Data Science for Executives Course

Introduction

 Data science is a rapidly spreading field that combines statistical analysis, data management, computation, and substantive expertise, with the goal of improving decision-making in business, government, administration, law, and just about every other field.

One of the key challenges for decision-makers and managers is to understand what makes for good data science, and how the evidence from this field should be used in evaluation and decision-making.

The focus of this course is on examples of good and bad data science, with real-world applications from government, business, and law. By the end of the course, students will be familiar with the concepts of data science and will have learned how to evaluate quantitative evidence and how to design new studies using big data and data scientific tools.

Duration

5 Days

Who should attend?

This course is designed for:

• C-Suite
• Senior leaders and managers in financial and commercial corporations with responsibility for corporate social responsibility activities.
• Senior executives in the second or third echelon of large organizations
• Managing directors or Management team members of medium-sized organizations
• Regional directors and country managers
• Senior project managers
• Business Unit directors

Course objectives

• A comprehensive top-level understanding of the core concepts and methods of data science, including data management, data analysis, machine learning, and statistical learning.
• The ability to evaluate evidence from statistical learning and data science, in order to make informed decisions.
• A thorough awareness of the core issues in designing new data scientific studies.
• Practical and applied knowledge of the core material through applications drawn from business, government, and law, including at least one presentation from a practitioner in one or more of these areas.

Module 1: Data and Data Science

• Introduction and overview of data science
• The vocabulary of data, the structure of data and the types of data
• Introduction to the tools used to record, structure, link and retrieve data
• Databases and their role in building a business or organizational infrastructure

Module 2: Data Science Analysis and Data Regulation

• Interpreting Data Science
• Hypothesis tests, Type I and Type II errors, p-values, statistical significance, coefficients, regression analysis, model fit, and common association measures
• Bayesian reasoning and probability
• Data protection and the law
• Data governance and ethical considerations

Module 3: Machine Learning and Artificial Intelligence

• An overview of machine learning and how to interpret it
• Practical guide to machine learning methods and their potential pitfalls and limitations
• Supervised and supervised methods of machine learning
• Business uses of data and AI
• Recent developments in AI such as embeddings, neural networks and deep learning

Module 4: Collecting the interpreting survey data

• How does survey data fit into other types of data
• Translating managerial problems into a survey
• Approaches in survey research
• Implementing survey research
• Items: Statements and scales
• Pitfalls to avoid in survey research
• Good and bad practices, e.g. the net promoter score

Module 5: Social Listening: Deriving actionable insights from social media and textual data

• How new sources of data can be used to derive consumer behavior insights
• Social listening and machine learning
• Advances in machine learning
• Text mining and sentiment analysis
• Course conclusions