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Big Data Business Intelligence for Criminal Intelligence Analysis培訓(xùn)
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 班級規(guī)模及環(huán)境--熱線:4008699035 手機(jī):15921673576/13918613812( 微信同號) |
| 每個班級的人數(shù)限3到5人，互動授課， 保障效果，小班授課。 |
| 上間和地點 |
上課地點：【上海】：同濟(jì)大學(xué)(滬西)/新城金郡商務(wù)樓(11號線白銀路站) 【深圳分部】：電影大廈(地鐵一號線大劇院站)/深圳大學(xué)成教院 【北京分部】：北京中山/福鑫大樓 【南京分部】：金港大廈(和燕路) 【武漢分部】：佳源大廈（高新二路） 【成都分部】：領(lǐng)館區(qū)1號（中和大道） 【沈陽分部】：沈陽理工大學(xué)/六宅臻品 【鄭州分部】：鄭州大學(xué)/錦華大廈 【石家莊分部】：河北科技大學(xué)/瑞景大廈 【廣州分部】：廣糧大廈 【西安分部】：協(xié)同大廈
近開間(周末班/連續(xù)班/晚班）：2018年3月18日 |
| 實驗設(shè)備 |
◆小班教學(xué)，教學(xué)效果好
☆注重質(zhì)量☆邊講邊練
☆合格學(xué)員免費推薦工作
★實驗設(shè)備請點擊這兒查看★ |
| 質(zhì)量保障 |
1、培訓(xùn)過程中，如有部分內(nèi)容理解不透或消化不好，可免費在以后培訓(xùn)班中重聽；
2、課程完成后,授課老師留給學(xué)員手機(jī)和Email,保障培訓(xùn)效果,免費提供半年的技術(shù)支持。
3、培訓(xùn)合格學(xué)員可享受免費推薦就業(yè)機(jī)會。☆合格學(xué)員免費頒發(fā)相關(guān)工程師等資格證書，提升職業(yè)資質(zhì)。專注高端技術(shù)培訓(xùn)15年，曙海學(xué)員的能力得到大家的認(rèn)同，受到用人單位的廣泛贊譽，曙海的證書受到廣泛認(rèn)可。 |
課程大綱 |
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- Day 01
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Overview of Big Data Business Intelligence for Criminal Intelligence Analysis
- Case Studies from Law Enforcement - Predictive Policing
Big Data adoption rate in Law Enforcement Agencies and how they are aligning their future operation around Big Data Predictive Analytics
Emerging technology solutions such as gunshot sensors, surveillance video and social media
Using Big Data technology to mitigate information overload
Interfacing Big Data with Legacy data
Basic understanding of enabling technologies in predictive analytics
Data Integration & Dashboard visualization
Fraud management
Business Rules and Fraud detection
Threat detection and profiling
Cost benefit analysis for Big Data implementation
Introduction to Big Data
- Main characteristics of Big Data -- Volume, Variety, Velocity and Veracity.
MPP (Massively Parallel Processing) architecture
Data Warehouses – static schema, slowly evolving dataset
MPP Databases: Greenplum, Exadata, Teradata, Netezza, Vertica etc.
Hadoop Based Solutions – no conditions on structure of dataset.
Typical pattern : HDFS, MapReduce (crunch), retrieve from HDFS
Apache Spark for stream processing
Batch- suited for analytical/non-interactive
Volume : CEP streaming data
Typical choices – CEP products (e.g. Infostreams, Apama, MarkLogic etc)
Less production ready – Storm/S4
NoSQL Databases – (columnar and key-value): Best suited as analytical adjunct to data warehouse/database
NoSQL solutions
- KV Store - Keyspace, Flare, SchemaFree, RAMCloud, Oracle NoSQL Database (OnDB)
KV Store - Dynamo, Voldemort, Dynomite, SubRecord, Mo8onDb, DovetailDB
KV Store (Hierarchical) - GT.m, Cache
KV Store (Ordered) - TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord
KV Cache - Memcached, Repcached, Coherence, Infinispan, EXtremeScale, JBossCache, Velocity, Terracoqua
Tuple Store - Gigaspaces, Coord, Apache River
Object Database - ZopeDB, DB40, Shoal
Document Store - CouchDB, Cloudant, Couchbase, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Prsevere, Riak-Basho, Scalaris
Wide Columnar Store - BigTable, HBase, Apache Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI
Varieties of Data: Introduction to Data Cleaning issues in Big Data
- RDBMS – static structure/schema, does not promote agile, exploratory environment.
