Course Overview
Enterprises across the globe are shifting their focus to data-driven goals and decision-making. In fact, the International Data Corporation reports that worldwide data will grow 61% to 175 zettabytes by 2025*. So, why is data science so important? Because it enables organisations to efficiently process and interpret data that can be used to make informed business decisions & drive growth, optimisation, and performance.One can learn how to process and understand data that can be used to drive better, smarter decisions within your organisation.
Course Objectives
- Create and implement business strategies leveraging data science.
- Make data-driven decisions to solve business problems using data insights.
- Demonstrate how analytics can be combined with experiments to make data-informed recommendations for business growth.
- Explain the key challenges and risks in data science projects.
- Evaluate an organisation’s data strategy and recommend ways to achieve a sustainable competitive advantage.
- Analyse organisational needs and drive business improvement through data science future trends.
Course Outcome
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- Transition into a data-centric senior management role
- Gather analytical expertise to handle greater responsibilities.
- Utilise predictive models to build effective strategies that address key issues in business operations and product quality.
- Become a leader for sustainable business growth.
- Spearhead complete ownership of key business tasks and understand underlying strategic implications.
Prerequisite
No coding is required; however, a basic knowledge of Excel would be beneficial. Industries and Functions that can benefit include
Target Audience
The programme is designed for both tech and non-tech professionals with relevant work experience:
Industries:
IT, E-Commerce, Computer Software, Finance, Marketing and Advertising, Banking, Education Management, and Management Consulting
Functions:
Engineering, Programming, Technology, General Management, Marketing, Finance, Operations, and HR Functions
Course Content
Module 1: Leveraging Data as a Competitive Edge.
- Key terminologies of data science
- Different levels of data analytics and their significance to decision-making
- Data features and insights to attain sustainable competitive advantage.
- Applications of data analytics and its role in creating new business opportunities
Module 2: Data Analytics in Action
- Analytical approach to resolve a business problem.
- Is your organisation is data-driven
- Trends in data and obtaining related insights to enhance business performance.
- Impact an organisation’s omnichannel strategies have on sales.
- How to identify appropriate data/insights
Module 3: Basic Statistics for Data Analysis
- Comparison of independent data sets to obtain insights.
- How to apply strategic decision-making using said
MODULE 4: Predictive Analytics
- Regression to analyse the strength/impact of variables.
- Predict variable impact using optimal model fit and regression effects.
- Logistic regression model to test and predict expected outcomes.
- Apply predictive analytics to organisational events to advance strengths and counter threats.
MODULE 5: Field Experiments and Causality
- Correlation and causality and their significance to enhancing business performance
- Experimentation for business problems to make effective inferences.
- Multivariate, A/B and Multi-Armed Bandit testing
- Effectiveness of using experimental design to make data-informed recommendations for business growth.
Recommendation Systems- Recommendations and Ranking
- Collaborative Filtering
- Personalized Recommendation
MODULE 6: Machine Learning Models for Data Analytics
- ML and its role in driving organisational productivity.
- Apply ML algorithms to achieve optimal analytical accuracy.
- Programme-building facets of neural networks and deep learning
- Combine analytics with experiments to produce effective business strategies.
MODULE 7: Decision Making
Decision Making Under Uncertainty- Bayesian Decision Making
Simulations to make decisions under uncertainty.
- Bayesian Decision Making
Optimal Decision Making- Linear Optimisation
Sensitivity Analysis and Shadow Price
- Linear Optimisation
MODULE 8: Data Science to Drive Business Value
- Driving digital transformation within the organisation
- Change management: The role of data analytics, machine learning and its applications.
- Aligning organisations and teams for data-driven approaches
- Making the business case for Data Science
- Data Storytelling with Visualisation using tableau.
MODULE 9: Addressing Key Challenges and Risks in Data Science Projects
- Key challenges to data science projects and their solutions
- Delta Framework and Delta Plus Model
- Project-level risks and examples of failed data science projects
- Predict the success of big data project using DATA techniques.
MODULE 10: Data Science and the Future
- Drivers, expected outcomes, and technology enablers for Industry 4.0
- Components for AI success
- Challenges in the implementation of AI in systems
- Evaluate an organisation’s digital transformation journey and sustain a competitive advantage.
For more information, please contact:
- Customer Hotline – +601175726090
- Email Enquiry – info@axsel.com.my
Data Science & Analytics For Strategic Decisions