Big Data Analytics

This training will provide you with a thorough methodological and practical coverage of state-of-art data mining techniques (e.g. from statistics, machine learning and artificial intelligence) that identify unexpected patterns, structures, models or trends in data to make crucial decisions. This course will provide you with practical data mining experience and throughout the course illustrations of the concepts and methods will be given. You will be able to apply what you have learnt within a state-of-art data-mining workbench using benchmark data.

The naïve and blind ‘black-box’ use of data mining software packages has its obvious pitfalls and can, and probably often does, lead to practically worthless results and misleading conclusions. Data mining is easy to do badly. It is therefore important to understand enough of the characteristics of the underlying data mining methodologies (both their advantages and their pitfalls) to be able to make an informed choice about which data mining methods to use and also to be able to critically appraise their own results and those of others. In this course we will apply a “white-box” methodology, which emphasises an understanding of the algorithmic and statistical model structures underlying the ‘black-box’ software.

Instruction proceeds from tangible examples to theory – from the big picture, or ‘whole’, to details, or ‘parts’ – and from a conceptual understanding to the ability to perform specific statistical data mining tasks.

In summary, this 3-day workshop divides class time between lectures covering, in a software-vendor independent way, the methodological aspects and practical applications of statistical data mining, and between hands-on practice, where you will have a chance to try on your own the methods learnt in the course within a state-of-art data mining workbench using data.

Key benefits of attending this workshop

• Demystification of big data, data science and statistics
• Comprehend key concepts and why they are important to enable data-driven decision-making success
• Understand the role and applicability of data science (a rebranding of data mining)
• Master data preprocessing
• Software-vendor independent overview of the statistical data mining terminology and methods, resources and practical issues.
• Hands-on practice within a state-of-art data mining workbench.
• Get guidance from a renowned Swiss course facilitator with more than 15 years of real-world experience

• Data Scientist

• Data Analyst

• Data Engineer

• Business Analyst

• Business Intelligence Analyst

• Big Data Analytics Specialist

• Market Research Analyst

• Business Intelligence and Performance Management Consultant

• Statistician

From the following industries:

• Agriculture

• Banking & Financial Services

• Critical Manufacturing

• Education

• Emergency Services

• Gaming

• Government

• Information & Telecommunications 

• Insurance

• Media + Communications

• Oil & Gas / Energy

• Pharmaceutical

• Public Health

• Retail + Consumer

• Technology

• Transportation

• Utilities

Professor Dr. Kuonen, PhD in Statistics and CStat PStat CSci, founded in 2001 Statoo Consulting, a software-vendor independent Swiss consulting firm, where he serves as CEO and CAO. He has extensive experience in applying statistical engineering, statistical thinking, statistics, data science – a rebranding of data mining – and big data analytics within large and small companies in Switzerland and throughout Europe. This resulted in fruitful collaborations helping companies to enhance their business and to make optimised data-driven decisions by the use of statistics and data science. One of his credos with respect to data-driven decision making is the wish to fill the gap between academic research and professional management practice.

In January 2016 Professor Dr. Kuonen, CStat PStat CSci, was ranked 22th within Maptive’s global `Top 100 Big Data Experts to Follow in 2016’ list, in February 2016 he was ranked 12th within Onalytica’s global `Big Data 2016: Top 100 Influencers and Brands' list, 29th within Onalytica’s global `Internet of Things (IoT) 2016: Top 100 Influencers and Brands' list and 45th within Onalytica’s global `Artificial Intelligence and Machine Learning 2016: Top 100 Influencers and Brands' list.

In addition, he is also Adjunct Professor of Data Science at the `Geneva School of Economics and Management’ (GSEM) of the University of Geneva, Switzerland.

Hands-On Practice:

• Data mining practicals with Dell Statistica Data Miner on unsupervised methods (‘class discovery’)
• Data mining practicals with Dell Statistica Data Miner on supervised methods (‘class prediction’)

Interested in partnering with us?

Please contact us to discuss your requirements.
Click here to get in touch with us.

Workshop Venue:

M Hotel Singapore

81, Anson Road

Singapore 079908

T: (65) 6224 1133

F: (65) 6224 3173

Website: www.m-hotel.com

For room reservations, please contact:

Priscilla Pey

Assistant Director of Sales

M Hotel Singapore

D: (65) 6500 6202

M: (65) 9339 3854

E: priscilla.pey@millenniumhotels.com

Industry Insights 1.0:

Download this presentation slides to find out how can we demystify the hype of "Big Data" and "Data Science" and also to understand what distinguishes data science from statistics.