NoSQL – semi structured, enough structure to store data without exact schema before storing data
Data cleaning issues
Hadoop
- When to select Hadoop?
STRUCTURED - Enterprise data warehouses/databases can store massive data (at a cost) but impose structure (not good for active exploration)
SEMI STRUCTURED data – difficult to carry out using traditional solutions (DW/DB)
Warehousing data = HUGE effort and static even after implementation
For variety & volume of data, crunched on commodity hardware – HADOOP
Commodity H/W needed to create a Hadoop Cluster
Introduction to Map Reduce /HDFS
- MapReduce – distribute computing over multiple servers
HDFS – make data available locally for the computing process (with redundancy)
Data – can be unstructured/schema-less (unlike RDBMS)
Developer responsibility to make sense of data
Programming MapReduce = working with Java (pros/cons), manually loading data into HDFS
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Day 02
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Big Data Ecosystem -- Building Big Data ETL (Extract, Transform, Load) -- Which Big Data Tools to use and when?
- Hadoop vs. Other NoSQL solutions
For interactive, random access to data
Hbase (column oriented database) on top of Hadoop
Random access to data but restrictions imposed (max 1 PB)
Not good for ad-hoc analytics, good for logging, counting, time-series
Sqoop - Import from databases to Hive or HDFS (JDBC/ODBC access)
Flume – Stream data (e.g. log data) into HDFS
Big Data Management System
- Moving parts, compute nodes start/fail :ZooKeeper - For configuration/coordination/naming services
Complex pipeline/workflow: Oozie – manage workflow, dependencies, daisy chain
Deploy, configure, cluster management, upgrade etc (sys admin) :Ambari
In Cloud : Whirr
Predictive Analytics -- Fundamental Techniques and Machine Learning based Business Intelligence
- Introduction to Machine Learning
Learning classification techniques
Bayesian Prediction -- preparing a training file
Support Vector Machine
KNN p-Tree Algebra & vertical mining
Neural Networks
Big Data large variable problem -- Random forest (RF)
Big Data Automation problem – Multi-model ensemble RF
Automation through Soft10-M
Text analytic tool-Treeminer
Agile learning
Agent based learning
Distributed learning
Introduction to Open source Tools for predictive analytics : R, Python, Rapidminer, Mahut
Predictive Analytics Ecosystem and its application in Criminal Intelligence Analysis
- Technology and the investigative process
Insight analytic
Visualization analytics
Structured predictive analytics
Unstructured predictive analytics
Threat/fraudstar/vendor profiling
Recommendation Engine
Pattern detection
Rule/Scenario discovery – failure, fraud, optimization
Root cause discovery
Sentiment analysis
CRM analytics
Network analytics
Text analytics for obtaining insights from transcripts, witness statements, internet chatter, etc.
