標(biāo)題: Titlebook: Data Science; Create Teams That As Doug Rose Book 2016 Doug Rose 2016 data science.team.agile.analytics.data-driven organization.data minin [打印本頁] 作者: Lipase 時間: 2025-3-21 17:21
書目名稱Data Science影響因子(影響力)
書目名稱Data Science影響因子(影響力)學(xué)科排名
書目名稱Data Science網(wǎng)絡(luò)公開度
書目名稱Data Science網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Science被引頻次
書目名稱Data Science被引頻次學(xué)科排名
書目名稱Data Science年度引用
書目名稱Data Science年度引用學(xué)科排名
書目名稱Data Science讀者反饋
書目名稱Data Science讀者反饋學(xué)科排名
作者: Fluctuate 時間: 2025-3-21 23:24
Book 2016u through the process of creating and managing effective datascience 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 organiza作者: ingestion 時間: 2025-3-22 04:25
Forming the Teamunds experimenting with your data to create knowledge. That’s the scientific method in data science. It’s an empirical process of exploration. You’ll ask good questions, gather evidence, and try to draw conclusions.作者: NUDGE 時間: 2025-3-22 05:25 作者: curriculum 時間: 2025-3-22 11:31
Remy Cazabet,Pierre Borgnat,Pablo Jensensoning by asking interesting questions. Next, I explore how you can find the right mindset for your team as a whole. And finally, you learn about making sense of the data and get some tips on how to find your way out of team freezes.作者: 滔滔不絕的人 時間: 2025-3-22 15:42
Lale Madahali,Lotfi Najjar,Margeret Hallit’s not valuable, the team can quickly pivot to other questions. If it is, the team can take a deeper dive and maybe even come up with questions based on feedback from the business. This feedback loop is essential in making sure that the team’s work is tied to business value.作者: 滔滔不絕的人 時間: 2025-3-22 17:06
Book 2016in 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.作者: OFF 時間: 2025-3-23 01:07
Thinking Like a Data Science Teamsoning by asking interesting questions. Next, I explore how you can find the right mindset for your team as a whole. And finally, you learn about making sense of the data and get some tips on how to find your way out of team freezes.作者: 無脊椎 時間: 2025-3-23 04:31
Working in Sprintsit’s not valuable, the team can quickly pivot to other questions. If it is, the team can take a deeper dive and maybe even come up with questions based on feedback from the business. This feedback loop is essential in making sure that the team’s work is tied to business value.作者: insurrection 時間: 2025-3-23 08:02 作者: 否決 時間: 2025-3-23 13:35
https://doi.org/10.1007/978-1-4842-2253-9data science; team; agile; analytics; data-driven organization; data mining; scientific method; storytellin作者: 討好女人 時間: 2025-3-23 15:32 作者: 連詞 時間: 2025-3-23 20:57
Spanning Edge Betweenness in Practice you’ll want to store your data. Technologies like NoSQL provide you with a lot of flexibility to store different data types. Relational databases give you less flexibility, but they’re sometimes easier to work with, and it’s generally easier to generate reports in relational databases.作者: Obituary 時間: 2025-3-23 22:25 作者: Coeval 時間: 2025-3-24 03:30
https://doi.org/10.1007/978-3-319-30569-1 organization to think about your data in creative and interesting ways. Data analysts will help you analyze your data, but they may not be the best source of new insights. As mentioned in Chapter 6, you should think about data science as a team endeavor—small groups of people with different backgro作者: 手術(shù)刀 時間: 2025-3-24 08:49 作者: 態(tài)學(xué) 時間: 2025-3-24 13:03
Remy Cazabet,Pierre Borgnat,Pablo Jensen team? In this chapter, I help you out by examining some common ways to keep your team on track. First, I cover how to keep from reporting without reasoning by asking interesting questions. Next, I explore how you can find the right mindset for your team as a whole. And finally, you learn about maki作者: arthroscopy 時間: 2025-3-24 15:44 作者: Engulf 時間: 2025-3-24 22:08
Lale Madahali,Lotfi Najjar,Margeret Halling. Your team can create questions and get quick feedback to see if the data stories are valuable and resonate with the rest of the organization. If it’s not valuable, the team can quickly pivot to other questions. If it is, the team can take a deeper dive and maybe even come up with questions base作者: Nausea 時間: 2025-3-25 00:13 作者: progestin 時間: 2025-3-25 06:19
Doug RoseShows how to create data science teams from existing talent in organizations.Teaches how to cost-efficiently extract maximum business value from the organizations’ data.Presents the data science life 作者: 誘騙 時間: 2025-3-25 11:15 作者: STING 時間: 2025-3-25 15:20
Covering Database Basicsk interesting questions. There are many different types of databases. In addition, there is a lot of terminology used specifically for databases. You will need to be familiar with the basic concepts and terms used in the database world and with how different databases are organized.作者: 粗糙濫制 時間: 2025-3-25 18:51
