数据挖掘（Data Mining）培训
由讲师进行实时指导的数据挖掘本地培训课程通过动手练习和实操演示了数据挖掘的基础知识，其方法来源包括人工智能、机器学习、统计和数据库系统，以及其用法和应用。 数据挖掘培训形式包括“现场实时培训”和“远程实时培训”。现场实时培训可在客户位于China的所在场所或NobleProg位于China的企业培训中心进行，远程实时培训可通过交互式远程桌面进行。 NobleProg -- 您的本地培训提供商
客户评论
技术介绍。
Continental AG / Abteilung: CF IT Finance
课程名称: A practical introduction to Data Analysis and Big Data
Translated by
presentation of technologies
总的来说内容很好
Sameer Rohadia
课程名称: A practical introduction to Data Analysis and Big Data
Translated by
Overall the Content was good.
灵活性与Autodata相关的细节融合在一起，以获得更多真实世界的场景。
Autodata Ltd
课程名称: Beyond the relational database: neo4j
Translated by
Flexibility to blend in with Autodata related details to get more of a real world scenario as we went on.
灵活性与Autodata相关的细节融合在一起，以获得更多真实世界的场景。
Autodata Ltd
课程名称: Beyond the relational database: neo4j
Translated by
Flexibility to blend in with Autodata related details to get more of a real world scenario as we went on.
培训师的确提供了一些很好的见解和方法来开发图形数据库。他使用幻灯片中的例子，但也借鉴了他自己的经验，这很好。
Autodata Ltd
课程名称: Beyond the relational database: neo4j
Translated by
The trainer did bring some good insight and ways to approach developing a graph database. He used examples from the slides presented but also drew on his own experience which was good.
培训师的确提供了一些很好的见解和方法来开发图形数据库。他使用幻灯片中的例子，但也借鉴了他自己的经验，这很好。
Autodata Ltd
课程名称: Beyond the relational database: neo4j
Translated by
The trainer did bring some good insight and ways to approach developing a graph database. He used examples from the slides presented but also drew on his own experience which was good.
培训师的确提供了一些很好的见解和方法来开发图形数据库。他使用幻灯片中的例子，但也借鉴了他自己的经验，这很好。
Autodata Ltd
课程名称: Beyond the relational database: neo4j
Translated by
The trainer did bring some good insight and ways to approach developing a graph database. He used examples from the slides presented but also drew on his own experience which was good.
实例演习; 实际工作经验分享
澳新银行
课程名称: Data Vault: Building a Scalable Data Warehouse
立方体和DV
Alan Xie
课程名称: Data Vault: Building a Scalable Data Warehouse
老师对数据仓库的知识很全面，赞！
澳新银行
课程名称: Data Vault: Building a Scalable Data Warehouse
老师讲解细致, 讨论气氛融洽
澳新银行
课程名称: Data Vault: Building a Scalable Data Warehouse
示例和培训材料足以让您轻松理解您在做什么。
Teboho Makenete
课程名称: Data Science for Big Data Analytics
Translated by
The example and training material were sufficient and made it easy to understand what you are doing
课程简介
| 代码 | 名字 | 时长 | 概览 |
|---|---|---|---|
| smtwebint | Semantic Web Overview | 7小时 | The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. |
| BigData_ | 数据分析和大数据的实用介绍 | 35小时 | 参与者完成此次培训后，将会对大数据及其相关技术、方法、工具有一个实际和真实的理解。 参与者将有机会通过动手练习将这些知识付诸实践。小组互动和讲师反馈是课堂的重要组成部分。 本课程首先介绍大数据的基本概念，然后讲解用于执行数据分析的编程语言和方法，最后我们会讨论可启用大数据存储、分布式处理及可扩展性的工具和基础架构。 受众 开发人员/程序员 IT顾问 课程形式 部分讲座、部分讨论、实操、偶尔测评进度 |
| mdlmrah | Model MapReduce and Apache Hadoop | 14小时 | The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers. |
| ApHadm1 | Apache Hadoop: Manipulation and Transformation of Data Performance | 21小时 | This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis. The major focus of the course is data manipulation and transformation. Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation. This training also addresses performance metrics and performance optimisation. The course is entirely hands on and is punctuated by presentations of the theoretical aspects. |
| sspsspas | Statistics with SPSS Predictive Analytics Software | 14小时 | Goal: Learning to work with SPSS at the level of independence The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques. |
| matlabfundamentalsfinance | MATLAB基础 + MATLAB用于财务工作 | 35小时 | 本课程提供了MATLAB技术计算环境的全面介绍，及使用MATLAB进行金融应用的介绍。本课程面向初级使用者及有意复习相关知识的使用者。不需有编程经验或MATLAB知识。在整个课程中探讨数据分析、可视化、建模、编程等主题。主题包括： 使用MATLAB用户界面 输入命令并创建变量 分析向量和矩阵 可视化矢量和矩阵数据 使用数据文件 处理数据类型 使用脚本自动执行命令 用逻辑和流控制编写程序 写作功能 使用金融工具箱进行定量分析 |
| bdbiga | Big Data Business Intelligence for Govt. Agencies | 35小时 | Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information. High-value government solutions will be created from a mashup of the most disruptive technologies: Mobile devices and applications Cloud services Social business technologies and networking Big Data and analytics IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured. But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog. The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it. The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge. Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.). Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers. |
| matlabdsandreporting | MATLAB基础、数据科学和报告生成 | 126小时 | 本次培训的第一部分介绍了MATLAB的基本原理及其作为语言和平台的功能。本次讨论包括MATLAB语法、数组和矩阵、数据可视化、脚本开发及面向对象原理的介绍。 在第二部分中，我们演示如何使用MATLAB进行数据挖掘、机器学习和预测性分析。为了给参与者一个关于MATLAB方法和功能的清晰和实用的观点，我们将使用MATLAB和使用电子表格、C、C ++、Visual Basic等其他工具进行比较。 在培训的第三部分，参与者学习如何通过自动化数据处理和报告生成来简化工作。 在整个课程中，参与者将在实验室环境里把通过动手练习学到的想法付诸实践。培训结束后，参与者将对MATLAB的功能有一个全面的掌握，并将能够用它来解决实际的数据科学问题，并通过自动化来简化他们的工作。 整个课程中将进行评估以衡量进度。 课程形式 课程包含理论和实践练习，包括案例讨论、样本代码检查和实操。 注意 练习课程将根据预先安排的样本数据报告模板进行。如果您有特殊要求，请联系我们以作安排。 |
