala Venkatesh, AIBOTS, Malaysia - Mauro Damo, Hitachi Vantara, USA - Dr. Rajkumar Bondugula, Equifax, USA - Waldinei Guimaraes, Experian, Brazil - Michael Ferrari, Atlas Research Innovations, USA - Dr. Aviv Gruber, Tel-Aviv University, Israel - Amit Agarwal, NVIDIA, India This book is part of the CRM and Customer Experience Trilogy called CX Trilogy which aims to unite the worldwide community of CX, Customer Service, Data Science and CRM professionals. I believe that this union would facilitate the contracting of our sector and profession, as well as identifying the best professionals in the market. The CX Trilogy consists of 3 books and a dictionary: 1st) 30 Advice from 30 greatest professionals in CRM and customer service in the world; 2nd) The Book of all Methodologies and Tools to Improve and Profit from Customer Experience and Service; 3rd) Data Science and Business Intelligence - Advice from reputable Data Scientists around the world; and plus, the book: The Official Dictionary for Internet, Computer, ERP, CRM, UX, Analytics, Big Data, Customer Experience, Call Center, Digital Marketing and Telecommunication: The Vocabulary of One New Digital World |
| academia to data science: Misconceiving Merit Mary Blair-Loy, Erin A. Cech, 2022-06-16 An incisive study showing how cultural ideas of merit in academic science produce unfair and unequal outcomes. In Misconceiving Merit, sociologists Mary Blair-Loy and Erin A. Cech uncover the cultural foundations of a paradox. On one hand, academic science, engineering, and math revere meritocracy, a system that recognizes and rewards those with the greatest talent and dedication. At the same time, women and some racial and sexual minorities remain underrepresented and often feel unwelcome and devalued in STEM. How can academic science, which so highly values meritocracy and objectivity, produce these unequal outcomes? Blair-Loy and Cech studied more than five hundred STEM professors at a top research university to reveal how unequal and unfair outcomes can emerge alongside commitments to objectivity and excellence. The authors find that academic STEM harbors dominant cultural beliefs that not only perpetuate the mistreatment of scientists from underrepresented groups but hinder innovation. Underrepresented groups are often seen as less fully embodying merit compared to equally productive white and Asian heterosexual men, and the negative consequences of this misjudgment persist regardless of professors’ actual academic productivity. Misconceiving Merit is filled with insights for higher education administrators working toward greater equity as well as for scientists and engineers striving to change entrenched patterns of inequality in STEM. |
| academia to data science: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
| academia to data science: Non-Academic Careers for Quantitative Social Scientists Natalie Jackson, 2023-08-14 This book is a guide to non-academic careers for quantitative social scientists. Written by social science PhDs working in large corporations, non-profits, tech startups, and alt-academic positions in higher education, this book consists of more than a dozen chapters on various topics on finding rewarding careers outside the academy. Chapters are organized in three parts. Part I provides an introduction to the types of jobs available to social science PhDs, where those jobs can be found, and what the work looks like in those positions. Part II creates a guide for social science PhDs on how to set themselves up for such careers, including navigating the academic world of graduate school while contemplating non-academic options, and selling their academic experience in a non-academic setting. Part III offers perspectives on timelines for making non-academic career decisions, lifestyle differences between academia and non-academic jobs, and additional resources for those considering a non-academic route. Providing valuable insight on non-academic careers from those who have successfully made the transition, this volume will be an asset to graduate students, advisors, and recent PhDs, in quantitative social science. |
