Data Analysis for Decision Making

Overview:

Introduction:

This course provides you with the tools you need to put data to work: how to set up experiments, how to collect data, how to learn from data, and make decisions to how to navigate the organizational, legal, and ethical issues involved in data-based decision making. The program teaches widely-used frameworks of business analytics: biases, experimentation, descriptive analytics, prescriptive analytics, predictive analytics. Participants then implement the frameworks they have learned through assignments.

Course Objectives:

At the end of this course the participants will be able to:

• Recognize different types of biases that give way to bad decision making and learn how to overcome them.
• Avoid bias in decision making by asking questions critical to your business and identifying the data needed to answer those questions.
• Learn about the sources of data, the intermediary software services that can fetch those data into your database, and then assess the quality of the collected data.
• Explain the reasons behind past events by analyzing and summarising data.
• Predict future outcomes by choosing the appropriate machine learning algorithm to use in a business context.
• Learn the implementation challenges of creating a data-driven organization.
• Understand the ethics and regulatory issues involved in making decisions using data.

Targeted Audience:

This course is designed for managers and employees across different functions who are interested in implementing analytics projects at their organization. It provides business managers with the techniques needed to transform their organization into a data-driven organization. The assignments and cases in the program focus on interpreting the results of analysis and taking decisions based on those analyses. 

Course Outlines:

Unit 1: Decision Biases:

• Decision traps.
• Benefits of analytics.
• Identify the types of biases in a decision-making process and learn how to ask for the right information.

Unit 2: Descriptive Analytics:

• What is data mining?
• Web scraping.
• Application Programming Interface (API).
• What data can you find?
• What do you think about the data you found?
• Amazon and APIs.
• Data cleaning.
• Descriptive statistics.
• Normal and not normal distributions.
• Effect size and confidence intervals.
• Be able to collect, clean, and describe the data you have.

Unit 3: Big Data Opportunities:

• What does Big Data mean to you?
• Introduction to Big Data.
• What is Big Data?
• Four Vs of big data – volume, variety, velocity, and veracity.
• Challenges for big data.
• Big Data opportunities.
• Identify what big data means to you and what you can do with it.

Unit 4: Experimentation:

• Design experiments to gather meaningful data to make data-driven decisions.

Unit 5: Predictive Analytics: Machine Learning:

• Machine learning vs hypothesis testing.
• Machine learning in practice.
• Machine learning algorithms.
• Supervised machine learning.
• Interpret an analysis.
• Machine learning in the real world.

Unit 6: Prescriptive Analytics:

• What is prescriptive analytics?
• Connecting predictive analytics to a business objective.
• Deep dive into a business model.
• Making a business decision.

Unit 7: Prescriptive Analytics: Behavioural Economics Biases:

• Risk aversion.
• Diversification.
• Decision-making process and debiasing.

Unit 8: Ethics/Legal and Organisational Issues:

• Implementation challenges.
• Setting up the right infrastructure.
• Big data strategy.
• Personal data.
• Privacy and Anonymization.
• Hacking and insider threats.
• Making customers comfortable.
• Identify Organisational issues that you will need to consider when making decisions.
• identify the legal and ethical issues behind the gathering, storing, and using data.