| datamin | Data Mining | 21小时 | Course can be provided with any tools, including free open-source data mining software and applications |
| TalendDI | Talend Open Studio for Data Integration | 28小时 | Talend Open Studio for Data Integration is an open-source data integration product used to combine, convert and update data in various locations across a business. In this instructor-led, live training, participants will learn how to use the Talend ETL tool to carry out data transformation, data extraction, and connectivity with Hadoop, Hive, and Pig. By the end of this training, participants will be able to Explain the concepts behind ETL (Extract, Transform, Load) and propagation Define ETL methods and ETL tools to connect with Hadoop Efficiently amass, retrieve, digest, consume, transform and shape big data in accordance to business requirements Upload to and extract large records from Hadoop, Hive, and NoSQL databases Audience Business intelligence professionals Project managers Database professionals SQL Developers ETL Developers Solution architects Data architects Data warehousing professionals System administrators and integrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| d2dbdpa | From Data to Decision with Big Data and Predictive Analytics | 21小时 | Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. |
| PentahoDI | Pentaho Data Integration Fundamentals | 21小时 | Pentaho Data Integration is an open-source data integration tool for defining jobs and data transformations. In this instructor-led, live training, participants will learn how to use Pentaho Data Integration's powerful ETL capabilities and rich GUI to manage an entire big data lifecycle, maximizing the value of data to the organization. By the end of this training, participants will be able to: Create, preview, and run basic data transformations containing steps and hops Configure and secure the Pentaho Enterprise Repository Harness disparate sources of data and generate a single, unified version of the truth in an analytics-ready format. Provide results to third-part applications for further processing Audience Data Analyst ETL developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pmml | Predictive Models with PMML | 7小时 | The course is created to scientific, developers, analysts or any other people who want to standardize or exchange their models with Predictive Model Markup Language (PMML) file format. |
| datavault | Data Vault: Building a Scalable Data Warehouse | 28小时 | Data vault modeling is a database modeling technique that provides long-term historical storage of data that originates from multiple sources. A data vault stores a single version of the facts, or "all the data, all of the time". Its flexible, scalable, consistent and adaptable design encompasses the best aspects of 3rd normal form (3NF) and star schema. In this instructor-led, live training, participants will learn how to build a Data Vault. By the end of this training, participants will be able to: Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI. Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse Develop a consistent and repeatable ETL (Extract, Transform, Load) process Build and deploy highly scalable and repeatable warehouses Audience Data modelers Data warehousing specialist Business Intelligence specialists Data engineers Database administrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dataminr | Data Mining with R | 14小时 | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| zendfundamentals | Zend Framework: Fundamentals | 21小时 | Zend framework is an open-source, object-orientated framework for developing, deploying, and managing enterprise-ready PHP based web applications and services. Zend framework utilizes the Model-View-Controller (MVC) paradigm to develop basic structures for applications. Zend is considered a "component library"; its unique modular design enables users to use components independently of one another. In this instructor-led, live training, participants will learn how to create a reliable and scalable web application using the Zend framework. By the end of this training, participants will be able to: Use Model-View-Controller design patterns to build a database-based web application Receive and process forms Set up input validation and view scripts Handle the various types of MVC events and services offered by Zend Framework MVC component library Prepare and execute queries for a database adapter Audience Intermediate to advanced PHP developers seeking to develop secure, enterprise scale web-applications Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| datashrinkgov | Data Shrinkage for Government | 14小时 | The objective of the course is to enable participants to gain a mastery of the fundamentals of data shrinkage for government. |
