Data Science Project Life Cycle

  data science project life cycle: Managing Data Science Kirill Dubovikov, 2019-11-12 Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.
  data science project life cycle: 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
  data science project life cycle: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-03-07 An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.
  data science project life cycle: Steps to Facilitate Principal-Investigator-Led Earth Science Missions National Research Council, Division on Engineering and Physical Sciences, Space Studies Board, Committee on Earth Studies, 2004-04-21 Principal-investigator (PI) Earth science missions are small, focused science projects involving relatively small spacecraft. The selected PI is responsible for the scientific and programmatic success of the entire project. A particular objective of PI-led missions has been to help develop university-based research capacity. Such missions, however, pose significant challenges that are beyond the capabilities of most universities to manage. To help NASA's Office of Earth Science determine how best to address these, the NRC carried out an assessment of key issues relevant to the success of university-based PI-led Earth observation missions. This report presents the result of that study. In particular, the report provides an analysis of opportunities to enhance such missions and recommendations about whether and, if so, how they should be used to build university-based research capabilities.
  data science project life cycle: 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.
  data science project life cycle: Guide to Intelligent Data Science Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo, 2020-08-06 Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.
  data science project life cycle: Big Data Fundamentals Thomas Erl, Wajid Khattak, Paul Buhler, 2015-12-29 “This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning
  data science project life cycle: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
  data science project life cycle: Reproducibility and Replicability in Science National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Board on Research Data and Information, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Reproducibility and Replicability in Science, 2019-10-20 One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.
  data science project life cycle: Comet for Data Science Angelica Lo Duca, Gideon Mendels, 2022-08-26 Gain the key knowledge and skills required to manage data science projects using Comet Key Features • Discover techniques to build, monitor, and optimize your data science projects • Move from prototyping to production using Comet and DevOps tools • Get to grips with the Comet experimentation platform Book Description This book provides concepts and practical use cases which can be used to quickly build, monitor, and optimize data science projects. Using Comet, you will learn how to manage almost every step of the data science process from data collection through to creating, deploying, and monitoring a machine learning model. The book starts by explaining the features of Comet, along with exploratory data analysis and model evaluation in Comet. You'll see how Comet gives you the freedom to choose from a selection of programming languages, depending on which is best suited to your needs. Next, you will focus on workspaces, projects, experiments, and models. You will also learn how to build a narrative from your data, using the features provided by Comet. Later, you will review the basic concepts behind DevOps and how to extend the GitLab DevOps platform with Comet, further enhancing your ability to deploy your data science projects. Finally, you will cover various use cases of Comet in machine learning, NLP, deep learning, and time series analysis, gaining hands-on experience with some of the most interesting and valuable data science techniques available. By the end of this book, you will be able to confidently build data science pipelines according to bespoke specifications and manage them through Comet. What you will learn • Prepare for your project with the right data • Understand the purposes of different machine learning algorithms • Get up and running with Comet to manage and monitor your pipelines • Understand how Comet works and how to get the most out of it • See how you can use Comet for machine learning • Discover how to integrate Comet with GitLab • Work with Comet for NLP, deep learning, and time series analysis Who this book is for This book is for anyone who has programming experience, and wants to learn how to manage and optimize a complete data science lifecycle using Comet and other DevOps platforms. Although an understanding of basic data science concepts and programming concepts is needed, no prior knowledge of Comet and DevOps is required.
  data science project life cycle: Data Science Parveen Kumari, 2024-03-02 Data science is the study of how to extract useful information from data for students, strategic planning, and other purposes by using cutting-edge analytics methods, and scientific principles. Data science combines a number of fields, such as information technology, preparing data, data mining, predictive analytics, machine learning, and data visualization, in addition to statistics, mathematics, and software development.
  data science project life cycle: 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 project life cycle: Intelligent Techniques for Data Science Rajendra Akerkar, Priti Srinivas Sajja, 2016-10-11 This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p> The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.
