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| data science work life balance: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
| data science work life balance: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-06 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 |
| data science work life balance: Work-Life Balance M. Joseph Sirgy, Dong-Jin Lee, 2023-01-31 Identifies a set of personal interventions that employees commonly use to increase their work-life balance and life satisfaction. |
| data science work life balance: 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. |
| data science work life balance: Fifty Challenging Problems in Probability with Solutions Frederick Mosteller, 2012-04-26 Remarkable puzzlers, graded in difficulty, illustrate elementary and advanced aspects of probability. These problems were selected for originality, general interest, or because they demonstrate valuable techniques. Also includes detailed solutions. |
| data science work life balance: Fundamentals of Data Science Mr.Desidi Narsimha Reddy, Lova Naga Babu Ramisetti, Mr.Harikrishna Pathipati, 2024-09-05 Mr.Desidi Narsimha Reddy, Data Consultant (Data Governance, Data Analytics: Enterprise Performance Management, AI & ML), Soniks consulting LLC, 101 E Park Blvd Suite 600, Plano, TX 75074, United States. Lova Naga Babu Ramisetti, EPM Consultant, Department of Information Technology, MiniSoft Empowering Techonolgy, 10333 Harwin Dr. #375e, Houston, TX 77036, USA. Mr.Harikrishna Pathipati, EPM Manager, Department of Information Technology, ITG Technologies, 10998 S Wilcrest Dr, Houston, TX 77099, USA. |
| data science work life balance: How's Life? 2020 Measuring Well-being OECD, 2020-03-09 How’s Life? charts whether life is getting better for people in 37 OECD countries and 4 partner countries. This fifth edition presents the latest evidence from an updated set of over 80 indicators, covering current well-being outcomes, inequalities, and resources for future well-being. |
| data science work life balance: The Data Science Handbook Field Cady, 2017-02-28 A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon. |
| data science work life balance: Data Science and Intelligent Applications Ketan Kotecha, Vincenzo Piuri, Hetalkumar N. Shah, Rajan Patel, 2020-06-17 This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike. |
| data science work life balance: Data Science and Productivity Analytics Vincent Charles, Juan Aparicio, Joe Zhu, 2020-05-23 This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others. Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data. Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis. |
| data science work life balance: Handbook of Research on Future of Work and Education: Implications for Curriculum Delivery and Work Design Ramlall, Sunil, Cross, Ted, Love, Michelle, 2021-10-08 Higher education has changed significantly over time. In particular, traditional face-to-face degrees are being revamped in a bid to ensure they stay relevant in the 21st century and are now offered online. The transition for many universities to online learning has been painful—only exacerbated by the COVID-19 pandemic, forcing many in-person students to join their virtual peers and professors to learn new technologies and techniques to educate. Moreover, work has also changed with little doubt as to the impact of digital communication, remote work, and societal change on the nature of work itself. There are arguments to be made for organizations to become more agile, flexible, entrepreneurial, and creative. As such, work and education are both traversing a path of immense changes, adapting to global trends and consumer preferences. The Handbook of Research on Future of Work and Education: Implications for Curriculum Delivery and Work Design is a comprehensive reference book that analyzes the realities of higher education today, strategies that ensure the success of academic institutions, and factors that lead to student success. In particular, the book addresses essentials of online learning, strategies to ensure the success of online degrees and courses, effective course development practices, key support mechanisms for students, and ensuring student success in online degree programs. Furthermore, the book addresses the future of work, preferences of employees, and how work can be re-designed to create further employee satisfaction, engagement, and increase productivity. In particular, the book covers insights that ensure that remote employees feel valued, included, and are being provided relevant support to thrive in their roles. Covering topics such as course development, motivating online learners, and virtual environments, this text is essential for academicians, faculty, researchers, and students globally. |
| data science work life balance: Cracking the Data Science Interview Leondra R. Gonzalez, Aaren Stubberfield, 2024-02-29 Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more Key Features Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learn Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency Who this book is for Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews. |