Recognizing Different Data Types you’ll want to store your data. Technologies like NoSQL provide you with a lot of flexibility to store different data types. Relational databases give you less flexibility, but they’re sometimes easier to work with, and it’s generally easier to generate reports in relational databases.作者: 有惡臭 時間: 2025-3-25 20:12 作者: anaerobic 時間: 2025-3-26 00:12 作者: anthropologist 時間: 2025-3-26 05:50
A New Way of Workingstions, and then you’ll research those questions. Next, you’ll use the research to come up with new insights. Your team needs to take an empirical approach to the work. Instead of planning, they’ll need to adapt. Instead of relying on answers, they’ll look for interesting questions.作者: babble 時間: 2025-3-26 09:13
Avoiding Pitfalls in Delivering in Data Science Sprintsves. Many organizations focus on objectives and create powerful compliance departments. These departments ensure that everyone meets those objectives. This focus can keep your team from exploring and discovering. A data science team needs to take advantage of serendipity and add to organizational knowledge.作者: Ruptured-Disk 時間: 2025-3-26 14:48 作者: ANTE 時間: 2025-3-26 18:18
Spanning Edge Betweenness in Practice you’ll want to store your data. Technologies like NoSQL provide you with a lot of flexibility to store different data types. Relational databases give you less flexibility, but they’re sometimes easier to work with, and it’s generally easier to generate reports in relational databases.作者: 吵鬧 時間: 2025-3-27 00:58
Studies in Computational Intelligencetistics and math to see if they can get at answers. Statistics is a very interesting field. To participate in a data science team, you need some basic understanding of the language. There are several terms you need to be familiar with as you explore statistical analysis. They are:作者: constellation 時間: 2025-3-27 03:51 作者: cancellous-bone 時間: 2025-3-27 07:22 作者: ARCHE 時間: 2025-3-27 13:22
Springer Proceedings in Complexityves. Many organizations focus on objectives and create powerful compliance departments. These departments ensure that everyone meets those objectives. This focus can keep your team from exploring and discovering. A data science team needs to take advantage of serendipity and add to organizational knowledge.作者: 細胞膜 時間: 2025-3-27 16:47 作者: 遣返回國 時間: 2025-3-27 18:59 作者: entail 時間: 2025-3-28 00:51
Spanning Edge Betweenness in PracticeWe defined data science in Chapter 2 and covered what it means to be a “data scientist.” In this chapter, you’ll see how to break that role into several team roles. Then you’ll see how this team can work together to build a greater data science mindset.作者: MOAT 時間: 2025-3-28 02:08
https://doi.org/10.1007/978-3-319-54241-6In this chapter, we cover the two of the main pitfalls that affect data science teams. First, if a team reaches a consensus too quickly, it stifles discovery and is a sign that the team has blind spots and is prone to groupthink.作者: Matrimony 時間: 2025-3-28 08:49
https://doi.org/10.1007/978-3-030-14459-3Most of the people on your data science team will be familiar with a typical project life cycle. People from a software development background are familiar with the software development life cycle (SDLC). People from data mining probably used the Cross Industry Standard Process for Data Mining (CRISP-DM).作者: 陰謀小團體 時間: 2025-3-28 10:54 作者: 文字 時間: 2025-3-28 18:15 作者: 紅腫 時間: 2025-3-28 21:59 作者: ANIM 時間: 2025-3-29 00:42
Rounding Out Your TalentWe defined data science in Chapter 2 and covered what it means to be a “data scientist.” In this chapter, you’ll see how to break that role into several team roles. Then you’ll see how this team can work together to build a greater data science mindset.作者: affinity 時間: 2025-3-29 05:37 作者: jet-lag 時間: 2025-3-29 07:24
Using a Data Science Life CycleMost of the people on your data science team will be familiar with a typical project life cycle. People from a software development background are familiar with the software development life cycle (SDLC). People from data mining probably used the Cross Industry Standard Process for Data Mining (CRISP-DM).作者: Consensus 時間: 2025-3-29 14:38 作者: 悄悄移動 時間: 2025-3-29 19:11 作者: 除草劑 時間: 2025-3-29 20:28 作者: 頭盔 時間: 2025-3-30 01:54
Applying Statistical Analysististics and math to see if they can get at answers. Statistics is a very interesting field. To participate in a data science team, you need some basic understanding of the language. There are several terms you need to be familiar with as you explore statistical analysis. They are:作者: Institution 時間: 2025-3-30 05:02 作者: elastic 時間: 2025-3-30 11:26 作者: 隱語 時間: 2025-3-30 16:23 作者: Concrete 時間: 2025-3-30 17:04 作者: 想象 時間: 2025-3-30 21:23 作者: savage 時間: 2025-3-31 04:23
Avoiding Pitfalls in Delivering in Data Science Sprintsves. Many organizations focus on objectives and create powerful compliance departments. These departments ensure that everyone meets those objectives. This focus can keep your team from exploring and discovering. A data science team needs to take advantage of serendipity and add to organizational kn
