IDA, International Institute of Data & Analytics

Benefits: Our purposes of building, enhancing analytics education and training are multi-fold:

For individuals: we help you build your theoretical foundation of understanding why you do it in that way, your capabilities of how you crackle the nuggets, and what to do to discover valuable insights hidden in data;
For corporates: we help you understand the challenges, gaps and problems explicitly or implicitly appear in your organisation, including technology, infrastructure, capability, process, performance and interactions. More importantly, we can help you build your organisational assets, team, technology, best practices and strengths in the increasingly competitive business markets.

Strengths: Key people in IDA have unique experience that cannot be available from other training channels and universities, such as

Deep foundation of data science theories and tools, including data science, statistics, data mining, machine learning, and optimisation, built on their research and innovation;
Best practices of big data analytics in many large organisations and domains and on many typical and challenging business problems, such as risk management, fraud detection, complex behavior analysis, sentiment analysis, social media analysis, financial service compliance and decision-support.
Experienced lecturers who teach courses at different levels and organize seminars and workshops for various groups of end users.
Capabilities to synergize innovation (why), implementation (what) and process (how) for the best possible solutions and performance.

Corporate Training

Objectives: Corporate training is customized to help our clients to build their own understanding and capabilities of big data analytics for their own problem-solving and evidence-based decision-making. Differentiating from the traditional courses offered at universities or by commercial entities, our courses are solutions and practices-based which synergizes the relevant theories, experience, practices and tools towards best possible output.

Format:A course is a journey of addressing a real business problem through demonstrating a systematic view to learners and conveying a good understanding and matching of specific data characteristics, suitable approaches, operable solutions, and meaningful outcomes. As a result, trainees will master not only how to use an analytic method but also why, when and where to use it.

Example: For example, in one of our subjects: "credit card fraud detection and prevention", learners have chance to explore the following questions:

What data can be used?
What variables can be used?
What variables need to be derived/re-constructed?
What extra variables can be mined?
What kind of domain knowledge needs to be converted and how to achieve it?
How to select the effective variables?
How to choose the appropriate methods and models?
How to combine different approaches?
How to undertake training and evaluate results?
How to tune models and refine parameters?
How to refresh a model?
How to treat the fresh data and the historical data?

Short Courses

Course Objectives:To bridge the gaps in the capability set available in the market and requirements for big data analytics, we customize and offer short courses to help you build your compentency and confidence for exploring big data.

Course Levels: Our courses cover three levels:

Foundational courses: focus on theories and foundation for advanced analytics, big data, data mining, machine learning, etc.
Technical courses: build your skill sets for analytics tools, algorithms, models, programming and evaluation.
Management courses: share methodologies, skills, experience and process for managing and evaluating analytics projects, and communication skills for converting analytics outcomes to business and decision-makers.

Course Categories: Our courses cover areas and business needs including but not limited to:

Analytics project management
Analytics methodologies
Data transformation and ETL
Feature selection, extraction, mining and optimisation
Data mining and tools
Machine learning and tools
Analytics programming
Analytics algorithms and models
Analytics optimisation and decision-support
Trends of analytics and big data technologies
Best practices of business analytics
Analytics infrastructure and tools
Analytics evaluation and assurance