| data science work life balance: Fathers and Mothers: Dilemmas of the Work-Life Balance Margret Fine-Davis, Jeanne Fagnani, Dino Giovannini, Lis Højgaard, Hilary Clarke, 2004-03-03 At the risk of sounding frivolous, there is a good case to be made for the argument that women constitute the revolutionary force behind contemporary social and economic transformation. It is in large part the changing role of women that explains the new household structure, our altered demographic behaviour, the growth of the service economy and, as a consequence, the new dilemmas that the advanced societies face. Most European countries have failed to adapt adequately to the novel challenges and the result is an increasingly serious disequilibrium. Women explicitly desire economic independence and the societal collective, too, needs to maximise female employment. And yet, this runs up against severe incompatibility problems that then result in very low birth rates. Our aging societies need more kids, yet fertility levels are often only half of what citizens define as their desired number of children. No matter what happens in the next decade, we are doomed to have exceedingly small cohorts that, in turn, must shoulder the massive burden of supporting a retired baby-boom generation. Hence it is tantamount that tomorrow’s adults be maximally productive and, yet, the typical EU member state invests very little in its children and families. |
| data science work life balance: Data Professionals at Work Malathi Mahadevan, 2018-10-11 Enjoy reading interviews with more than two dozen data professionals to see a picture of what it’s like to work in the industry managing and analyzing data, helping you to know what it takes to move from your current expertise into one of the fastest growing areas of technology today. Data is the hottest word of the century, and data professionals are in high demand. You may already be a data professional such as a database administrator or business intelligence analyst. Or you may be one of the many people who want to work as a data professional, and are curious how to get there. Either way, this collection helps you understand how data professionals work, what makes them successful, and what they do to keep up. You’ll find interviews in this book with database administrators, database programmers, data architects, business intelligence professionals, and analytics professionals. Interviewees work across industry sectors ranging from healthcare and banking to finance and transportation and beyond. Each chapter illuminates a successful professional at the top of their game, who shares what helped them get to the top, and what skills and attitudes combine to make them successful in their respective fields. Interviewees in the book include: Mindy Curnutt, Julie Smith, Kenneth Fisher, Andy Leonard, Jes Borland, Kevin Feasel, Ginger Grant, Vicky Harp, Kendra Little, Jason Brimhall, Tim Costello, Andy Mallon, Steph Locke, Jonathan Stewart, Joseph Sack, John Q. Martin, John Morehouse, Kathi Kellenberger, Argenis Fernandez, Kirsten Benzel, Tracy Boggiano, Dave Walden, Matt Gordon, Jimmy May, Drew Furgiuele, Marlon Ribunal, and Joseph Fleming. All of them have been successful in their careers, and share their perspectives on working and succeeding in the field as data and database professionals. What You'll Learn Stand out as an outstanding professional in your area of data work by developing the right set of skills and attitudes that lead to success Avoid common mistakes and pitfalls, and recover from operational failures and bad technology decisions Understand current trends and best practices, and stay out in front as the field evolvesBreak into working with data through database administration, business intelligence, or any of the other career paths represented in this book Manage stress and develop a healthy work-life balance no matter which career path you decide upon Choose a suitable path for yourself from among the different career paths in working with data Who This Book Is For Database administrators and developers, database and business intelligence architects, consultants, and analytic professionals, as well as those intent on moving into one of those career paths. Aspiring data professionals and those in related technical fields who want to make a move toward managing or analyzing data on a full-time basis will find the book useful. Existing data professionals who want to be outstanding and successful at what they do will also appreciate the book's advice and guidance. |
| data science work life balance: An Introduction to Data Science: Everything About AI, ML and Big Data Rudra Tiwari, 2022-09-18 First Edition of this book is predominantly envisioned for students who want to redefine the way they think about artificial intelligence (AI) and Data Science. Therefore the book, which is organized as a assortment of essentially self-contained articles, comprises both general strategic considerations and some detailed sector-specific material. It shares visions into what it means to work with AI and how to do it more proficiently; how to use AI in detailed industries such as investment or insurance; how AI interrelates with other technologies such as blockchain. Rudra Tiwari |
