5 Technologies + 5 steps to get organisations on the Big Data road

This course is designed to provide participants, with no prior exposure to Big Data (BD), with the knowledge and practical skills to start using Big Data in their organisations.

Event Start: Thu 04 May 08:30 AM AEST
Event Finish: Fri 05 May 05:15 PM AEST

Completed

Registration End Date: Tue 02 May 05:00 PM

Simply Big Data (5 + 5) ™ 5 Technologies + 5 Steps to Get Organisations on the Big Data Road Duration: 2 days Overview This course is designed to provide participants, with no prior exposure to Big Data (BD), with the knowledge and practical skills to start using Big Data in their organisations. Initially the course covers four (4) of the most popular technologies for processing Big Data –Apache Hadoop, Apache Spark, Apache Hive and MongoDB. The 5th technology, KNIME is introduced, to show how Machine Learning is used to obtain real business insights. A distinguishing feature of the course is its practical aspect. At the end of each module there is a practical exercise designed to reinforce the concepts of the module and to give participants a chance to “get their hands dirty”. The exercises are either scenarios where participants work in groups or individual tasks executed within a Virtual Machine (VM). Most importantly, the last 3 hours of the course are devoted to creating a real Big Data pilot project, by progressing through the following stages: • define the business need • discover relevant datasources • determine available and required resources/expertise, including costing • select and implement a pilot solution • produce results for the business. At the start of the course, each participant will receive a USB with a fully configured VM that will be used for the practical work throughout the course. Students will be allowed to keep the VMs after the course, so that they refer to the exercises in their day-to-day work and, when they feel more confident, to modify and enhance them to be used in their audit engagements. Additionally, each student will receive a full set of course slides and the complete descriptions, including answers and results, of all scenarios and exercises. Structure Day 1 8:45 – 9:00 Registration, Introductions and Housekeeping Big Data - Introduction • Overview • Big Data Success Stories • Big Data Projects – Success Factors • Practical Work – role-playing scenario Big Data Sources • Structured Data • Semi-structured Data 10:30 – 10:45 Morning Break Big Data Sources (cont.) • Unstructured Data • Practical work – accessing, downloading and pre-processing datasets 12:15 – 13:00 Lunch Big Data Tools • Apache Hadoop o Overview o Components – YARN, HDFS, Map Reduce, etc. o Practical Work – Analysis of a retail dataset Big Data Tools (cont.) • Apache Hive o Overview o Using Apache Hive and HiveQL o Practical Work – Analysis of a retail dataset 15:30 – 15:45 Afternoon Break Big Data Tools (cont.) • Apache Spark o Overview o Components – Core, MLib, GraphX, Spark SQL, Streaming o Practical Work – Analysis of a Payroll dataset Re-cap on Day 1, Comments, Feedback 17:15 End of Day 1 Day 2 8:45 – 9:00 Re-cap on Day 1 Big Data Tools (cont.) • MongoDB o Overview o Document Databases o Practical Work – Analysing an Accounts Payable dataset 10:30 – 10:45 Morning Break Machine Learning/Data Mining • Overview • Types of Machine Learning/Data Mining o Supervised Learning o Unsupervised Learning o Ensemble Learning • Use cases for Machine Learning 12:15 – 13:00 Lunch Machine Learning/Data Mining (cont.) • Practical work o Introduction to KNIME o Practical Work - Discovering relationships within datasets Putting Big Data and Machine Learning Together (Creating a Big Data pilot) • Define the business need • Discover/determine relevant datasources 15:30 – 15:45 Afternoon Break Putting Big Data and Machine Learning Together (Creating a Big Data pilot) (cont.) • Determine available and required resources/expertise, including costing • Select and implement a pilot solution • Produce results for the business Re-cap on Day 2, Course Comments and Feedback Simply Big Data (5 + 5) ™ Course Overview Copyright © 2017 Meta Business Systems Pty Ltd 17:15 End of Course

Contact Details

Kylie Barringer
Events Manager
kylie.barringer@acs.org.au

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CPD HOURS 15	SKILL
Speaker	Mario Bojilov Mario Bojilov, Chief Data Strategist, Meta Business Systems MARIO BOJILOV is a Certified Information Systems Auditor, a Lecturer in Accounting Information Systems and has worked in the area of Data Analytics since 1994. Mario founded Meta Business Systems in 2004 of which he is the current Chief Data Strategist. The company is focused on performance monitoring, business intelligence and social media analytics. A number of Queensland-based organisations, such as Griffith University, Brisbane City Council, Queensland Urban Utilities and QUT currently use solutions designed and implemented by Mario and Meta Business Systems. Mario Bojilov holds a Master of Engineering Science Degree from the University of Queensland and a Graduate Diploma of Applied Finance and Investment from the Financial Services Institute of Australasia (FINSIA). Mario has appeared as a presenter and lecturer at various professional and academic bodies, such as Institute of Internal Auditors (IIA), Information Systems Audit and Control Association (ISACA), University of Queensland and University of Southern Queensland. Further information about Mario Bojilov can be found on LinkedIn and Twitter. LinkedIn: linkedin/in/mariobojilov Twitter: @mariogb Show More

Completed

where
ACS Qld Office
Level 7/333 Adelaide St
Brisbane QLD 4000

when
Event Start:
Thu 04 May 08:30 AM AEST

Event Finish:
Fri 05 May 05:15 PM AEST

event price
Early Member: $1050.00
Member: $1150.00

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5 Technologies + 5 steps to get organisations on the Big Data road

Contact Details

Share this event:

CPD HOURS

SKILL

Mario Bojilov

Mario Bojilov, Chief Data Strategist, Meta Business Systems

where

when

event price

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