Technology assisted review
Fraud analytics
Real Time Analytic
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Day 03
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Real Time and Scalable Analytics Over Hadoop
- Why common analytic algorithms fail in Hadoop/HDFS
Apache Hama- for Bulk Synchronous distributed computing
Apache SPARK- for cluster computing and real time analytic
CMU Graphics Lab2- Graph based asynchronous approach to distributed computing
KNN p -- Algebra based approach from Treeminer for reduced hardware cost of operation
Tools for eDiscovery and Forensics
- eDiscovery over Big Data vs. Legacy data – a comparison of cost and performance
Predictive coding and Technology Assisted Review (TAR)
Live demo of vMiner for understanding how TAR enables faster discovery
Faster indexing through HDFS – Velocity of data
NLP (Natural Language processing) – open source products and techniques
eDiscovery in foreign languages -- technology for foreign language processing
Big Data BI for Cyber Security – Getting a 360-degree view, speedy data collection and threat identification
- Understanding the basics of security analytics -- attack surface, security misconfiguration, host defenses
Network infrastructure / Large datapipe / Response ETL for real time analytic
Prescriptive vs predictive – Fixed rule based vs auto-discovery of threat rules from Meta data
Gathering disparate data for Criminal Intelligence Analysis
- Using IoT (Internet of Things) as sensors for capturing data
Using Satellite Imagery for Domestic Surveillance
Using surveillance and image data for criminal identification
Other data gathering technologies -- drones, body cameras, GPS tagging systems and thermal imaging technology
Combining automated data retrieval with data obtained from informants, interrogation, and research
Forecasting criminal activity
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Day 04
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Fraud prevention BI from Big Data in Fraud Analytics
- Basic classification of Fraud Analytics -- rules-based vs predictive analytics
Supervised vs unsupervised Machine learning for Fraud pattern detection
Business to business fraud, medical claims fraud, insurance fraud, tax evasion and money laundering
Social Media Analytics -- Intelligence gathering and analysis
- How Social Media is used by criminals to organize, recruit and plan
Big Data ETL API for extracting social media data
Text, image, meta data and video
Sentiment analysis from social media feed
Contextual and non-contextual filtering of social media feed
Social Media Dashboard to integrate diverse social media
Automated profiling of social media profile
Live demo of each analytic will be given through Treeminer Tool
Big Data Analytics in image processing and video feeds
- Image Storage techniques in Big Data -- Storage solution for data exceeding petabytes
LTFS (Linear Tape File System) and LTO (Linear Tape Open)
GPFS-LTFS (General Parallel File System - Linear Tape File System) -- layered storage solution for Big image data
Fundamentals of image analytics
Object recognition
Image segmentation
Motion tracking
3-D image reconstruction
Biometrics, DNA and Next Generation Identification Programs
- Beyond fingerprinting and facial recognition
Speech recognition, keystroke (analyzing a users typing pattern) and CODIS (combined DNA Index System)
Beyond DNA matching: using forensic DNA phenotyping to construct a face from DNA samples
Big Data Dashboard for quick accessibility of diverse data and display :
- Integration of existing application platform with Big Data Dashboard
Big Data management
Case Study of Big Data Dashboard: Tableau and Pentaho
Use Big Data app to push location based services in Govt.
Tracking system and management
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Day 05
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How to justify Big Data BI implementation within an organization:
- Defining the ROI (Return on Investment) for implementing Big Data
Case studies for saving Analyst Time in collection and preparation of Data – increasing productivity
Revenue gain from lower database licensing cost
Revenue gain from location based services
Cost savings from fraud prevention
An integrated spreadsheet approach for calculating approximate expenses vs. Revenue gain/savings from Big Data implementation.
Step by Step procedure for replacing a legacy data system with a Big Data System
- Big Data Migration Roadmap
What critical information is needed before architecting a Big Data system?
What are the different ways for calculating Volume, Velocity, Variety and Veracity of data
How to estimate data growth
Case studies
Review of Big Data Vendors and review of their products.