| data science work life balance: Big Data, Cloud Computing, and Data Science Engineering Roger Lee, 2023-03-12 This book presents scientific results of the 7th IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2021) which was held on August 4-6, 2022 in Danang, Vietnam. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. All aspects (theory, applications, and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here in the results of the articles featured in this book. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 15 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science. |
| data science work life balance: Learn Data Science from Scratch Pratheerth Padman, 2024-02-15 Turn raw data into meaningful solutions KEY FEATURES ● Complete guide to master data science basics. ● Practical and hands-on examples in ML, deep learning, and NLP. ● Drive innovation and improve decision making through the power of data. DESCRIPTION Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions. This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision making. By the end of this book, you will be well-equipped with the essential skills and knowledge to navigate the exciting world of data science. You will be able to collect, analyze, and interpret data, build and evaluate machine learning models, and effectively communicate your findings, making you a valuable asset in any data-driven environment. WHAT YOU WILL LEARN ● Master key data science tools like Python, NumPy, Pandas, and more. ● Build a strong foundation in statistics and probability for data analysis. ● Learn and apply machine learning, from regression to deep learning. ● Expertise in NLP and recommender systems for advanced analytics. ● End-to-end data project from data collection to model deployment, with planning and execution. WHO THIS BOOK IS FOR This book is ideal for beginners with a basic understanding of programming, particularly in Python, and a foundational knowledge of mathematics. It is well-suited for aspiring data scientists and analysts. TABLE OF CONTENTS 1. Unraveling the Data Science Universe: An Introduction 2. Essential Python Libraries and Tools for Data Science 3. Statistics and Probability Essentials for Data Science 4. Data Mining Expedition: Web Scraping and Data Collection Techniques 5. Painting with Data: Exploration and Visualization 6. Data Alchemy: Cleaning and Preprocessing Raw Data 7. Machine Learning Magic: An Introduction to Predictive Modeling 8. Exploring Regression: Linear, Logistic, and Advanced Methods 9. Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes 10. Exploring Tree-Based Models: Decision Trees to Gradient Boosting 11. Support Vector Machines: Simplifying Complexity 12. Dimensionality Reduction: From PCA to Advanced Methods 13. Unlocking Unsupervised Learning 14. The Essence of Neural Networks and Deep Learning 15. Word Play: Text Analytics and Natural Language Processing 16. Crafting Recommender Systems 17. Data Storage Mastery: Databases and Efficient Data Management 18. Data Science in Action: A Comprehensive End-to-end Project |
| data science work life balance: Intelligent Computing and Innovation on Data Science Sheng-Lung Peng, Sun-Yuan Hsieh, Suseendran Gopalakrishnan, Balaganesh Duraisamy, 2021-09-27 This book gathers high-quality papers presented at 2nd International Conference on Technology Innovation and Data Sciences (ICTIDS 2021), organized by Lincoln University, Malaysia from 19 – 20 February 2021. It covers wide range of recent technologies like artificial intelligence and machine learning, big data and data sciences, Internet of Things (IoT), and IoT-based digital ecosystem. The book brings together works from researchers, scientists, engineers, scholars and students in the areas of engineering and technology, and provides an opportunity for the dissemination of original research results, new ideas, research and development, practical experiments, which concentrate on both theory and practices, for the benefit of common man. |
| data science work life balance: Advanced Methods in Statistics, Data Science and Related Applications Matilde Bini, |
| data science work life balance: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-28 A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook |
| data science work life balance: Foundations of Data Science Based Healthcare Internet of Things Parikshit N. Mahalle, Sheetal S. Sonawane, 2021-01-22 This book offers a basic understanding of the Internet of Things (IoT), its design issues and challenges for healthcare applications. It also provides details of the challenges of healthcare big data, role of big data in healthcare and techniques, and tools for IoT in healthcare. This book offers a strong foundation to a beginner. All technical details that include healthcare data collection unit, technologies and tools used for the big data analytics implementation are explained in a clear and organized format. |