  data science project life cycle: Project Management Waterfall-Agile-It-Data Science Dr. Festus Elleh PhD PMP PMI-ACP, 2023-03-22 This book is intended to introduce learners to waterfall, agile, information technology, and data science project management methodologies. Readers will learn about the concepts, processes, tools, and techniques that are useful for executing projects in waterfall, agile information technology, and data science environments. The objective is for learners to become contributors to the field of project management and deploy a structured approach to managing projects. Learners who read this book will be able to think critically about the concepts and practices of project management and perform exceptionally well in the PMP and PMI-ACP examinations.
  data science project life cycle: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness.
  data science project life cycle: Business Intelligence Roadmap Larissa Terpeluk Moss, S. Atre, 2003 This software will enable the user to learn about business intelligence roadmap.
  data science project life cycle: 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 project life cycle: Guide to Intelligent Data Analysis Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, 2010-06-23 Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.
  data science project life cycle: The Beginner's Guide to Data Science Robert Ball, Brian Rague, 2022-11-15 This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered. Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in “big data,” leveraging database and data collection tools such as web scraping and text identification. This book is organized as 11 chapters, structured as independent treatments of the following crucial data science topics: Data gathering and acquisition techniques including data creation Managing, transforming, and organizing data to ultimately package the information into an accessible format ready for analysis Fundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurements Inferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recorded Metrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observations Recommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some manner Machine learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analytics Natural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate manner Time series analysis, techniques to examine and generate forecasts about the progress and evolution of data over time Data science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on data science and business intelligence projects as well as advanced-level students and researchers focused on data science, computer science, business and mathematics programs will benefit from this book.
  data science project life cycle: Learn Microsoft Fabric Arshad Ali, Bradley Schacht, 2024-02-29 Harness the power of Microsoft Fabric to develop data analytics solutions for various use cases guided by step-by-step instructions Key Features Explore Microsoft Fabric and its features through real-world examples Build data analytics solutions for lakehouses, data warehouses, real-time analytics, and data science Monitor, manage, and administer your Fabric platform and analytics system to ensure flexibility, performance, security, and control Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover the capabilities of Microsoft Fabric, the premier unified solution designed for the AI era, seamlessly combining data integration, OneLake, transformation, visualization, universal security, and a unified business model. This book provides an overview of Microsoft Fabric, its components, and the wider analytics landscape. In this book, you'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You’ll learn how to build end-to-end lakehouse and data warehouse solutions using the medallion architecture, unlock the real-time analytics, and implement machine learning and AI models. As you progress, you’ll build expertise in monitoring workloads and administering Fabric across tenants, capacities, and workspaces. The book also guides you step by step through enhancing security and governance practices in Microsoft Fabric and implementing CI/CD workflows with Azure DevOps or GitHub. Finally, you’ll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey. By the end of this book, you’ll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and mastery over the essential concepts and principles of Microsoft Fabric.What you will learn Get acquainted with the different services available in Microsoft Fabric Build end-to-end data analytics solution to scale and manage high performance Integrate data from different types of data sources Apply transformation with Spark, Notebook, and T-SQL Understand and implement real-time stream processing and data science capabilities Perform end-to-end processes for building data analytics solutions in the AI era Drive insights by leveraging Power BI for reporting and visualization Improve productivity with AI assistance and Copilot integration Who this book is for This book is for data professionals, including data analysts, data engineers, data scientists, data warehouse developers, ETL developers, business analysts, AI/ML professionals, software developers, and Chief Data Officers who want to build a future-ready data analytics solution for long-term success in the AI era. For PySpark and SQL students entering the data analytics field, this book offers a broad foundation for developing the skills to build end-to-end analytics systems for various use cases. Basic knowledge of SQL and Spark is assumed.