- Accenture
APTEAN (Formerly CDC Software)
Cisco Systems
Cloudera
Dell
EMC
GoodData Corporation
Guavus
Hitachi Data Systems
Hortonworks
HP
IBM
Informatica
Intel
Jaspersoft
Microsoft
MongoDB (Formerly 10Gen)
MU Sigma
Netapp
Opera Solutions
Oracle
Pentaho
Platfora
Qliktech
Quantum
Rackspace
Revolution Analytics
Salesforce
SAP
SAS Institute
Sisense
Software AG/Terracotta
Soft10 Automation
Splunk
Sqrrl
Supermicro
Tableau Software
Teradata
Think Big Analytics
Tidemark Systems
Treeminer
VMware (Part of EMC)
Q/A session
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本部份程部分實驗室實景 |
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 合作伙伴與授權(quán)機(jī)構(gòu) |
 Altera全球合作培訓(xùn)機(jī)構(gòu) |
 諾基亞Symbian公司授權(quán)培訓(xùn)中心 |
 Atmel公司全球戰(zhàn)略合作伙伴 |
 微軟全球嵌入式培訓(xùn)合作伙伴 |
 英國ARM公司授權(quán)培訓(xùn)中心 |
 ARM工具關(guān)鍵合作單位 |
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| 我們培訓(xùn)過的企業(yè)客戶評價： |
曙海的andriod系統(tǒng)與應(yīng)用培訓(xùn)完全符合了我公司的要求，達(dá)到了我公司培訓(xùn)的目的。特別值得一提的是授部份講師針對我們公司的開發(fā)的項目專門提供了一些很好程序的源代碼，基本滿足了我們的項目要求。
——上海貝爾，李工
曙海培訓(xùn)DSP2000的老師，上部份思路清晰，口齒清楚，由淺入深，重點突出，培訓(xùn)效果是不錯的，
達(dá)到了我們想要的效果，希望繼續(xù)合作下去。
——中國電子科技集團(tuán)技術(shù)部主任馬工
曙海的FPGA培訓(xùn)很好地填補(bǔ)了高校FPGA培訓(xùn)空白，不錯。總之，有利于學(xué)生的發(fā)展，有利于教師的發(fā)展，有利于部份程的發(fā)展，有利于社會的發(fā)展。
——上海電子，馮老師
曙海給我們公司提供的Dsp6000培訓(xùn)，符合我們項目的開發(fā)要求，解決了很多困惑我們很久的問題，與曙海的合作非常愉快。
——公安部第三研究所，項目部負(fù)責(zé)人李先生
MTK培訓(xùn)-我在網(wǎng)上找了很久，就是找不到。在曙海居然有MTK驅(qū)動的培訓(xùn)，老師經(jīng)驗很豐富，知識面很廣。下一個還想培訓(xùn)IPHONE蘋果手機(jī)。跟他們合作很愉快，老師很有人情味，態(tài)度很和藹。
——臺灣雙揚科技，研發(fā)處經(jīng)理，楊先生
曙海對我們公司的iPhone培訓(xùn)，實驗項目很多，確實學(xué)到了東西。受益無窮啊！特別是對于那種正在開發(fā)項目的，確實是物超所值。
——臺灣歐澤科技,張工
通過參加Symbian培訓(xùn)，再做Symbian相關(guān)的項目感覺更加得心應(yīng)手了，理論加實踐的授部份方式，很有針對性，非常的適合我們。學(xué)完之后，很輕松的就完成了我們的項目。
——IBM公司，沈經(jīng)理
有曙海這樣的DSP開發(fā)培訓(xùn)單位，是教育行業(yè)的財富，聽了他們的部份，茅塞頓開。
——上海醫(yī)療器械高等學(xué)校，羅老師