| data science work life balance: Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies Murugan, Thangavel, E., Nirmala, 2023-09-21 Disruptive innovations are now propelling Industry 4.0 (I4.0) and presenting new opportunities for value generation in all major industry segments. I4.0 technologies' innovations in cybersecurity and data science provide smart apps and services with accurate real-time monitoring and control. Through enhanced access to real-time information, it also aims to increase overall effectiveness, lower costs, and increase the efficiency of people, processes, and technology. The Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies discusses the technological foundations of cybersecurity and data science within the scope of the I4.0 landscape and details the existing cybersecurity and data science innovations with I4.0 applications, as well as state-of-the-art solutions with regard to both academic research and practical implementations. Covering key topics such as data science, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, computer scientists, scholars, researchers, academicians, practitioners, instructors, and students. |
| data science work life balance: 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. |
| data science work life balance: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses |
| data science work life balance: Advances in Data Science and Intelligent Data Communication Technologies for COVID-19 Aboul-Ella Hassanien, Sally M. Elghamrawy, Ivan Zelinka, 2021-07-23 This book presents the emerging developments in intelligent computing, machine learning, and data mining. It also provides insights on communications, network technologies, and the Internet of things. It offers various insights on the role of the Internet of things against COVID-19 and its potential applications. It provides the latest cloud computing improvements and advanced computing and addresses data security and privacy to secure COVID-19 data. |
| data science work life balance: Data Driven Thomas C. Redman, 2008-09-22 Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the Data Doc, shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that. |
| data science work life balance: Data Science and Security Samiksha Shukla, |
| data science work life balance: IoT and Data Science in Engineering Management Fausto Pedro García Márquez, Isaac Segovia Ramírez, Pedro José Bernalte Sánchez, Alba Muñoz del Río, 2023-03-24 This book presents the selected research works from the 16th International Conference on Industrial Engineering and Industrial Management in 2022. The conference was promoted by ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización), organized by Ingenium Research Group at Universidad de Castilla-La Mancha, Spain, and it took place on July 7th and 8th, 2022, in Toledo, Spain. The book highlights some of the latest research advances and cutting-edge analyses of real-world case studies on Industrial Engineering and Industrial Management from a wide range of international contexts. It also identifies business applications and the latest findings and innovations in Operations Management and in Decision Sciences. |
| data science work life balance: 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. |
| data science work life balance: Work-Life Balance, Management Practices and Productivity Nicholas Bloom, Tobias Kretschmer, John Van Reenen, 2014 Many critics of free-market liberalism argue that higher product-market competition and the Anglo-Saxon management practices it stimulates increases productivity only at the expense of employees' work-life balance (WLB). The empirical basis of these claims is unclear. To address this issue we use an innovative survey tool to collect the first international data on management practices and work-life balance practices, surveying 732 medium sized manufacturing firms in the US, France, Germany and the UK. We find that WLB outcomes are significantly associated with better management, so that well run firms are both more productive and better for their employees. After controlling for management practices, however, we find no additional relationship between WLB and productivity. WLB practices are also not reduced by tougher competition, suggesting no deleterious effect of competition on employees' working environment. Finally, looking at multinationals we find that US subsidiaries in Europe adopt the superior management practices of their US parent firms but the local WLB practices of their European competitors. |
| data science work life balance: The Future of Open Data Pamela Robinson, Teresa Scassa, 2022-05-24 The Future of Open Data flows from a multi-year Social Sciences and Humanities Research Council (SSHRC) Partnership Grant project that set out to explore open government geospatial data from an interdisciplinary perspective. Researchers on the grant adopted a critical social science perspective grounded in the imperative that the research should be relevant to government and civil society partners in the field. This book builds on the knowledge developed during the course of the grant and asks the question, “What is the future of open data?” The contributors’ insights into the future of open data combine observations from five years of research about the Canadian open data community with a critical perspective on what could and should happen as open data efforts evolve. Each of the chapters in this book addresses different issues and each is grounded in distinct disciplinary or interdisciplinary perspectives. The opening chapter reflects on the origins of open data in Canada and how it has progressed to the present date, taking into account how the Indigenous data sovereignty movement intersects with open data. A series of chapters address some of the pitfalls and opportunities of open data and consider how the changing data context may impact sources of open data, limits on open data, and even liability for open data. Another group of chapters considers new landscapes for open data, including open data in the global South, the data priorities of local governments, and the emerging context for rural open data. |
