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Murrumbeena 
Shiivong - Prof databases - Murrumbeena
$22/h
This teacher is currently unavailable
Profile Verified
Response Time 24h
Lesson formats available..
  • One-to-one
Lessons can be held..
Subjects taught
  • Databases
  • SQL
  • MySQL
  • Data Analysis
Levels
  • All Levels

Monash Data Science Graduate who can also teach Acoustic and Bass Guitar

Methodology

I believe in understanding the topic in a holistic way before deep-diving into the individual bits. I like to make my classes as much fun and interactive as possible, nobody wants a boring teacher who can't make a joke here and there.

Background

I am a Data Analyst with a combined experience of over 20 months at KPMG and Deloitte. I have taken several online classes in Data Science at Monash. I play Bass/Electric/Acoustic guitars in my free time and also love discussing International Politics. Also a die-hard FC Barcelona fan.

Rates

Rate for online lessons : $22/h
Rate for 5 hours of lessons : $20
Rate for 10 hours of lessons : $18
Lesson formats available..
One-to-one
Lessons can be held..
at their place
Subjects taught
  • Databases
  • SQL
  • MySQL
  • Data Analysis
Levels
  • All Levels

Shiivong's CV

Monash University, Melbourne Jul 2018 – May 2020

Master of Data Science Weighted Average Mark: 72.5 • Awarded FIT Merit Scholarship

VIT University, Chennai Jul 2013 – Jul 2017

Bachelor of Computer Science CGPA: 7.97

• Published research papers in IEEE Xplore and IJCA in Data Mining domain • Developed a Smart Waste Management System using IOT and R Shiny Professional Employment

Deloitte Australia Jan 2020 – Feb 2020
Data Analyst Intern

• Collaborated closely within the Data Modernization team by developing SSAS Cubes • Gained hands-on experience on Azure Cloud

KPMG India Jan 2017 – July 2018

Data Analyst

• Worked exclusively on Power BI, SSIS, SSAS and R for a major Healthcare client in BI and Data Warehousing domain • Developed a fully functional Business Process Mining application in R Shiny • Built Machine Learning Algorithms for classifying highly imbalanced data

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