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| 我們新培訓(xùn)過的企業(yè)客戶以及培訓(xùn)的主要內(nèi)容： |
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一汽海馬汽車DSP培訓(xùn)
蘇州金屬研究院DSP培訓(xùn)
南京南瑞集團(tuán)技術(shù)FPGA培訓(xùn)
西安愛生技術(shù)集團(tuán)FPGA培訓(xùn)，DSP培訓(xùn)
成都熊谷加世電氣DSP培訓(xùn)
福斯賽諾分析儀器（蘇州）FPGA培訓(xùn)
南京國電工程FPGA培訓(xùn)
北京環(huán)境特性研究所達(dá)芬奇培訓(xùn)
中國科微系統(tǒng)與信息技術(shù)研究所FPGA高級培訓(xùn)
重慶網(wǎng)視只能流技術(shù)開發(fā)達(dá)芬奇培訓(xùn)
無錫力芯微電子股份IC電磁兼容
河北科研究所FPGA培訓(xùn)
上海微小衛(wèi)星工程中心DSP培訓(xùn)
廣州航天航空POWERPC培訓(xùn)
桂林航天工DSP培訓(xùn)
江蘇五維電子科技達(dá)芬奇培訓(xùn)
無錫步進(jìn)電機(jī)自動控制技術(shù)DSP培訓(xùn)
江門市安利電源工程DSP培訓(xùn)
長江力偉股份CADENCE培訓(xùn)
愛普生科技（無錫）數(shù)字模擬電路
河南平高電氣DSP培訓(xùn)
中國航天員科研訓(xùn)練中心A/D仿真
常州易控汽車電子WINDOWS驅(qū)動培訓(xùn)
南通大學(xué)DSP培訓(xùn)
上海集成電路研發(fā)中心達(dá)芬奇培訓(xùn)
北京瑞志合眾科技WINDOWS驅(qū)動培訓(xùn)
江蘇金智科技股份FPGA高級培訓(xùn)
中國重工第710研究所FPGA高級培訓(xùn)
蕪湖伯特利汽車安全系統(tǒng)DSP培訓(xùn)
廈門中智能軟件技術(shù)Android培訓(xùn)
上海科慢車輛部件系統(tǒng)EMC培訓(xùn)
中國電子科技集團(tuán)第五十研究所，軟件無線電培訓(xùn)
蘇州浩克系統(tǒng)科技FPGA培訓(xùn)
上海申達(dá)自動防范系統(tǒng)FPGA培訓(xùn)
四川長虹佳華信息MTK培訓(xùn)
公安部第三研究所--FPGA初中高技術(shù)開發(fā)培訓(xùn)以及DSP達(dá)芬奇芯片視頻、圖像處理技術(shù)培訓(xùn)
上海電子信息職業(yè)技術(shù)--FPGA高級開發(fā)技術(shù)培訓(xùn)
上海點逸網(wǎng)絡(luò)科技有限公司--3G手機(jī)ANDROID應(yīng)用和系統(tǒng)開發(fā)技術(shù)培訓(xùn)
格科微電子有限公司--MTK應(yīng)用（MMI）和驅(qū)動開發(fā)技術(shù)培訓(xùn)
南昌航空大學(xué)--fpga高級開發(fā)技術(shù)培訓(xùn)
IBM公司--3G手機(jī)ANDROID系統(tǒng)和應(yīng)用技術(shù)開發(fā)培訓(xùn)
上海貝爾--3G手機(jī)ANDROID系統(tǒng)和應(yīng)用技術(shù)開發(fā)培訓(xùn)
中國雙飛--Vxworks應(yīng)用和BSP開發(fā)技術(shù)培訓(xùn)
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上海水務(wù)建設(shè)工程有限公司--Alter/XilinxFPGA應(yīng)用開發(fā)技術(shù)培訓(xùn)
恩法半導(dǎo)體科技--AllegroCandencePCB仿真和信號完整性技術(shù)培訓(xùn)
中國計量--3G手機(jī)ANDROID應(yīng)用和系統(tǒng)開發(fā)技術(shù)培訓(xùn)
冠捷科技--FPGA芯片設(shè)計技術(shù)培訓(xùn)
芬尼克茲節(jié)能設(shè)備--FPGA高級技術(shù)開發(fā)培訓(xùn)
川奇光電--3G手機(jī)ANDROID系統(tǒng)和應(yīng)用技術(shù)開發(fā)培訓(xùn)