| data science work life balance: 101+ Careers in Public Health Beth Seltzer, MD, MPH, Heather Krasna, MS, EdM, 2021-10-12 The public health landscape is one of the most rapidly growing and cutting-edge fields at the moment and, in the wake of the global COVID-19 pandemic, there has never been a more meaningful time to enter the field. This thoroughly updated and revised third edition of 101+ Careers in Public Health continues to act as a career guide both for students seeking a first job in the field of public health and for anyone seeking guidance on how to best navigate the next stages of an existing career. Discussing not only emerging career paths but also traditional and familiar job types in public health, this book offers comprehensive advice and practical tips. It includes a wide survey of career profiles, including careers closely involved with pandemic response, climate change, technology and data science, and social justice advocacy. This third edition continues to provide a clear introduction to the history of public health with detailed descriptions of the many educational pathways that lead to public health careers. The book explores more than 120 different jobs in public health, with complete job descriptions, educational requirements, and future outlooks in addition to public health profiles from working professionals in the field. Whether interested in positions in government, healthcare, non-governmental organizations, technology, research, academia, philanthropic organizations, global health, consulting, or other private sector companies, this exciting third edition of 101+ Careers in Public Health provides excellent career guidance and produces helpful self-reflection when deciding on a public health career path. Key Features: Provides an introduction to the important competencies, training, and requirements needed to secure job opportunities at different career stages Includes step-by-step advice on how to network, apply, and interview for the job that best matches your interests, complete with a sample resume and cover letter Presents 50 new interviews from early career, management, and leadership positions as well as job descriptions for 20 occupations new to this edition Expanded coverage on global health and related opportunities, in addition to jobs in data science and technology Offers career advice for entry-level candidates and also for anyone looking to change careers |
| data science work life balance: Employee Performance Management for Improved Workplace Motivation Rajapakshe, Wasantha, 2024-08-27 In the dynamic landscape of organizational management, the challenge of effectively evaluating and enhancing employee performance stands as a pivotal obstacle to maximizing workplace productivity and motivation. Traditional performance appraisal methods often fall short in providing meaningful insights into employees' contributions and fostering a culture of continuous improvement. This gap between outdated evaluation techniques and the evolving demands of the modern workforce presents a pressing dilemma for Human Resource Management professionals and organizational leaders worldwide. Employee Performance Management for Improved Workplace Motivation emerges as a definitive solution to this critical problem, offering a comprehensive guide to revolutionizing performance management systems. This book meticulously explores the intricacies of performance evaluation, from planning and monitoring to reviewing and rewarding. By integrating theoretical frameworks, practical case studies, and strategic insights, the book equips HR professionals, managers, and scholars with the tools and knowledge needed to implement effective performance management practices that drive employee motivation and organizational success. |
| data science work life balance: 101 Careers in Mathematics: Fourth Edition Deanna Haunsperger, Robert Thompson, 2019-09-24 What can you do with a degree in math? This book addresses this question with 125 career profiles written by people with degrees and backgrounds in mathematics. With job titles ranging from sports analyst to science writer to inventory specialist to CEO, the volume provides ample evidence that one really can do nearly anything with a degree in mathematics. These professionals share how their mathematical education shaped their career choices and how mathematics, or the skills acquired in a mathematics education, is used in their daily work. The degrees earned by the authors profiled here are a good mix of bachelors, masters, and PhDs. With 114 completely new profiles since the third edition, the careers featured within accurately reflect current trends in the job market. College mathematics faculty, high school teachers, and career counselors will all find this a useful resource. Career centers, mathematics departments, and student lounges should have a copy available for student browsing. In addition to the career profiles, the volume contains essays from career counseling professionals on the topics of job-searching, interviewing, and applying to graduate school. |
