The Data Science & Big Data Overview | Tools, Tech & Modern Roles in the Data-Driven Enterprise is an introductory level course that introduces the entire multi-disciplinary Data Science team to the many evolving and related terms, with focus on Big Data, Data Science, Predictive Analytics, Artificial Intelligence, Data Mining, Data Warehousing. The overview explores the current state of the art and science, the major components of a modern data science infrastructure, team roles and responsibilities, and level-setting realistic possible outcomes for your investment. This goal of this course is to provide students with a baseline understanding of core concepts and technologies to a conversant level.
This course provides a high-level view of a variety of core, current data science related technologies, strategies, skillsets, initiatives and supporting tools in common business enterprise practices. This list covers a general range of topics current to the time of course distribution. We will collaborate with your team to refine level of depth of coverage, understand areas of greater importance to your team, where you would like to add demos, etc.
Students will explore:
- Foundations: Grids & Virtualization; SOA, ESB / EMB, The Cloud
- The Hadoop Ecosystem: HDFS; Resource Navigators, MapReduce, Spark, Distributions
- Big Data, NOSQL, and ETL
- ETL: Exchange, Transform, Load
- Handling Data & a Survey of Useful tools
- Enterprise Integration Patterns and Message Busses
- Developing in Hadoop Ecosystem: R, Python, Java, Scala, Pig, and BPMN
- Artificial Intelligence and Business Systems
- Who’s on the Team? Evolving Roles and Functions in Data Science
- Growing your Infrastructure
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional Big Data / Data Science, Hadoop, programming, analytics, Python/R, and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This introductory-level / primer course is an overview intended for Business Analysts, Data Analysts, Data Architects, DBAs, Network (Grid) Administrators, Developers or anyone else in the data science realm who need to have a baseline understanding of some of the core areas of modern Data Science technologies, practices and available tools.
Attendees should have prior exposure to Enterprise Information Technology. As well as familiarity with Relational Databases.
Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most.
- Grids and Virtualization
- Service-Oriented Architecture
- Enterprise Service Bus
- Enterprise Message Bus
- The Cloud
The Hadoop Ecosystem
- HDFS: Hadoop Distributed File System
- Resource Negotiators: YARN, Mesos, and Spark; ZooKeeper
- Hadoop Map/Reduce
- Hadoop Ecosystem Distributions: Cloudera, Hortonworks, OpenSource
Big Data, NOSQL, and ETL
- Big Data vs. RDBMS
- NOSQL: Not Only SQL
- Relational Databases: Oracle, MariaDB, DB/2, SQL Server, PostGreSQL
- Key/Value Databases: JBoss Infinispan, Terracotta, Dynamo, Voldemort
- Columnar Databases: Cassandra, HBase, BigTable
- Document Databases: MongoDB, CouchDB/CouchBase
- Graph Databases: Giraph, Neo4J, GraphX
- Apache Hive
- Common Data Formats
- Leveraging SQL and SQL variants
ETL: Exchange, Transform, Load
- Data Ingestion, Transformation, and Loading
- Exporting Data
- Sqoop, Flume, Informatica, and other tools
Enterprise Integration Patterns and Message Busses
- Enterprise Integration Patterns: Apache Camel and Spring Integration
- Enterprise Message Busses: Apache Kafka, ActiveMQ, and other tools
An Overview of Developing in Hadoop Ecosystem
- Languages: R, Python, Java, Scala, Pig, and BPMN
- Libraries and Frameworks
- Development, Testing, and Deployment
Exploring Artificial Intelligence and Business Systems
- Artificial Intelligence: Myths, Legends, and Reality
- The Math
- Clustering Algorithms, Mahout, MLLib, SciKit, and Madlib
- Business Rule Systems: Drools, JRules, Pegasus
The Modern Data Team
- Agile Data Science
- NOSQL Data Architects and Administrators
- Grid Administrators
- Business and Data Analysts
- Evolving your Team
- Growing your Infrastructure
Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable).