Core Course
BAN621 – Data-driven Decision Making in Business
Course Unit Code: BAN621
Type Of Unit: Core
Level of Course Unit: Graduate
Year of Study: 1
Semester: Semester 2
Number of ECTS Credits: 10
Class Contact Hours: 12
Mode of Delivery
Distance Learning
Prerequisites
None
Course Objectives
Data literacy is a crucial skill that extends beyond data scientists and analysts, encompassing all roles within an organization. As businesses amass larger volumes of data, it becomes imperative
for everyone to be able to comprehend and analyze this data effectively. Throughout this course, you will gain an understanding of data-driven decision-making fundamentals and
apply these skills to real-life scenarios in finance, marketing, and operations. By employing frameworks such as supply and demand, cost and benefit, and risk and rewards, you will
learn how to extract valuable insights and seize opportunities in this new data-driven era, providing you with practical abilities to succeed. In this course, you will delve into the essentials of data analytics and its application in problem-solving for businesses. We will explore the significance of data analytics in guiding decision-making processes and introduce you to a comprehensive data analysis framework along with commonly used tools. Moreover, we’ll discuss the diverse career paths and roles available in the realm of data analytics and data science.
This course will introduce you to various data analytics tools and essential technologies used for data analysis. Emphasizing the significance of data visualization in the practice of
data analytics, we will also identify different tools and programming languages, discussing their optimal use cases and scenarios
Learning Outcomes
1: Foundational factual understanding of machine learning and artificial intelligence within the domain of business analytics.
2: Comprehensive familiarity with factual information, principles, processes, and fundamental concepts related to big data, machine learning and learning in the context of business analytics.
3: Understand the different types of supervised machine learning methodologies, unsupervised learning and their applications.
4: Evaluate and select appropriate machine learning methodologies to address various solutions using AI and Big Data methods.
5: Gain familiarity with deep learning methods and use them to solve complex problems.
6: Apply ML algorithms to extract meaningful insights from data.
7: Possess an extensive and specialized understanding of factual and theoretical aspects of ML and big data in the context of business analytics, while also being conscious of the limitations and boundaries of this knowledge.
Course Content
1st week: Fundamentals of Big Data
2nd week: Fundamentals of Artificial Intelligence
3rd week: AI and big data for business Machine Learning Part 1: fundamentals and examples (unsupervised learning) K-Means clustering
4th week: AI and big data for business Machine Learning Part 1: fundamentals and examples (unsupervised learning) Hierarchical clustering
5th week: AI and big data for business Machine Learning Part 2: fundamentals and examples (supervised learning)Regression – Linear Regression
6th week: AI and big data for business Machine Learning Part 2: fundamentals and examples (supervised learning)Classification – Decision Trees
7th week: AI and big data for business Machine Learning Part 2: fundamentals and examples (supervised learning)Classification – K – Nearest Neighbors
8th week: AI and big data for business Machine Learning Part 2: fundamentals and examples (supervised learning)Classification – Random Forests
9th week: AI and big data for business Machine Learning Part 2: fundamentals and examples (supervised learning)Classification – SVM
10th week: AI and bigdata for Business Deep Learning : fundamentals and examples an introduction
11th week: AI and bigdata for Business Deep Learning : fundamentals and examples. Neural Netowrks
12th week: AI and bigdata for Business Deep Learning : fundamentals and examples Deep Networks
Course Features
Weekly self-assessment activities :
On a weekly basis, students will have the possibility to engage in self-assessment activities to judge their own level of understanding of the concepts covered so far. The weekly self-assessment activities provide immediate feedback.
Weekly interactive activities (20%):
On a weekly basis, students will have the possibility to engage with self-assessment activities to judge their own level of understanding of the concepts covered so far. Such activities include online quiz and a web-based interactive development environment for code exercsises, and data manipulation.
The weekly interactive activities provide immediate feedbacks.
The participation to such activities accounts for at most 20% of the final grade.
Project (30%)
There will be one final assignment/project which accounts for 30% of the final grade.
Final exam (50%):
Students will be provided with a problem statement and the corresponding data set and will have to develop a solution for that problem using a web-based interactive development environment (Jupyter Notebook).
English (UK) 
Ελληνικά