| data science work life balance: The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies Anthony Elliott, 2024-07-22 The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies examines the relationship of the social sciences to artificial intelligence, surveying the various convergences and divergences between science and technology studies on the one hand and identity transformations on the other. It provides representative coverage of all aspects of the AI revolution, from employment to education to military warfare, impacts on public policy and governance and the future of ethics. How is AI currently transforming social, economic, cultural and psychological processes? This handbook answers these questions by looking at recent developments in supercomputing, deep learning and neural networks, including such topics as AI mobile technology, social robotics, big data and digital research. It focuses especially on mechanisms of identity by defining AI as a new context for self-exploration and social relations and analyzing phenomena such as race, ethnicity and gender politics in human-machine interfaces. |
| data science work life balance: Interdisciplinary Research in Technology and Management Satyajit Chakrabarti, Rintu Nath, Pradipta Kumar Banerji, Sujit Datta, Sanghamitra Poddar, Malay Gangopadhyaya, 2021-09-14 The conference on ‘Interdisciplinary Research in Technology and Management” was a bold experiment in deviating from the traditional approach of conferences which focus on a specific topic or theme. By attempting to bring diverse inter-related topics on a common platform, the conference has sought to answer a long felt need and give a fillip to interdisciplinary research not only within the technology domain but across domains in the management field as well. The spectrum of topics covered in the research papers is too wide to be singled out for specific mention but it is noteworthy that these papers addressed many important and relevant concerns of the day. |
| data science work life balance: Work-Life Balance and Women's Entrepreneurship Claire Sophie Zerwas, 2019-10-17 This book offers a comprehensive overview of work-life balance in the context of women’s entrepreneurship, specifically focusing on the factors that influence this balance. Using thematic qualitative text analysis, it interprets semi-structured interviews with experts in the field of women’s entrepreneurship, and based on this, presents the “7M” model, which is composed of seven dimensions and the corresponding factors that influence the work-life balance of women entrepreneurs. It also provides an in-depth analysis of all seven dimensions and describes the specific role of each dimension, highlighting the fact that women entrepreneurs are a highly heterogeneous group and that their work-life balance results from a complex interplay of various inter-related factors. |
| data science work life balance: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data science work life balance: Data and AI Driving Smart Cities Pedro Ponce, Therese Peffer, Juana Isabel Mendez Garduno, Ursula Eicker, Arturo Molina, Troy McDaniel, Edgard D. Musafiri Mimo, Ramanunni Parakkal Menon, Kathryn Kaspar, Sadam Hussain, 2023-07-08 This book illustrates how the advanced technology developed for smart cities requires increasing interaction with citizens to motivate and incentive them. Megacities' needs have been encouraging for the creation of smart cities in which the needs of inhabitants are collected using virtualization and digitalization systems. On the other hand, machine learning algorithms have been implemented to provide better solutions for diverse areas in smart cities, such as transportation and health. Besides, conventional electric grids have transformed into smart grids, improving energy quality. Gamification, serious games, machine learning, dynamic interfaces, and social networks are some elements integrated holistically to provide novel solutions to design and develop smart cities. Also, this book presents in a friendly way the concept of social devices that are incorporated into smart homes and buildings. This book is used to understand and design smart cities where citizens are strongly interconnected so the demand response time can be reduced. |
| data science work life balance: Handbook of Computational Social Science, Volume 2 Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg, 2021-11-10 The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
A STUDY ON WORK-LIFE BALANCE IN WORKING FEMALE …
duties, affect married working female’s work-life balance. • To analyze the work-life balance crisis of married working women across their demographic distinctiveness such as age group, …
WORK LIFE BALANCE OF WORKING WOMEN: A LITERATURE …
IT sector and the factors that affect the work life balance of women employees are working hours, Job satisfaction, working condition etc. 9. Sushree Sangita Das and Dr. Shashi A. Mishra …
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International Journal of Multidisciplinary Research in Arts, Science and Technology (IJMRAST) (81) This research explores how work-life balance initiatives enhance firms' attractiveness to …
The Relationship Between Work Engagement and Work–Life …
