標(biāo)題: Titlebook: Business Analytics; Data Science for Bus Walter R. Paczkowski Book 2021 Springer Nature Switzerland AG 2021 data science.machine learning.b [打印本頁(yè)] 作者: Coolidge 時(shí)間: 2025-3-21 18:45
書(shū)目名稱Business Analytics影響因子(影響力)
作者: 雪崩 時(shí)間: 2025-3-21 23:33 作者: 惡名聲 時(shí)間: 2025-3-22 01:56
Data Visualization: The Basicsscussed in many books. I focus on data visualization from a practical analytical point-of-view in this chapter, not their presentation. This does not mean, however, that they cannot be used in a presentation; they certainly can be used. The graphs I describe are meant to aid and enhance the extracti作者: 殖民地 時(shí)間: 2025-3-22 08:37 作者: 含糊 時(shí)間: 2025-3-22 08:57 作者: 產(chǎn)生 時(shí)間: 2025-3-22 14:08 作者: 多嘴 時(shí)間: 2025-3-22 18:19
Advanced , for Business Data Analyticsns of the Data Cube. The reason for the segue is that Business Data Analytics involves more than just modeling. It also involves classification. Both are methods for learning from your data, the former in a supervised fashion and the latter in an unsupervised fashion.作者: Daily-Value 時(shí)間: 2025-3-22 21:35 作者: 現(xiàn)暈光 時(shí)間: 2025-3-23 03:54 作者: 獨(dú)行者 時(shí)間: 2025-3-23 08:33 作者: 披肩 時(shí)間: 2025-3-23 10:03
Walter R. PaczkowskiUses case studies to illustrate concepts.Presents examples using Python in the context of Jupyter notebooks with Programming Literacy examples.Features appendices with technical details作者: prosperity 時(shí)間: 2025-3-23 13:50
http://image.papertrans.cn/b/image/192028.jpg作者: Exclude 時(shí)間: 2025-3-23 20:30
Data Sources, Organization, and Structuresever, that you need data first is too simplistic and trivial. Where data originate, how you get them, and what you do with them, that is, how you manipulate them, before you begin your work is important to address.作者: 獨(dú)裁政府 時(shí)間: 2025-3-24 00:37 作者: Obloquy 時(shí)間: 2025-3-24 03:38
Advanced Data Handling: Preprocessing Methodso or very few issues as I noted at the beginning of Chap. .. Unfortunately, real world data do not agree with this paradigm. They are, to say the least, messy. They have missing values, are disorganized relative to what you need to do, and are just, well, a mess. Before any meaningful work is done, you have to . or, better yet, . your messy data.作者: epicardium 時(shí)間: 2025-3-24 09:58 作者: Omniscient 時(shí)間: 2025-3-24 12:17
Time Series Analysissiness data sets. The data, a ., could be for each second because of sensor readings, each minute for a production process, daily for accounting recording, monthly for sales and revenue processing and reporting, quarterly for financial reporting to legal and regulatory agencies, or annually for shareholder meetings.作者: 走調(diào) 時(shí)間: 2025-3-24 15:20 作者: Visual-Field 時(shí)間: 2025-3-24 20:58
Classification with Supervised Learning Methodss. Recall that “prediction” is a broad label that includes forecasting as a subset: all forecasts are predictions but not all predictions are forecasts. Predicting in general is a very important function of Business Data Analytics which is why I spent so much time on it.作者: 使人入神 時(shí)間: 2025-3-25 02:22
Food and Identity in Early Rabbinic JudaismSpoiler-alert: . (.), the focus of this book, is solely concerned with one task, and one task only: to provide the richest information possible to decision makers.作者: quiet-sleep 時(shí)間: 2025-3-25 04:17 作者: Inveterate 時(shí)間: 2025-3-25 08:38
Molecular Identification of MealybugsIn this chapter, I will set the stage for analysis beyond what I discussed in the previous chapters. I covered that material at a high level. Specialized books cover them in greater detail; in fact, whole volumes are written on each of those topics. The ones in this chapter are different. They cover advanced data handling topics.作者: facilitate 時(shí)間: 2025-3-25 14:29