  data science project life cycle: Data Science Doug Rose, 2016-11-17 Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business. Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story. Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts: You should mine your own company for talent. You can’t change your organization by hiring a few data science superheroes. You should form small, agile-like data teams that focus on delivering valuable insights early and often. You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry. What Your Will Learn: Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization’s data Understand key data science terms and concepts Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering value Use sprints and storytelling to help your team stay on track and adapt to new knowledge Who This Book Is For Data science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists.
  data science project life cycle: Getting Started with Greenplum for Big Data Analytics Sunila Gollapudi, 2013-10-23 Standard tutorial-based approach.Getting Started with Greenplum for Big Data Analytics is great for data scientists and data analysts with a basic knowledge of Data Warehousing and Business Intelligence platforms who are new to Big Data and who are looking to get a good grounding in how to use the Greenplum Platform. It’s assumed that you will have some experience with database design and programming as well as be familiar with analytics tools like R and Weka.
  data science project life cycle: Effective Data Science Infrastructure Ville Tuulos, 2022-08-30 Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents 1 Introducing data science infrastructure 2 The toolchain of data science 3 Introducing Metaflow 4 Scaling with the compute layer 5 Practicing scalability and performance 6 Going to production 7 Processing data 8 Using and operating models 9 Machine learning with the full stack
  data science project life cycle: Practical Data Science with Hadoop and Spark Ofer Mendelevitch, Casey Stella, Douglas Eadline, 2016-12-08 The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language
  data science project life cycle: Azure Data Scientist Associate Certification Guide Andreas Botsikas, Michael Hlobil, 2021-12-03 Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with ease Key FeaturesCreate end-to-end machine learning training pipelines, with or without codeTrack experiment progress using the cloud-based MLflow-compatible process of Azure ML servicesOperationalize your machine learning models by creating batch and real-time endpointsBook Description The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam. What you will learnCreate a working environment for data science workloads on AzureRun data experiments using Azure Machine Learning servicesCreate training and inference pipelines using the designer or codeDiscover the best model for your dataset using Automated MLUse hyperparameter tuning to optimize trained modelsDeploy, use, and monitor models in productionInterpret the predictions of a trained modelWho this book is for This book is for developers who want to infuse their applications with AI capabilities and data scientists looking to scale their machine learning experiments in the Azure cloud. Basic knowledge of Python is needed to follow the code samples used in the book. Some experience in training machine learning models in Python using common frameworks like scikit-learn will help you understand the content more easily.
  data science project life cycle: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
  data science project life cycle: Big Data Analytics Venkat Ankam, 2016-09-28 A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science
  data science project life cycle: Hands-On Data Science with SQL Server 2017 Marek Chmel, Vladimír Mužný, 2018-11-29 Find, explore, and extract big data to transform into actionable insights Key FeaturesPerform end-to-end data analysis—from exploration to visualizationReal-world examples, tasks, and interview queries to be a proficient data scientistUnderstand how SQL is used for big data processing using HiveQL and SparkSQLBook Description SQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features. Hands-On Data Science with SQL Server 2017 starts with an overview of data science with SQL to understand the core tasks in data science. You will learn intermediate-to-advanced level concepts to perform analytical tasks on data using SQL Server. The book has a unique approach, covering best practices, tasks, and challenges to test your abilities at the end of each chapter. You will explore the ins and outs of performing various key tasks such as data collection, cleaning, manipulation, aggregations, and filtering techniques. As you make your way through the chapters, you will turn raw data into actionable insights by wrangling and extracting data from databases using T-SQL. You will get to grips with preparing and presenting data in a meaningful way, using Power BI to reveal hidden patterns. In the concluding chapters, you will work with SQL Server integration services to transform data into a useful format and delve into advanced examples covering machine learning concepts such as predictive analytics using real-world examples. By the end of this book, you will be in a position to handle the growing amounts of data and perform everyday activities that a data science professional performs. What you will learnUnderstand what data science is and how SQL Server is used for big data processingAnalyze incoming data with SQL queries and visualizationsCreate, train, and evaluate predictive modelsMake predictions using trained models and establish regular retraining coursesIncorporate data source querying into SQL ServerEnhance built-in T-SQL capabilities using SQLCLRVisualize data with Reporting Services, Power View, and Power BITransform data with R, Python, and AzureWho this book is for Hands-On Data Science with SQL Server 2017 is intended for data scientists, data analysts, and big data professionals who want to master their skills learning SQL and its applications. This book will be helpful even for beginners who want to build their career as data science professionals using the power of SQL Server 2017. Basic familiarity with SQL language will aid with understanding the concepts covered in this book.
