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

    • 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

    1. Recommendations and Ranking
    2. Collaborative Filtering
    3. 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

    1. Bayesian Decision Making
      Simulations to make decisions under uncertainty.

     


  • Optimal Decision Making

    1. Linear Optimisation
      Sensitivity Analysis and Shadow Price

     

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.

 

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Data Science & Analytics For Strategic Decisions

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