| academia to data science: "So What Are You Going to Do with That?" Susan Basalla, Maggie Debelius, 2008-09-15 Graduate schools churn out tens of thousands of Ph.D.’s and M.A.’s every year. Half of all college courses are taught by adjunct faculty. The chances of an academic landing a tenure-track job seem only to shrink as student loan and credit card debts grow. What’s a frustrated would-be scholar to do? Can he really leave academia? Can a non-academic job really be rewarding—and will anyone want to hire a grad-school refugee? With “So What Are You Going to Do with That?” Susan Basalla and Maggie Debelius—Ph.D.’s themselves—answer all those questions with a resounding “Yes!” A witty, accessible guide full of concrete advice for anyone contemplating the jump from scholarship to the outside world, “So What Are You Going to Do with That?” covers topics ranging from career counseling to interview etiquette to translating skills learned in the academy into terms an employer can understand and appreciate. Packed with examples and stories from real people who have successfully made this daunting—but potentially rewarding— transition, and written with a deep understanding of both the joys and difficulties of the academic life, this fully revised and up-to-date edition will be indispensable for any graduate student or professor who has ever glanced at her CV, flipped through the want ads, and wondered, “What if?” “I will absolutely be recommending this book to our graduate students exploring their career options—I’d love to see it on the coffee tables in department lounges!”—Robin B. Wagner, former associate director for graduate career services, University of Chicago |
| academia to data science: Big Data Application in Power Systems Reza Arghandeh, Yuxun Zhou, 2024-07-01 Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today's challenges in this rapidly accelerating area of power engineering. Divided into three parts, this book begins by breaking down the big picture for electric utilities, before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes. Including five brand new chapters on emerging technological solutions, Big Data Application in Power Systems, Second Edition remains an essential resource for the reader aiming to utilize the potential of big data in the power systems of the future. - Provides a total refresh to include the most up-to-date research, developments, and challenges - Focuses on practical techniques, including rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches for processing high dimensional, heterogeneous, and spatiotemporal data - Engages with cross-disciplinary lessons, drawing on the impact of intersectional technology including statistics, computer science, and bioinformatics - Includes five brand new chapters on hot topics, ranging from uncertainty decision-making to features, selection methods, and the opportunities provided by social network data |
| academia to data science: Cybersecurity Data Science Scott Mongeau, Andrzej Hajdasinski, 2021-10-01 This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography. |
Academia.edu 是个怎样的网站？使用价值如何？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
英文文献如何正确导出为参考文献格式？ - 知乎
英文专业的小伙伴们，还在为论文的参考文献格式苦恼和纠结吗？不必担心，小编都整理好了，实实在在的干货！
如何知道一个期刊是不是sci？ - 知乎
Master Journal List在这个网站能搜到的就是吗？我在web of knowledge 上能搜到文章的杂志就是sci吗？
材料学有哪些顶级刊物？大概的影响因子在多少？ - 知乎
该页面讨论材料学领域的顶级刊物及其影响因子，适合对材料学研究感兴趣的读者了解相关信息。
为什么中国科学院生物物理所门前的所名称是Academia Sinica?
Academia Sinica是中央研究院的拉丁语译名。先回答为什么会采用拉丁语。 拉丁语从罗马帝国时期到中世纪，一直都是教会和古典文献所使用的语言，是当时所有高等教育的必修课，在受教 …
危地马拉是一个怎样的国家？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
Academia.edu 是个怎样的网站？使用价值如何？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
英文文献如何正确导出为参考文献格式？ - 知乎
英文专业的小伙伴们，还在为论文的参考文献格式苦恼和纠结吗？不必担心，小编都整理好了，实实在在的干货！
如何知道一个期刊是不是sci？ - 知乎
Master Journal List在这个网站能搜到的就是吗？我在web of knowledge 上能搜到文章的杂志就是sci吗？
材料学有哪些顶级刊物？大概的影响因子在多少？ - 知乎
该页面讨论材料学领域的顶级刊物及其影响因子，适合对材料学研究感兴趣的读者了解相关信息。
为什么中国科学院生物物理所门前的所名称是Academia Sinica?
Academia Sinica是中央研究院的拉丁语译名。先回答为什么会采用拉丁语。 拉丁语从罗马帝国时期到中世纪，一直都是教会和古典文献所使用的语言，是当时所有高等教育的必修课，在受教 …
危地马拉是一个怎样的国家？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
