預測分析培訓 | Predictive Analytics培訓

Overview Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month. Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored. With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.) This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain. Course objectives Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following:

• Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective
• How Big Data analytic differs from legacy data analytic
• In-house justification of Big Data -Telco perspective
• Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem
• How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach
• Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco
• Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies
• Network failure and service failure analytics from Network meta-data and IPDR
• Financial analysis-fraud, wastage and ROI estimation from sales and operational data
• Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data
• Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space
• Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization

Target Audience

• Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office.
• Business Analysts in Telco
• CFO office managers/analysts
• Operational managers
• QA managers

聽眾如果您試圖理解您可以訪問或想要分析網絡上可用的非結構化數據（如Twitter，鏈接等等），那麼本課程適合您。 它主要針對決策者和需要選擇哪些數據值得收集以及值得分析的人。 它不是針對人們配置解決方案，但這些人將從大局中受益。 交貨方式在課程期間，代表們將獲得大多數開源技術的工作示例。 講座後將進行簡短的講座，參加者將進行簡單的練習使用的內容和軟件每次運行課程時都會更新所有使用的軟件，因此我們會檢查最新版本。 它涵蓋了從獲取，格式化，處理和分析數據的過程，以解釋如何使用機器學習自動化決策制定過程。

R是用于統計計算，數據分析和圖形的開源免費編程語言。 R被企業和學術界內越來越多的管理人員和數據分析人員所使用。 R有各種各樣的數據挖掘軟件包。

R是用于統計計算，數據分析和圖形的開源免費編程語言。 R被企業和學術界內越來越多的管理人員和數據分析人員所使用。 R有各種各樣的數據挖掘軟件包。

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to:

• Create predictive models to analyze patterns in historical and transactional data
• Use predictive modeling to identify risks and opportunities
• Build mathematical models that capture important trends
• Use data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

技術的進步和越來越多的信息正在改變執法的方式。 Big Data帶來的挑戰幾乎與Big Data的承諾一樣令人生畏。有效存儲數據是這些挑戰之一;有效地分析它是另一回事。 在以講師為主導的現場培訓中，參與者將學習處理Big Data技術的思維方式，評估其對現有流程和政策的影響，並實施這些技術，以識別犯罪活動和預防犯罪。將審查世界各地執法組織的案例研究，以深入了解其採用方法，挑戰和結果。 在培訓結束時，參與者將能夠：

• 將Big Data技術與傳統的數據收集流程相結合，在調查過程中拼湊出一個故事
• 實施工業大數據存儲和處理數據分析解決方案
• 準備一份提案，以採用最適當的工具和程序，使數據驅動的方法能夠進行刑事調查

聽眾

• 具有技術背景的執法專家

課程形式

• 部分講座，部分討論，練習和繁重的實踐練習

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake.

RapidMiner是一個開源數據科學軟件平台，用於快速應用程序原型設計和開發。它包括用於數據準備，機器學習，深度學習，文本挖掘和預測分析的集成環境。 在這個由講師指導的實時培訓中，參與者將學習如何使用RapidMiner Studio進行數據準備，機器學習和預測模型部署。 在培訓結束時，參與者將能夠：

• 安裝和配置RapidMiner
• 使用RapidMiner準備和可視化數據
• 驗證機器學習模型
• Mashup數據並創建預測模型
• 在業務流程中實施預測分析
• 對RapidMiner故障排除和優化

聽眾

• 數據科學家
• 工程師
• 開發商

課程格式

• 部分講座，部分討論，練習和繁重的實踐練習

注意

• 要申請本課程的定制培訓，請聯繫我們安排。

H2O是一個開源預測分析平台。它支持R， Python ， Scala ， Java和REST。 這種以講師為主導的現場培訓（現場或遠程）是針對希望使用GLM， Deep Learning和隨機森林等算法構建機器學習模型的技術人員。 在培訓結束時，參與者將能夠：

• 安裝並配置H2O 。
• 使用不同的流行算法創建機器學習模型。
• 根據數據類型和業務需求評估模型。

課程格式

• 互動講座和討論。
• 大量的練習和練習。
• 在實時實驗室環境中親自實施。

課程自定義選項

• 要申請本課程的定制培訓，請聯繫我們安排。
• 要了解有關H2O更多信息，請訪問：https：//www.h2o.ai/

This instructor-led, live training in 台灣 (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities. By the end of this training, participants will be able to:

• Load datasets in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.