  data science project life cycle: Life-Cycle of Structures and Infrastructure Systems Fabio Biondini, Dan M. Frangopol, 2023-06-28 Life-Cycle of Structures and Infrastructure Systems contains the lectures and papers presented at IALCCE 2023- The Eighth International Symposium on Life-Cycle Civil Engineering, held at Politecnico di Milano, Milan, Italy, 2-6 July, 2023. This book contains the full papers of 514 contributions presented at IALCCE 2023, including the Fazlur R. Khan Plenary Lecture, nine Keynote Lectures, and 504 technical papers from 45 countries. The papers cover recent advances and cutting-edge research in the field of life-cycle civil engineering, including emerging concepts and innovative applications related to life-cycle design, assessment, inspection, monitoring, repair, maintenance, rehabilitation, and management of structures and infrastructure systems under uncertainty. Major topics covered include life-cycle safety, reliability, risk, resilience and sustainability, life-cycle damaging processes, life-cycle design and assessment, life-cycle inspection and monitoring, life-cycle maintenance and management, life-cycle performance of special structures, life-cycle cost of structures and infrastructure systems, and life-cycle-oriented computational tools, among others. This Open Access Book provides both an up-to-date overview of the field of life-cycle civil engineering and significant contributions to the process of making more rational decisions to mitigate the life-cycle risk and improve the life-cycle reliability, resilience, and sustainability of structures and infrastructure systems exposed to multiple natural and human-made hazards in a changing climate. It will serve as a valuable reference to all concerned with life-cycle of civil engineering systems, including students, researchers, practicioners, consultants, contractors, decision makers, and representatives of managing bodies and public authorities from all branches of civil engineering.
  data science project life cycle: Executive Data Science Roger Peng, 2016-08-03 In this concise book you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You'll get a crash course in data science so that you'll be conversant in the field and understand your role as a leader. You'll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You'll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.
  data science project life cycle: Practitioner’s Guide to Data Science Hui Lin, Ming Li, 2023-05-23 This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!
  data science project life cycle: Capitalizing Data Science Mathangi Sri Ramachandran, 2022-12-03 Unlock the Potential of Data Science and Machine Learning to Your Business and Organization KEY FEATURES ● Includes today's most popular applications powered by data science and machine learning technology. ● A solid primer on the entire data science lifecycle, detailed with examples. ● An integrated approach to demonstrating the use of Image Processing, Natural Language Processing, and Neural Networks in business. DESCRIPTION Can you foresee how your company and its products will benefit from data science? How can the results of using AI and ML in business be tracked and questioned? Do questions like ‘how do you build a data science team?’ keep popping into your head? All these strategic concerns and challenges are addressed in this book. Firstly, the book explores the evolution of decision-making based on empirical evidence. The book then helps compare the data-supported era with the current data-led era. It also discusses how to successfully run a data science project, the lifecycle of a data science project, and what it looks like. The book dives fairly in-depth into various today's data-led applications, highlights example datasets, discusses obstacles, and explains machine learning models and algorithms intuitively. This book covers structural and organizational considerations for making a data science team. The book helps recommend the use of optimal data science organization structure based on the company's level of development. Finally, the book explains data science's effects on businesses by assisting technological leaders. WHAT YOU WILL LEARN ● Learn the entire data science lifecycle and become fluent in each phase. ● Discover the world of supervised and unsupervised learning applications and structured and unstructured datasets. ● Discuss NLP's function, its potential, and the application of well-known methods like BERT and GPT3. ● Explain practical applications like automatic captioning, machine translation, and emotion recognition. ● Provide a framework for evaluating your team's data science skills and resources. WHO THIS BOOK IS FOR Startups, investors, small businesses, product management teams, CxO and all developing businesses desiring to leverage a data science team to gain the most from this book. The book also discusses the potential of practical applications of machine learning and AI for the future of businesses in banking and e-commerce. TABLE OF CONTENTS 1. Data-Driven Decisions from Beginning to Now 2. Data Science Life Cycle —Part 1 3. Data Science Life Cycle —Part 2 4. Deep Dive into AI 5. Applying AI with Structured Data—Banking 6. Applying AI with Structured Data 7. Applying AI with Structured Data—On-Demand Deliveries 8. AI in Natural Language Processing 9. Bringing It All Together
  data science project life cycle: Veridical Data Science Bin Yu, Rebecca L. Barter, 2024-10-15 Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process Cultivates critical thinking throughout the entire data science life cycle Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners
  data science project life cycle: Essential PySpark for Scalable Data Analytics Sreeram Nudurupati, 2021-10-29 Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