東華大學(xué)--Dsp6000系統(tǒng)開發(fā)技術(shù)培訓(xùn)
上海理工大學(xué)--FPGA高級開發(fā)技術(shù)培訓(xùn)
同濟(jì)大學(xué)--Dsp6000圖像/視頻處理技術(shù)培訓(xùn)
上海醫(yī)療器械高等專科學(xué)校--Dsp6000圖像/視頻處理技術(shù)培訓(xùn)
中航工業(yè)無線電電子研究所--Vxworks應(yīng)用和BSP開發(fā)技術(shù)培訓(xùn)
北京交通大學(xué)--Powerpc開發(fā)技術(shù)培訓(xùn)
浙江理工大學(xué)--Dsp6000圖像/視頻處理技術(shù)培訓(xùn)
臺灣雙陽科技股份有限公司--MTK應(yīng)用（MMI）和驅(qū)動開發(fā)技術(shù)培訓(xùn)
滾石移動--MTK應(yīng)用（MMI）和驅(qū)動開發(fā)技術(shù)培訓(xùn)
冠捷半導(dǎo)體--Linux系統(tǒng)開發(fā)技術(shù)培訓(xùn)
奧波--CortexM3+uC/OS開發(fā)技術(shù)培訓(xùn)
迅時通信--WinCE應(yīng)用與驅(qū)動開發(fā)技術(shù)培訓(xùn)
海鷹醫(yī)療電子系統(tǒng)--DSP6000圖像處理技術(shù)培訓(xùn)
博耀科技--Linux系統(tǒng)開發(fā)技術(shù)培訓(xùn)
華路時代信息技術(shù)--VxWorksBSP開發(fā)技術(shù)培訓(xùn)
臺灣歐澤科技--iPhone開發(fā)技術(shù)培訓(xùn)
寶康電子--AllegroCandencePCB仿真和信號完整性技術(shù)培訓(xùn)
上海天能電子有限公司--AllegroCandencePCB仿真和信號完整性技術(shù)培訓(xùn)
上海亨通光電科技有限公司--andriod應(yīng)用和系統(tǒng)移植技術(shù)培訓(xùn)
上海智搜文化傳播有限公司--Symbian開發(fā)培訓(xùn)
先先信息科技有限公司--brew手機(jī)開發(fā)技術(shù)培訓(xùn)
鼎捷集團(tuán)--MTK應(yīng)用（MMI）和驅(qū)動開發(fā)技術(shù)培訓(xùn)
傲然科技--MTK應(yīng)用（MMI）和驅(qū)動開發(fā)技術(shù)培訓(xùn)
中軟國際--Linux系統(tǒng)開發(fā)技術(shù)培訓(xùn)
龍旗控股集團(tuán)--MTK應(yīng)用（MMI）和驅(qū)動開發(fā)技術(shù)培訓(xùn)
研祥智能股份有限公司--MTK應(yīng)用（MMI）和驅(qū)動開發(fā)技術(shù)培訓(xùn)
羅氏診斷--Linux應(yīng)用開發(fā)技術(shù)培訓(xùn)
西東控制集團(tuán)--DSP2000應(yīng)用技術(shù)及DSP2000在光伏并網(wǎng)發(fā)電中的應(yīng)用與開發(fā)
科大訊飛--MTK應(yīng)用（MMI）和驅(qū)動開發(fā)技術(shù)培訓(xùn)
東北農(nóng)業(yè)大學(xué)--IPHONE蘋果應(yīng)用開發(fā)技術(shù)培訓(xùn)
中國電子科技集團(tuán)--Dsp2000系統(tǒng)和應(yīng)用開發(fā)技術(shù)培訓(xùn)
中國船舶重工集團(tuán)--Dsp2000系統(tǒng)開發(fā)技術(shù)培訓(xùn)
晶方半導(dǎo)體--FPGA初中高技術(shù)培訓(xùn)
肯特智能儀器有限公司--FPGA初中高技術(shù)培訓(xùn)
哈爾濱大學(xué)--IPHONE蘋果應(yīng)用開發(fā)技術(shù)培訓(xùn)
昆明電器科學(xué)研究所--Dsp2000系統(tǒng)開發(fā)技術(shù)
奇瑞汽車股份--單片機(jī)應(yīng)用開發(fā)技術(shù)培訓(xùn)
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