Singular behaviour of convex surfacesI will turn my attention to . methods in this chapter. Recall that these methods do not have a target variable that guides them to learn from a set of features. There are still features, but without a target another approach is needed to extract the information buried inside your data.作者: superfluous 時(shí)間: 2025-3-25 17:05
Introduction to Business Data Analytics: Setting the StageSpoiler-alert: . (.), the focus of this book, is solely concerned with one task, and one task only: to provide the richest information possible to decision makers.作者: 感情脆弱 時(shí)間: 2025-3-25 21:31 作者: Admonish 時(shí)間: 2025-3-26 03:52
Advanced Data Handling for Business Data AnalyticsIn this chapter, I will set the stage for analysis beyond what I discussed in the previous chapters. I covered that material at a high level. Specialized books cover them in greater detail; in fact, whole volumes are written on each of those topics. The ones in this chapter are different. They cover advanced data handling topics.作者: 親愛(ài) 時(shí)間: 2025-3-26 07:39
Grouping with Unsupervised Learning MethodsI will turn my attention to . methods in this chapter. Recall that these methods do not have a target variable that guides them to learn from a set of features. There are still features, but without a target another approach is needed to extract the information buried inside your data.作者: 拱形面包 時(shí)間: 2025-3-26 12:30
https://doi.org/10.1057/9781137363794ever, that you need data first is too simplistic and trivial. Where data originate, how you get them, and what you do with them, that is, how you manipulate them, before you begin your work is important to address.作者: 注射器 時(shí)間: 2025-3-26 13:40 作者: 騷動(dòng) 時(shí)間: 2025-3-26 18:56 作者: 彎曲道理 時(shí)間: 2025-3-27 00:22
Slaves at Greco-Roman Banquets: A Responsens. An example of the former is a price elasticity used for repricing an existing product or setting the initial price for a new one. In either case, the elasticity is calculated based on the relationship between price and quantity, allowing for other factors such as income levels, seasons of the year, geographic locations, and so forth.作者: 懶惰民族 時(shí)間: 2025-3-27 05:04
A Typology of the Communal Mealsiness data sets. The data, a ., could be for each second because of sensor readings, each minute for a production process, daily for accounting recording, monthly for sales and revenue processing and reporting, quarterly for financial reporting to legal and regulatory agencies, or annually for shareholder meetings.作者: 懸崖 時(shí)間: 2025-3-27 07:06 作者: 偶像 時(shí)間: 2025-3-27 11:40
Some applications to hyperbolic geometrys. Recall that “prediction” is a broad label that includes forecasting as a subset: all forecasts are predictions but not all predictions are forecasts. Predicting in general is a very important function of Business Data Analytics which is why I spent so much time on it.作者: Inelasticity 時(shí)間: 2025-3-27 14:29 作者: Bravura 時(shí)間: 2025-3-27 21:33 作者: Obscure 時(shí)間: 2025-3-28 01:25 作者: GAVEL 時(shí)間: 2025-3-28 04:34 作者: 變異 時(shí)間: 2025-3-28 07:49 作者: single 時(shí)間: 2025-3-28 13:18 作者: Isometric 時(shí)間: 2025-3-28 15:18 作者: 等待 時(shí)間: 2025-3-28 19:12 作者: Invigorate 時(shí)間: 2025-3-28 23:03 作者: shrill 時(shí)間: 2025-3-29 05:07 作者: expdient 時(shí)間: 2025-3-29 10:09 作者: Accolade 時(shí)間: 2025-3-29 14:07 作者: AORTA 時(shí)間: 2025-3-29 18:45
Basic Data Handlingthat may have to be merged or joined is not discussed. Consequently, learners must determine from other sources how to handle “messy” and large amounts of data. These are, in fact, typical operations in Business Data Analytics. They require . before any analysis can begin.作者: 地名詞典 時(shí)間: 2025-3-29 20:17
lysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research..978-3-030-87025-6978-3-030-87023-2作者: bypass 時(shí)間: 2025-3-30 00:19