  data science project life cycle: Data Storytelling with Altair and AI Angelica Lo Duca, 2024-09-24 Great data presentations tell a story. Learn how to organize, visualize, and present data using Python, generative AI, and the cutting-edge Altair data visualization toolkit. Take the fast track to amazing data presentations! Data Storytelling with Altair and AI introduces a stack of useful tools and tried-and-tested methodologies that will rapidly increase your productivity, streamline the visualization process, and leave your audience inspired. In Data Storytelling with Altair and AI you’ll discover: • Using Python Altair for data visualization • Using Generative AI tools for data storytelling • The main concepts of data storytelling • Building data stories with the DIKW pyramid approach • Transforming raw data into a data story Data Storytelling with Altair and AI teaches you how to turn raw data into effective, insightful data stories. You’ll learn exactly what goes into an effective data story, then combine your Python data skills with the Altair library and AI tools to rapidly create amazing visualizations. Your bosses and decision-makers will love your new presentations—and you’ll love how quick Generative AI makes the whole process! About the technology Every dataset tells a story. After you’ve cleaned, crunched, and organized the raw data, it’s your job to share its story in a way that connects with your audience. Python’s Altair data visualization library, combined with generative AI tools like Copilot and ChatGPT, provide an amazing toolbox for transforming numbers, code, text, and graphics into intuitive data presentations. About the book Data Storytelling with Altair and AI teaches you how to build enhanced data visualizations using these tools. The book uses hands-on examples to build powerful narratives that can inform, inspire, and motivate. It covers the Altair data visualization library, along with AI techniques like generating text with ChatGPT, creating images with DALL-E, and Python coding with Copilot. You’ll learn by practicing with each interesting data story, from tourist arrivals in Portugal to population growth in the USA to fake news, salmon aquaculture, and more. What's inside • The Data-Information-Knowledge-Wisdom (DIKW) pyramid • Publish data stories using Streamlit, Tableau, and Comet • Vega and Vega-Lite visualization grammar About the reader For data analysts and data scientists experienced with Python. No previous knowledge of Altair or Generative AI required. About the author Angelica Lo Duca is a researcher at the Institute of Informatics and Telematics of the National Research Council, Italy. The technical editor on this book was Ninoslav Cerkez. Table of Contents PART 1 1 Introducing data storytelling 2 Running your first data story in Altair and GitHub Copilot 3 Reviewing the basic concepts of Altair 4 Generative AI tools for data storytelling PART 2 5 Crafting a data story using the DIKW pyramid 6 From data to information: Extracting insights 7 From information to knowledge: Building textual context 8 From information to knowledge: Building the visual context 9 From knowledge to wisdom: Adding next steps PART 3 10 Common issues while using generative AI 11 Publishing the data story A Technical requirements B Python pandas DataFrameC Other chart types
  data science project life cycle: Data Science Concepts and Techniques with Applications Usman Qamar, Muhammad Summair Raza, 2023-04-02 This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. This book delivers a systematic, carefully thoughtful material on Data Science. from the Foreword by Witold Pedrycz, U Alberta, Canada.
  data science project life cycle: Win with Advanced Business Analytics Jean-Paul Isson, Jesse Harriott, 2012-09-25 Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. Provides the essential concept and framework to implement business analytics Written clearly for a nontechnical audience Filled with case studies across a variety of industries Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.
  data science project life cycle: Data Scientist Pocket Guide Mohamed Sabri, 2021-06-24 Discover one of the most complete dictionaries in data science. KEY FEATURES ● Simplified understanding of complex concepts, terms, terminologies, and techniques. ● Combined glossary of machine learning, mathematics, and statistics. ● Chronologically arranged A-Z keywords with brief description. DESCRIPTION This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. WHAT YOU WILL LEARN ● Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. ● Keep yourself technically strong and sound-minded during data science meetings. ● Strengthen your knowledge in the field of Big data and business intelligence. WHO THIS BOOK IS FOR This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. TABLE OF CONTENTS 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z
  data science project life cycle: Financial Data Analytics Sinem Derindere Köseoğlu, 2022-04-25 ​This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.
  data science project life cycle: Advances on Intelligent Computing and Data Science Faisal Saeed, Fathey Mohammed, Errais Mohammed, Tawfik Al-Hadhrami, Mohammed Al-Sarem, 2023-08-16 This book presents the papers included in the proceedings of the 3rd International Conference of Advanced Computing and Informatics (ICACin’22) that was held in Casablanca, Morocco, on October 15–16, 2022. A total of 98 papers were submitted to the conference, but only 60 papers were accepted and published in this book with an acceptance rate of 61%. The book presents several hot research topics which include artificial intelligence and data science, big data analytics, Internet of Things (IoT) and smart cities, information security, cloud computing and networking, and computational informatics.
The Data Life Cycle
Oct 4, 2019 · To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user.

