* All fees are exclusive of vat
** PREMIUM - Customize your learning experience
Course Description
This interactive, applications-driven 5-day course will highlight the added value that data analytics can offer a professional as a decision support tool in management decision making. It will show the use of data analytics to support strategic initiatives; to inform on policy information; and to direct operational decision making. The course will emphasize applications of data analytics in management practice; focus on the valid interpretation of data analytics findings; and create a clearer understanding of how to integrate quantitative reasoning into management decision making. Exposure to the discipline of data analytics will ultimately promote greater confidence in the use of evidence-based information to support management decision making.
What Do Participants Learn?
- Appreciate data analytics in a decision support role.
- Explain the scope and structure of data analytics.
- Apply a cross-section of useful data analytics.
- Interpret meaningfully and critically assess statistical evidence.
- Identify relevant applications of data analytics in practice.
Who Should Attend?
- Managers
- IT Professionals
- IT Support Staff
- Any member of an IT team involved in the delivery of IT Services.
What Will the Learning Experience Include?
Phase: 1
Introduce
- Comprehensive pre-program activities include:
- Web-based information forms & surveys completed by attendee.
- Direct consultation with the attendee about the expectations.
- During the training, participants engage in data, activities, and conversations that lead to insight and knowledge.
- Participants learn from expert trainers who have both academic and business experiences.
- Highly applicable training content & instructive activities for adding depth to training topics.
- **A half-day site visit for integrating the experience & plan next steps. Opportunities to provide connections, ideas & support.
Phase: 2
Explore & Practice
Phase: 3
Apply
- Apply & sustain the learning experience by using this ongoing support:
- To ensure participant has new skills or behavior progress.
- Optional, fee-based mentoring & coaching with the trainer.
- Training materials & additional documents (e-books, pdf files, presentations and articles)
- Evaluate your training experience by giving us feedbacks and help us to reach our organizational goals.
- Participant's Evaluation
- Trainer's Evaluation
Phase: 4
EVALUATE
Section One: Setting the Statistical Scene in Management
- Introduction; The quantitative landscape in management
- Thinking statistically about applications in management (identifying KPIs)
- The integrative elements of data analytics
- Data: The raw material of data analytics (types, quality and data preparation)
- Exploratory data analysis using excel (pivot tables)
- Using summary tables and visual displays to profile sample data
Section Two: Evidence-based Observational Decision Making
- Numeric descriptors to profile numeric sample data
- Central and non-central location measures
- Quantifying dispersion in sample data
- Examine the distribution of numeric measures (skewness and bimodal)
- Exploring relationships between numeric descriptors
- Breakdown analysis of numeric measures
Section Three: Statistical Decision Making – Drawing Inferences from Sample Data
- The foundations of statistical inference
- Quantifying uncertainty in data – the normal probability distribution
- The importance of sampling in inferential analysis
- Sampling methods (random-based sampling techniques)
- Understanding the sampling distribution concept
- Confidence interval estimation
Section Four: Statistical Decision Making – Drawing Inferences from Hypotheses Testing
- The rationale of hypotheses testing
- The hypothesis testing process and types of errors
- Single population tests (tests for a single mean)
- Two independent population tests of means
- Matched pairs test scenarios
- Comparing means across multiple populations
Section Five: Predictive Decision Making – Statistical Modeling and Data Mining
- Exploiting statistical relationships to build prediction-based models
- Model building using regression analysis
- Model building process – the rationale and evaluation of regression models
- Data mining overview – its evolution
- Descriptive data mining – applications in management
- Predictive (goal-directed) data mining – management applications
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