Join our specialized training program to gain expertise in identifying and utilizing statistical algorithms and data models for hypothesis testing and pattern derivation. Acquire knowledge of advanced computational methods, analytics platforms, and statistical modeling techniques.
Develop coding skills in programming languages and learn to diagnose and propose changes to analytical models. Enhance your ability to draw trends and insights from data analysis to support decision-making. Attend our program to unlock the power of computational modeling and statistical analysis in solving complex problems.
Training Duration: 10 Days
- Certificate Of Completion Available
- Group Private Class
- VILT Class Available
- SBL-Khas Claimable
This programme is created specifically for learners who wish to identify and utilise appropriate statistical algorithms and data models to test hypotheses and derive patterns or solutions.
- Types of algorithms and advanced computational methods
- Range and application of various statistical algorithms
- Range and application of various types of data models
- Usage of analytics platforms and tools
- Statistical modelling techniques
- Coding languages for programming of algorithms and signals
- Potential reasons for unintended outcomes
- Identify appropriate statistical algorithms and data models to test hypotheses or theories
- Use appropriate analytics platforms and analytical tools given specific analytics and reporting requirements
- Utilise a range of statistical methods and analytics approaches to data
- Conduct statistical modelling of data to derive patterns and/or solutions
- Perform coding and configuration of software agents or programs based on a selected model or algorithm
- Conduct tests on the actions taken and outcomes to assess effectiveness of the model
- Diagnose unintended outcomes produced by analytical models
- Propose changes or updates to the model or algorithms applied
- Implement changes to the coding and configuration of software agents or programs
- Draw relevant trends and insights from data analysis to support decision
MODULE 1: INTRODUCTION TO COMPUTATIONAL MODELLING FOR BI PROFESSIONALS
Topics:- Introduction to advanced computational methods
- Discussion of analytic use cases
- Characteristics of statistical algorithm and data model requirements
- Assumptions and limitations of computational methods
Mapped to:
- K1 Types of algorithms and advanced computational methods
- K2 Range and application of various statistical algorithms
- K3 Range and application of various types of data models
- A1 Identify appropriate statistical algorithms and data models to test hypotheses or theories
- A3 Utilise a range of statistical methods and analytics approaches to data
Rationale for Sequencing of the Units
- The first learning unit introduces learners to advanced computational methods. Learners are taught the various types of statistical algorithms, the characteristics and assumptions and limitations of computational methods. There is a discussion of analytic use cases, e.g. diagnostic analytics, clustering, prediction, network analysis, text analytics, image analytics, video analytics, modelling and simulation.
MODULE 2: TECHNOLOGY STRATEGY
Topics:- Trends in analytic development and production platform
- Technology strategy for development and operation of analytics
Mapped to:
- K4 Usage of analytics platforms and tools
- A2 Use appropriate analytics platforms and analytical tools given specific analytics and reporting requirements
Rationale for Sequencing of the Units
- Subsequently, the next learning unit teaches learners technology strategy by first going through trends in analytic development and platform strategy.
- The analytics platforms and analytical tools learners introduced to learners include R, SAS, Gretl, Orange.
MODULE 3: ADVANCED COMPUTATIONAL MODELLING DEVELOPMENT
Topics:- Introduction to Statistical Modelling
- Introduction to Machine Learning Modelling
- Introduction to Time Series Forecasting Modelling
Mapped to:
- K5 Statistical modelling techniques
- A4 Conduct statistical modelling of data to derive patterns and/or solutions
- A6 Conduct tests on the actions taken and outcomes to assess effectiveness of the model
- A10 Draw relevant trends and insights from data analysis to support decisions
Rationale for Sequencing of the Units
- In the next learning unit, the learners move to the next step in the sequence where they are brought through statistical modelling techniques for machine learning, e.g.
- Supervised machine learning –decision tree
- Unsupervised machine learning –clustering
- Learners work on exercises to practice Decision Tre, K-mean clustering, hierarchical clustering.
Range of Application:
- Machine Learning
MODULE 4: ADVANCED COMPUTATIONAL MODELLING CODING
Topics:- Data processing
- Data training and testing split
- Data sample balancing (optional)
- Model setting
- Model result report
Mapped to:
- K6 Coding languages for programming of algorithms and signals
- A5 Perform coding and configuration of software agents or programs based on a selected model or algorithm
- A8 Propose changes or updates to the model or algorithms applied
- A9 Implement changes to the coding and configuration of software agents or programs
Rationale for Sequencing of the Units
- In the next learning unit, the learners are taught data processing, data training and testing split, data sample balancing (optional), model setting and model result report.
- The software agents or programs learners use include Python, R, Gretl, JMP, SAS, Orange.
MODULE 5: POTENTIAL RISKS OF ANALYTICS MODEL
Topics:- Unintended outcomes produced by analytical models and their potential reasons
Mapped to:
- K7 Potential reasons for unintended outcomes
- A7 Diagnose unintended outcomes produced by analytical models
Rationale for Sequencing of the Units
- The last learning unit teaches learners on potential risks of analytics models by looking at unintended outcomes, the potential reasons for unintended outcomes and how to diagnose various unintended outcomes.