Chapter 02 Process of Data Science Projects - GitHub Pages
•Generic process for data science projects with six phases •Discovery, data preparation, model planning, model building, communication of results, and operationalization

CRISP-DM for Data Science- V2 - Data Science Process Alliance
Published in 1999, CRISP-DM (CRoss Industry Standard Process for Data Mining (CRISP-DM) is the most popular framework for executing data science projects. It provides a natural …

Data Science Life Cycle, CRISP-DM Methodology - cuni.cz
General process of discovering knowledge in data through data mining, extraction of patterns, machine learning, statistics, and database systems. ...

What we Learned About the Data Science Life Cycle: Best …
•Services for exploration of open data sets that help users: •Linkand leverage multiple datasets •Access and search data using natural language, using examples and using analytics •Get …

Project Management for Data Science - NYU Stern
Life cycle for processes and the project Answers to these questions: How will objectives be achieved? How will change be monitored/controlled? How will con guration management be …

Data Science Project Life Cycle - ijsrd.com
Abstract—the setting up a successful Data Science capability is not so easy. It contains its own challenges. To overcome some of those challenges during the execution and implementation, …

12 Uing s a ta Da Science Life Cycle - Springer
You ve seen how difficult it is to get data science teams to work within a proj-ect management framework, so let s look at two of the life cycles commonly used in project management. A life …

The Data Life Cycle - PubPub
Data science is the study of extracting value from data. “Value” is subject to the interpretation by the end user and “extracting” represents the work done in all phases of the data life cycle (see …

Phases Of Data Science Life Cycle Copy - interactive.cornish.edu
improve your data science projects through the use of DevOps and ModelOps By the end of this book you will be well versed with various data science solutions and have gained practical …

231CS33 PROGRAMMING FOR DATA SCIENCE UNIT I …
Information Commons – Data Science Project Life Cycle. 1.1. Data Science: Data science is the domain of study that deals with vast volumes of data using modern tools and techniques, …

Lifecycle of machine learning models - Oracle
In this book, we break down how machine learning models are built into six steps: data access and collection, data preparation and exploration, model build and train, model evaluation, …

Data Science Methodologies: Current Challenges and Future …
We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information …

The Data Science Life Cycle: - Stodden
scribe the complete process of data sci-ence with the Data Science Life Cycle. This work extends research in the Data Life Cycle by focusing on the genera-tion of scientific findings, and …

Introduction to Data Science - Stellenbosch University
• The data science project life cycle and the different role players involved, • The aspects included in each of the data science project life cycle phases, • The technologies applicable to the data …

Data Life Cycle Models and Concepts CEOS Version 1
CEOS Data Life Cycle Models and Concepts CEOS.WGISS.DSIG.TN01 Issue 1.2 April 2012 Version 13.0 19 April, 2012 INTRODUCTION This is a compilation of data lifecycle models and …

Phases Of Data Science Life Cycle [PDF] - interactive.cornish.edu
Phases Of Data Science Life Cycle: Data Science for Beginners Prof John Smith,2018-12-12 DATA SCIENCE FOR BEGINNERS Introduction to Data Science Python Coding Application …

Current approaches for executing big data science projects—a …
RQ2: What are the most common approaches regarding how data science projects are organized, managed and coordinated? RQ3: What are the phases or activities in a data science project …

Data Life Cycle: Introduction, Definitions and Considerations
life cycle •Provenance concepts describe how domain concepts are related •Domain and provenance models should be independent, but aligned •Aligning with a well-supported …

Phases Of Data Science Life Cycle (book)
After understanding the practical applications of data science and artificial intelligence you ll see how to incorporate them into your solutions Next you will go through the data science project …

The Data Life Cycle
Oct 4, 2019 · To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user.

