/

 

 

 

 
  IC培训
   
 
Big Data Business Intelligence for Criminal Intelligence Analysis培训

 
  班级规模及环境--热线:4008699035 手机:15921673576( 微信同号)
      每个班级的人数限3到5人,互动授课, 保障效果,小班授课。
  上间和地点
上课地点:【上海】:同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【北京分部】:北京中山学院/福鑫大楼 【深圳分部】:电影大厦(地铁一号线大剧院站)/深圳大学成教院 【成都分部】:领馆区1号(中和大道) 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【广州分部】:广粮大厦 【西安分部】:协同大厦
最近开间(周末班/连续班/晚班):2018年3月18日
  实验设备
    ◆小班教学,教学效果好
       
       ☆注重质量☆边讲边练

       ☆合格学员免费推荐工作
       ★实验设备请点击这儿查看★
  质量保障

       1、培训过程中,如有部分内容理解不透或消化不好,可免费在以后培训班中重听;
       2、课程完成后,授课老师留给学员手机和Email,保障培训效果,免费提供半年的技术支持。
       3、培训合格学员可享受免费推荐就业机会。☆合格学员免费颁发相关工程师等资格证书,提升职业资质。专注高端技术培训15年,端海学员的能力得到大家的认同,受到用人单位的广泛赞誉,端海的证书受到广泛认可。

课程大纲
 
  • Day 01
    =====
    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
    =====
    Day 02
    =====
    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
    =====
    Day 03
    =====
    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
    =====
    Day 04
    =====
    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
    =====
    Day 05
    =====
    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
端海教育实验设备
android开发板
linux_android开发板
fpga图像处理
fpga培训班*
 
本部份程部分实验室实景
端海实验室
实验室
端海培训优势
 
  合作伙伴与授权机构



Altera全球合作培训机构



诺基亚Symbian公司授权培训中心


Atmel公司全球战略合作伙伴


微软全球嵌入式培训合作伙伴


英国ARM公司授权培训中心


ARM工具关键合作单位
  我们培训过的企业客户评价:
    端海的andriod系统与应用培训完全符合了我公司的要求,达到了我公司培训的目的。特别值得一提的是授部份讲师针对我们公司的开发的项目专门提供了一些很好程序的源代码,基本满足了我们的项目要求。
——上海贝尔,李工
    端海培训DSP2000的老师,上部份思路清晰,口齿清楚,由浅入深,重点突出,培训效果是不错的,
达到了我们想要的效果,希望继续合作下去。
——中国电子科技集团技术部主任马工
    端海的FPGA培训很好地填补了高校FPGA培训空白,不错。总之,有利于学生的发展,有利于教师的发展,有利于部份程的发展,有利于社会的发展。
——上海电子学院,冯老师
    端海给我们公司提供的Dsp6000培训,符合我们项目的开发要求,解决了很多困惑我们很久的问题,与端海的合作非常愉快。
——公安部第三研究所,项目部负责人李先生
    MTK培训-我在网上找了很久,就是找不到。在端海居然有MTK驱动的培训,老师经验很丰富,知识面很广。下一个还想培训IPHONE苹果手机。跟他们合作很愉快,老师很有人情味,态度很和蔼。
——台湾双扬科技,研发处经理,杨先生
    端海对我们公司的iPhone培训,实验项目很多,确实学到了东西。受益无穷啊!特别是对于那种正在开发项目的,确实是物超所值。
——台湾欧泽科技,张工
    通过参加Symbian培训,再做Symbian相关的项目感觉更加得心应手了,理论加实践的授部份方式,很有针对性,非常的适合我们。学完之后,很轻松的就完成了我们的项目。
——IBM公司,沈经理
    有端海这样的DSP开发培训单位,是教育行业的财富,听了他们的部份,茅塞顿开。
——上海医疗器械高等学校,罗老师
  我们最新培训过的企业客户以及培训的主要内容:
 