| monetdb | MonetDB | 28小时 | MonetDB is an open-source database that pioneered the column-store technology approach. In this instructor-led, live training, participants will learn how to use MonetDB and how to get the most value out of it. By the end of this training, participants will be able to: Understand MonetDB and its features Install and get started with MonetDB Explore and perform different functions and tasks in MonetDB Accelerate the delivery of their project by maximizing MonetDB capabilities Audience Developers Technical experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| matlab2 | MATLAB 基础 | 21小时 | MATLAB软件简介 MATLAB（矩阵实验室）是MATrix LABoratory的缩写,是一款由美国The MathWorks公司出品的商业科学计算和仿真软件.MATLAB拥有一套可用于算法开发,数据可视化,数据分析以及数值计算的高级技术计算语言和交互式环境.除了矩阵运算,求解线性系统方程,绘制函数/数据图像等常用功能外,MATLAB还可以用来创建用户界面及与调用其它语言（包括C,C++,Java,Python和FORTRAN）编写的程序。 尽管MATLAB最初主要用于科学计算,但其不断增加的各种附加工具箱(到目前为止将近100个)使之适合不同领域和行业的应用,如控制系统设计与分析,生物医疗,图像处理,信号处理与通讯,金融建模和分析,汽车,航天航空等。另外还有一个基于模型化设计(MBD)的图形化仿真软件包Simulink用于系统模拟,代码生成,动态/嵌入式系统开发等方面. 培训目的 本课程将全面介绍MATLAB科学技术计算环境,旨在于使初学者迅速掌握MATLAB原理,在课程结束后可以: -> 熟悉MATLAB界面,查找帮助; -> 键入命令,进行变量,向量和矩阵的基本操作; -> 对数据进行多种可视化展示; -> 处理数据文件和不同数据类型; -> 编写脚本和函数,并在其中包含必要的逻辑和分支控制; -> 读写文本和二进制文件 课程特色 本次课程使用MATLAB2014a用于演示。本着由浅入深,注重实践,重点问题反复强调的原则,不拘泥于PPT讲义,尽量多使用实例进行示范操作. |
| datavis1 | Data Visualization | 28小时 | This course is intended for engineers and decision makers working in data mining and knoweldge discovery. You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers and help them to understand hidden information. |
| dsbda | Data Science for Big Data Analytics | 35小时 | Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy. |
| neo4j | Beyond the relational database: neo4j | 21小时 | Relational, table-based databases such as Oracle and MySQL have long been the standard for organizing and storing data. However, the growing size and fluidity of data have made it difficult for these traditional systems to efficiently execute highly complex queries on the data. Imagine replacing rows-and-columns-based data storage with object-based data storage, whereby entities (e.g., a person) could be stored as data nodes, then easily queried on the basis of their vast, multi-linear relationship with other nodes. And imagine querying these connections and their associated objects and properties using a compact syntax, up to 20 times lighter than SQL. This is what graph databases, such as neo4j offer. In this hands-on course, we will set up a live project and put into practice the skills to model, manage and access your data. We contrast and compare graph databases with SQL-based databases as well as other NoSQL databases and clarify when and where it makes sense to implement each within your infrastructure. Audience Database administrators (DBAs) Data analysts Developers System Administrators DevOps engineers Business Analysts CTOs CIOs Format of the course Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development. |
| processmining | Process Mining | 21小时 | Process mining, or Automated Business Process Discovery (ABPD), is a technique that applies algorithms to event logs for the purpose of analyzing business processes. Process mining goes beyond data storage and data analysis; it bridges data with processes and provides insights into the trends and patterns that affect process efficiency. Format of the course The course starts with an overview of the most commonly used techniques for process mining. We discuss the various process discovery algorithms and tools used for discovering and modeling processes based on raw event data. Real-life case studies are examined and data sets are analyzed using the ProM open-source framework. Audience Data science professionals Anyone interested in understanding and applying process modeling and data mining |
| kdd | Knowledge Discover in Databases (KDD) | 21小时 | Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience Data analysts or anyone interested in learning how to interpret data to solve problems Format of the course After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations. |
| druid | Druid: Build a fast, real-time data analysis system | 21小时 | Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo. In this course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment. Audience Application developers Software engineers Technical consultants DevOps professionals Architecture engineers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
Upcoming Courses
| 课程 | 日期 | 价格【远程 / 传统课堂】 |
|---|---|---|
| Predictive Models with PMML - 新东方大厦 | 星期二, 2018-06-12 09:30 | ￥10770 / ￥11970 |
| Statistics with SPSS Predictive Analytics Software - 北京 - 数码大厦 | 星期四, 2018-06-14 09:30 | ￥17750 / ￥20950 |
| Model MapReduce and Apache Hadoop - 深圳 - 新世界中心 | 星期二, 2018-06-19 09:30 | ￥18180 / ￥22560 |
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