Course Objectives
Certified Big Data and Data Analytics Practitioner - Virtual Learning
Course Objectives
By the end of the course, participants will be able to:
Design big data implementation plans and create strategies for data driven solutions
Explain the challenges of big data and traditional technologies like Excel
Discuss the main challenges and advantages of Hadoop ecosystem and other big data distributed architectures
Demonstrate and discuss key technologies for big data storage and compute, such as PostgreSQL and MongoDB
Discuss popular machine learning algorithms and the importance of ethics in data analytics and artificial intelligence
Deliver an architectural diagram for analytics focused use cases
Target Audience
This course is ideal for data analysts, data engineers, data scientists, as well as technically-inclined management and administrative professionals seeking to understand big data strategies, technologies and use cases. Recommended pre-knowledge includes basic programming experience and analyzing data in python, knowledge of basic database technologies, and awareness of analytics driven business initiatives.
Target Competencies
Big data hands-on labs
Big Data analytics structures and technologies
Ethics and integrity for big data analytics
Big data storage and computer system implementation
Architecture diagram design
Course Outline
Introduction to Big Data AnalyticsWhat is Big Data?5 “V’s” of big dataHow big data relates to data analyticsBig data impact on technologiesOpen source revolutionKey big data concepts and data typesText, audio, imagesBig data professional rolesHow can big data projects meet organizational needsBig data Examples:Netflix, LinkedIn, Facebook, Google, Orbitz, Dell, others.Best practices in project designAssessing the current state of your organization
Storing Big DataBig data architectures and paradigmsThe Hadoop EcosystemOverview of HadoopHadoop Distributed File System (HDFS)Massively parallel processing (MPP) versus distributed in-memory applicationsRDBMSs vs NoSQL DBsPostgreSQL, MongoDB, CassandraStreaming dataData-warehousing versus Data Mart
Computing Big DataHow to access big dataRole of cloud computingData movement riskNetworking and co-locationBig data extract, transform, load (ETL)Big data compute technologiesHadoop continuedMapReduce and beyondDistributed computeHigh performance clustersSparkStreaming: Storm, Spark structured streamingOther big data technologies: Kafka, etc.Cloud applications for big data
Big Data ProjectsBasics of data analyticsRoles and objectivesKey math and statistics conceptsSupervised versus UnsupervisedKey technologies and applicationsGetting Value out of Big Data5 P’s of data scienceImportance of EthicsProgrammability
Architecting Big Data SolutionsIdentify analytical opportunitiesDefine and assess the problemDescribe the impact and use of data to address the problemIdentify potential data sourcesBrainstorm an analytics strategy to implementStorage and computeIdentify a cloud environment strategyBrainstorm key storage systems and compute environments