一汽海马汽车DSP培训
苏州金属研究院DSP培训
南京南瑞集团技术FPGA培训
西安爱生技术集团FPGA培训,DSP培训
成都熊谷加世电气DSP培训
福斯赛诺分析仪器(苏州)FPGA培训
南京国电工程FPGA培训
北京环境特性研究所达芬奇培训
中国科学院微系统与信息技术研究所FPGA高级培训
重庆网视只能流技术开发达芬奇培训
无锡力芯微电子股份IC电磁兼容
河北科学院研究所FPGA培训
上海微小卫星工程中心DSP培训
广州航天航空POWERPC培训
桂林航天工学院DSP培训
江苏五维电子科技达芬奇培训
无锡步进电机自动控制技术DSP培训
江门市安利电源工程DSP培训
长江力伟股份CADENCE培训
爱普生科技(无锡)数字模拟电路
河南平高电气DSP培训
中国航天员科研训练中心A/D仿真
常州易控汽车电子WINDOWS驱动培训
南通大学DSP培训
上海集成电路研发中心达芬奇培训
北京瑞志合众科技WINDOWS驱动培训
江苏金智科技股份FPGA高级培训
中国重工第710研究所FPGA高级培训
芜湖伯特利汽车安全系统DSP培训
厦门中智能软件技术Android培训
上海科慢车辆部件系统EMC培训
中国电子科技集团第五十研究所,软件无线电培训
苏州浩克系统科技FPGA培训
上海申达自动防范系统FPGA培训
四川长虹佳华信息MTK培训
公安部第三研究所--FPGA初中高技术开发培训以及DSP达芬奇芯片视频、图像处理技术培训
上海电子信息职业技术学院--FPGA高级开发技术培训
上海点逸网络科技有限公司--3G手机ANDROID应用和系统开发技术培训
格科微电子有限公司--MTK应用(MMI)和驱动开发技术培训
南昌航空大学--fpga高级开发技术培训
IBM公司--3G手机ANDROID系统和应用技术开发培训
上海贝尔--3G手机ANDROID系统和应用技术开发培训
中国双飞--Vxworks应用和BSP开发技术培训

 

上海水务建设工程有限公司--Alter/XilinxFPGA应用开发技术培训
恩法半导体科技--AllegroCandencePCB仿真和信号完整性技术培训
中国计量学院--3G手机ANDROID应用和系统开发技术培训
冠捷科技--FPGA芯片设计技术培训
芬尼克兹节能设备--FPGA高级技术开发培训
川奇光电--3G手机ANDROID系统和应用技术开发培训
东华大学--Dsp6000系统开发技术培训
上海理工大学--FPGA高级开发技术培训
同济大学--Dsp6000图像/视频处理技术培训
上海医疗器械高等专科学校--Dsp6000图像/视频处理技术培训
中航工业无线电电子研究所--Vxworks应用和BSP开发技术培训
北京交通大学--Powerpc开发技术培训
浙江理工大学--Dsp6000图像/视频处理技术培训
台湾双阳科技股份有限公司--MTK应用(MMI)和驱动开发技术培训
滚石移动--MTK应用(MMI)和驱动开发技术培训
冠捷半导体--Linux系统开发技术培训
奥波--CortexM3+uC/OS开发技术培训
迅时通信--WinCE应用与驱动开发技术培训
海鹰医疗电子系统--DSP6000图像处理技术培训
博耀科技--Linux系统开发技术培训
华路时代信息技术--VxWorksBSP开发技术培训
台湾欧泽科技--iPhone开发技术培训
宝康电子--AllegroCandencePCB仿真和信号完整性技术培训
上海天能电子有限公司--AllegroCandencePCB仿真和信号完整性技术培训
上海亨通光电科技有限公司--andriod应用和系统移植技术培训
上海智搜文化传播有限公司--Symbian开发培训
先先信息科技有限公司--brew手机开发技术培训
鼎捷集团--MTK应用(MMI)和驱动开发技术培训
傲然科技--MTK应用(MMI)和驱动开发技术培训
中软国际--Linux系统开发技术培训
龙旗控股集团--MTK应用(MMI)和驱动开发技术培训
研祥智能股份有限公司--MTK应用(MMI)和驱动开发技术培训
罗氏诊断--Linux应用开发技术培训
西东控制集团--DSP2000应用技术及DSP2000在光伏并网发电中的应用与开发
科大讯飞--MTK应用(MMI)和驱动开发技术培训
东北农业大学--IPHONE苹果应用开发技术培训
中国电子科技集团--Dsp2000系统和应用开发技术培训
中国船舶重工集团--Dsp2000系统开发技术培训
晶方半导体--FPGA初中高技术培训
肯特智能仪器有限公司--FPGA初中高技术培训
哈尔滨大学--IPHONE苹果应用开发技术培训
昆明电器科学研究所--Dsp2000系统开发技术
奇瑞汽车股份--单片机应用开发技术培训


 

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