relationship between work–life balance and work engagement, which we review in this article. We identify and synthesize the findings of 37 articles empirically investigating the relationship …
Teachers’ work-life balance: the effect of work-leisure …
Teachers’ work-life balance: the effect of work-leisure conflict on work-related outcomes Heetae Choa,b, Do Young Pyunc and Chee Keng John Wangb aDepartment of Sport Science, …
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Work Life Balance, Quality Work Life dan Work Engagement berpengaruh secara simultan terhadap Kinerja Pegawai Puskesmas Janti. 2. Work Life Balance secara secara parsial tidak …
Influence of Flexibility on Work Life Balance
Influence of Flexibility on Work Life Balance Nikolina Birimisa nxb9572@rit.edu Follow this and additional works at: https://repository.rit.edu/theses ... Requirements for the Degree of Master …
School and Work Balance: The Experiences of Working …
struggle to balance work, school, and family demands [16][17]. A research finding from an analysis showed that most working students were satisfied with their school and work life balance. …
Work-life balance initiatives’ impact on perceived
ABSTRACT Work-lifebalance(WLB)isbecomingatopicoffocusformanagement,employees,HR specialistsandwellnesscoordinatorsduetoitsassociationtothehealth,wellbeingand
Workaholism, Life Balance, and Well-Being: A …
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The work-life balance of Teachers: A review
The work-life balance of Teachers: A review. Anna Gigi Eloor and Dr. Sreekumar D Menon . ... The secondary data used came from various books, journals, and websites. Science Direct, …
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effect on their health. And finally, it explains the relation between work-life conflicts and self-health issues due to unbalancing work-life. Dr . G. Balamurugan and M. Sreeleka (2020)- Women …
The Impact of Work-Life Balance on Organizational …
The Impact of Work-Life Balance on Organizational Commitment Sulieman Ibraheem Shelash Al-Hawary, Maali M. Al-mzary, ... by collecting data by means of a questionnaire distributed to a …
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Work-Life Balance, Experience and Organizational …
engagement in work, family, and social life. Furthermore, this led to failure in creating a "balance" between different life domains and may have a negative impact on their health. Striking a work …
Impact of Work Life Balance on the Psychological
Journal of Psychology and Behavioral Science June 2020, Vol. 8, No. 1, pp. 8-19 ... analyse the data. The study found that there was a statistically significant difference in the work life balance ...
A Study About the Work - Life Balance of Women …
International Journal of Science and Research (IJSR) ISSN: 2319-7064 SJIF (2020): 7.803 Volume 10 Issue 10, October 2021 ... Work Life Balance , Women Employees Social Support …
A STUDY ON NEED OF EMPLOYEES WORK LIFE BALANCE …
DECLARATION I ANAGHA P (40410012) hereby declare that the Project Report entitled “STUDY ON NEED OF EMPLOYEES WORK LIFE BALANCE DURING THE PANDEMIC IN IT …
XJM Work-lifebalance-a systematicreview - Emerald Insight
Work-life balance and family support. Spouse support enables better WLB (Dumas and Perry-Smith, 2018). Family support positively impacted WLB, especially for dual-career …
Work-Life Balance: An Overview - ResearchGate
According to data collected by work-life balance compendium (2001), the absence of employees had doubled in the past decade due to work-life conflict. The workers who experience high …
Impact of Job Satisfaction On Work-Life Balance - REST …
work-life balance and job satisfaction, and the changes in job satisfaction influences the changes in work-life balance. Keywords: Job satisfaction, work-life balance, ... Primary and secondary …
Effect Of Emotional Intelligence on Employee Engagement …
involvement or commitment between life and work (Sirgy & Lee, 2017). Work-life balance can be said as a person's ability to make a balance between the demands and obligations in the …
Data Scientists in Software Teams: State of the Art and …
on data science work and how different types of data sci-entists differ in terms of educational background, tool us-age, topics that they work on, and types of data that they work with. This …
The Relationship between Remote Working and Work-life …
The data were gathered using a structured questionnaire for the three constructs remote working, social support, and work-life balance. ... work-life balance has a positive direction, and the …
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Faculty of Psychology Master Theses (Psychology of Science) 2024 Pengaruh Work-Life Balance dan Lingkungan Kerja terhadap Kinerja Karyawan Thania, Dinda Eva Universitas Sumatera …
Work-Life Balance, Job Satisfaction and Performance
Work-Life Balance, Job Satisfaction and Performance Among Millennial and Gen Z Employees: A Systematic Review ... Data collection is done by studying the literature, where data or sources …
Pengaruh Work-Life Balance dan Burnout terhadap Kinerja …
Pengaruh Work-Life Balance dan Burnout terhadap Kinerja Karyawan Gen Z di Kota Denpasar Ni Made Pradnya Dhaniswari a,1, Sudarnice b,2* a,1 Universitas Terbuka, Indonesia ...