Chapter 02 Process of Data Science Projects - GitHub Pages
•Generic process for data science projects with six phases •Discovery, data preparation, model planning, model building, communication of results, and operationalization

CRISP-DM for Data Science- V2 - Data Science Process Alliance
Published in 1999, CRISP-DM (CRoss Industry Standard Process for Data Mining (CRISP-DM) is the most popular framework for executing data science projects. It provides a natural …

Data Science Life Cycle, CRISP-DM Methodology - cuni.cz
General process of discovering knowledge in data through data mining, extraction of patterns, machine learning, statistics, and database systems. ...

What we Learned About the Data Science Life Cycle: Best …
•Services for exploration of open data sets that help users: •Linkand leverage multiple datasets •Access and search data using natural language, using examples and using analytics •Get …

Project Management for Data Science - NYU Stern
Life cycle for processes and the project Answers to these questions: How will objectives be achieved? How will change be monitored/controlled? How will con guration management be …

Data Science Project Life Cycle - ijsrd.com
Abstract—the setting up a successful Data Science capability is not so easy. It contains its own challenges. To overcome some of those challenges during the execution and implementation, …

12 Uing s a ta Da Science Life Cycle - Springer
You ve seen how difficult it is to get data science teams to work within a proj-ect management framework, so let s look at two of the life cycles commonly used in project management. A life …

The Data Life Cycle - PubPub
Data science is the study of extracting value from data. “Value” is subject to the interpretation by the end user and “extracting” represents the work done in all phases of the data life cycle (see …

Phases Of Data Science Life Cycle Copy
improve your data science projects through the use of DevOps and ModelOps By the end of this book you will be well versed with various data science solutions and have gained practical …

231CS33 PROGRAMMING FOR DATA SCIENCE UNIT I …
Information Commons – Data Science Project Life Cycle. 1.1. Data Science: Data science is the domain of study that deals with vast volumes of data using modern tools and techniques, …

Lifecycle of machine learning models - Oracle
In this book, we break down how machine learning models are built into six steps: data access and collection, data preparation and exploration, model build and train, model evaluation, …

Data Science Methodologies: Current Challenges and Future …
We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information …

The Data Science Life Cycle: - Stodden
scribe the complete process of data sci-ence with the Data Science Life Cycle. This work extends research in the Data Life Cycle by focusing on the genera-tion of scientific findings, and …

Introduction to Data Science - Stellenbosch University
• The data science project life cycle and the different role players involved, • The aspects included in each of the data science project life cycle phases, • The technologies applicable to the data …

Data Life Cycle Models and Concepts CEOS Version 1
CEOS Data Life Cycle Models and Concepts CEOS.WGISS.DSIG.TN01 Issue 1.2 April 2012 Version 13.0 19 April, 2012 INTRODUCTION This is a compilation of data lifecycle models …

Phases Of Data Science Life Cycle [PDF]
Phases Of Data Science Life Cycle: Data Science for Beginners Prof John Smith,2018-12-12 DATA SCIENCE FOR BEGINNERS Introduction to Data Science Python Coding Application …

Current approaches for executing big data science …
RQ2: What are the most common approaches regarding how data science projects are organized, managed and coordinated? RQ3: What are the phases or activities in a data science project …

Data Life Cycle: Introduction, Definitions and Considerations
life cycle •Provenance concepts describe how domain concepts are related •Domain and provenance models should be independent, but aligned •Aligning with a well-supported …

Phases Of Data Science Life Cycle (book)
After understanding the practical applications of data science and artificial intelligence you ll see how to incorporate them into your solutions Next you will go through the data science project …