舉報(bào) 
會(huì)員
Building Analytics Teams
最新章節(jié)：
Index
InBuildingAnalyticsTeams,JohnK.Thompson,withhis30+yearsofexperienceandexpertise,illustratesthefundamentalconceptsofbuildingandmanagingahigh-performanceanalyticsteam,includingwhattodo,whotohire,projectstoundertake,andwhattoavoidinthejourneyofbuildingananalyticallysoundteam.Thecoreprocessesincreatinganeffectiveanalyticsteamandtheimportanceofthebusinessdecision-makinglifecycleareexploredtohelpachieveinitialandsustainablesuccess.Thebookdemonstratesthevarioustraitsofasuccessfulandhigh-performinganalyticsteamandthendelineatesthepathtoachievethiswithinsightsonthemindset,advancedanalyticsmodels,andpredictionsbasedondataanalytics.Italsoemphasizesthesignificanceofthemacroandmicroprocessesrequiredtoevolveinresponsetorapidlychangingbusinessneeds.Thebookdivesintothemethodsandpracticesofmanaging,developing,andleadingananalyticsteam.Onceyou'vebroughttheteamuptospeed,thebookexplainshowtogovernexecutiveexpectationsandselectwinningprojects.Bytheendofthisbook,youwillhaveacquiredtheknowledgetocreateaneffectivebusinessanalyticsteamanddevelopaproductionenvironmentthatdeliversongoingoperationalimprovementsforyourorganization.
目錄(129章)
倒序
- 封面
- 版權(quán)信息
- Why subscribe?
- In Praise of
- Foreword
- Contributors About the author
- About the reviewer
- Preface
- Introduction
- 1 An Overview of Successful and High-Performing Analytics Teams
- Introduction
- AI in the education system
- We are different
- The original sin
- The right home
- Ethics
- Summary
- Chapter 1 footnotes
- 2 Building an Analytics Team
- Organizational context and consideration
- Internships and co-op programs
- Diversity and inclusion
- Neuropersity
- Disciplinary action
- Labor market dynamics
- A fit to be found
- Evolved leadership is a requirement for success
- Continual learning and data literacy at the organizational level
- Defining a high-performing analytical team
- The general data science process
- Team architecture/structure options
- The implications of proprietary versus open source tools
- Summary
- Chapter 2 footnotes
- 3 Managing and Growing an Analytics Team
- Managerial focus and balance
- Sponsor and stakeholder management
- An open or fixed mindset?
- Productivity premium
- The rhythm of work
- Personal project portfolio
- Managing team dynamics
- The front end of the talent pipeline
- It takes a team
- Simply the best
- Organizational maxims
- Summary
- Chapter 3 footnotes
- 4 Leadership for Analytics Teams
- Artificial intelligence and leadership
- Traits of successful analytics leaders
- Building a supportive and engaged team
- Managing team cohesion
- Being the smartest person in the room
- Good (and bad) ideas can come from anywhere
- Emerging leadership roles – Chief Data Officer and Chief Analytics Officer
- Hiring the Chief Data Officer or Chief Analytics Officer – where to start?
- Summary
- Chapter 4 footnotes
- 5 Managing Executive Expectations
- You are not the only game in town
- Know what to say
- Know how to say it
- Shaping and directing the narrative
- Know before you go
- How many of us are out there?
- There is a proven path to success
- What are you hoping to accomplish?
- Outsourcing
- Elephants and squirrels
- Daily operations
- Summary
- Chapter 5 footnotes
- 6 Ensuring Engagement with Business Professionals
- Overcoming roadblocks to analytics adoption
- Organizational culture
- Data or algorithms – the knee of the curve or the inflection point
- A managerial mindset
- The skills gap
- Linear and non-linear thinking
- Do you really need a budget?
- Not big data but lots of small data
- Introductory projects
- Value realization
- Summary
- Chapter 6 footnotes
- 7 Selecting Winning Projects
- Analytics self determination
- Communicating the value of analytics
- Relative value of analytics
- The value of analytics made easy
- Enabling understanding
- Enterprise-class project selection process
- Understanding and communicating the value of projects
- Delegation of decision making
- Technical or organizational factors
- Guidance to end users
- Where is the value in a project?
- Operational considerations
- Selling a project – vision value or both?
- Don't make all the decisions
- Do the subject matter experts know what "good" looks like?
- The project mix – small and large
- Opportunity and responsibility
- Summary
- 8 Operationalizing Analytics – How to Move from Projects to Production
- The change management process
- Getting to know the business
- Change management
- Analytics and discovery
- Analytical and production cycles and systems – initial projects
- Summary
- 9 Managing the New Analytical Ecosystem
- Stakeholder engagement – your primary purpose
- Bias – accounting for it and minimizing it
- Ethics
- Summary
- 10 The Future of Analytics – What Will We See Next?
- Data
- AI today
- Quantum computing and AI
- Artificial General Intelligence
- Today we are failing
- Teaching children to love numbers patterns and math
- Blending rote memorization with critical thinking as a teaching paradigm
- Summary
- Chapter 10 footnotes
- Other Books You May Enjoy
- Index 更新時(shí)間：2021-06-18 18:31:05
推薦閱讀
- Canvas LMS Course Design
- 計(jì)算機(jī)圖形學(xué)
- 工業(yè)機(jī)器人技術(shù)及應(yīng)用
- Verilog HDL數(shù)字系統(tǒng)設(shè)計(jì)入門(mén)與應(yīng)用實(shí)例
- OpenStack for Architects
- Mastering VMware vSphere 6.5
- Blockchain Quick Start Guide
- Effective DevOps with AWS
- Ruby on Rails敏捷開(kāi)發(fā)最佳實(shí)踐
- 從零開(kāi)始學(xué)C++
- INSTANT Munin Plugin Starter
- PLC與變頻技術(shù)應(yīng)用
- 數(shù)據(jù)要素：全球經(jīng)濟(jì)社會(huì)發(fā)展的新動(dòng)力
- 人工智能：智能人機(jī)交互
- 企業(yè)級(jí)Web開(kāi)發(fā)實(shí)戰(zhàn)
- Kubernetes on AWS
- 輸送技術(shù)、設(shè)備與工業(yè)應(yīng)用
- 單片機(jī)硬件接口電路及實(shí)例解析
- Modern Big Data Processing with Hadoop
- 三維動(dòng)畫(huà)制作（3ds max7.0）
- CAD應(yīng)用程序開(kāi)發(fā)詳解
- 信息技術(shù)基礎(chǔ)與應(yīng)用
- DevOps：Puppet，Docker，and Kubernetes
- TensorFlow深度學(xué)習(xí)應(yīng)用實(shí)踐
- 人，機(jī)，生活
- Mastering Metasploit
- 實(shí)時(shí)數(shù)據(jù)處理和分析指南
- Java ME嵌入式程序設(shè)計(jì)
- 大道至簡(jiǎn)：軟件工程實(shí)踐者的思想
- Windows 8入門(mén)與提高