The Relationship between Job Satisfaction, Work-Life …
Kehl (2012) in industry Week Magazine, work life balance is rank as number one while compensation falls into rank two. Some more, an employee who feel have a good work life …
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Work-life balance, as observed, refers to achieving an optimal equilibrium between an individual's personal and professional life, ... Scopus and RePEc search data bases. The relevant authors …
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A STUDY ON WORK LIFE BALANCE” OF EMPLOYEES - IJISSH
2. In this study all the work life balance practices and how to implement work life balance business strategy 4. OBJECTIVES OF THE STUDY 1. The first objective is the importance of the work …
Impact of Work Life Balance on Job Satisfaction and …
international journal of multidisciplinary sciences and engineering, vol. 5, no. 9, september 2014 [issn: 2045-7057] www.ijmse.org 24
CIPD Good Work Index 2022: survey report
The CIPD Good Work Index captures data on seven dimensions of good work, summarised in Table 1. The index includes both objective and subjective measures. ... 3 Work–life balance …
WORK LIFE BALANCE PADA MAHASISWA YANG BEKERJA
used is the work-life balance inventory scale. The data analysis used by the researcher was descriptive analysis using the Statistical Package for Social Scale (SPSS) version 21. …
The role of work life balance for organizational commitment
3694 Based on data in June and December there were 2 employees who did not go to work without information (alpha), the highest number of employees left in July was 21 employees …
A PROJECT REPORT ON WORK-LIFE BALANCE AND …
work-life balance of the employee and the well-being of employees and whether managers can manage the work-life balance and provide the appropriate conditions that help the employee. …
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Malaysia, workers are well-known to have the worst work-life balance in he old (A Sggle to Attain Work-Life Balance´, 2020) ih Kala Lm coming in he la anking o of 40 cities surveyed for holistic …
WORK- LIFE BALANCE: A LITERATURE REVIEW - IJSDR
2Professor & Principal, Poona College of Arts, Science & Commerce, Camp, Pune, India ... This article involves data collected from previous literature available on work life balance, job …
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WORK LIFE BALANCE OF WOMEN EMPLOYEES: A STUDY ON …
Work Life balance means the balance between professional life, family life and personal life. Commitment at work and work life and personal life are inter-connected and interdependent. …
Evaluating Work-Life Balance in the Department of Defense
Data from the Defense Manpower Data Center (DMDC) reveal that the number ... some of the dissatisfaction with work-life balance will be presented. For example, the ... Administrative …
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WORK-LIFE BALANCE, SELF-EFFICACY AND JOB SATISFACTION vi 2.6 Work-life Balance and Self-efficacy predict Job Satisfaction 18 2.7 Theoretical Framework 19 2.8 Conceptual …
Work-Life Balance and Role Conflict among Academic Staff …
burnout. Heavy workloads with time constraints cause pressure, making it difficult to balance work and non-work responsibilities and this may cause a failure to maintain work-life balance. …
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2.2.1 Work-life balance Work-life balance means the balance between working life and private life and how they relate to each other. A balance between work and private life reduces the risk of …
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Work Life Balance, and the eighth section considers Work Stress and Work Life Balance. Assistant Professor, Jayoti Vidyapeeth Women’s University, Jaipur, Rajasthan, India. 10 …
Review of Work-Life Balance Theories - ResearchGate
Keywords: Work-life Balance; WLB Concepts; Work-Life Balance Theories; Family-Work Reference to this paper should be made as follows: Bello, Z; Tanko, G.I. (2020). Review of …
A Study on Work Life Balance of College Students as a Part …
To know about the Demographic Profile of Part Time Students of Work Life Balance. 2. To analyze the difference between demographic profile and Work Life Balance of Part Time Work …
