DATA SCIENCE FOR BUSINESS (DATA)

Materials

A brief summary of what we’ve seen in every session:

Done?	Session	Topic	Relevant Pages
✓	PRE	Python	Review Python
✓	1	Introduction to Data Science	Numpy
✓	2	Image Data I: How Computers See	Images in Python
✓	3-4	Image Data II: Image Analytics	Computer Vision, Haiteng Case, Haiteng Assignment
✓	5	Introduction to Pandas	Pandas , Investment Drivers
✓	6	From data to model I	AirBnB
✓	7	From data to model II	AirBnB, Assignment
✓	8	Working with Time Series	Time Series in Pandas
✓	9	Text Data I	How Computers Read
✓	10	Text Data II	How Computers Understand

Introduction

In today’s job market, the role of “data scientist” has become increasingly prominent, blending data analysis with programming and database expertise. While the foundational techniques have existed for decades, the rise of big data and advances in cloud computing have accelerated the growth of data science as a field.

Data science encompasses a wide range of roles that vary by industry, company, and job function. Though the individual skills involved—such as data analysis and programming—aren’t new, what sets data science apart is the integration of these competencies into a unified discipline.

At its core, data science involves gathering, analyzing, and interpreting data, alongside creating data-driven solutions using machine learning algorithms. This course will focus on the essential skills of data capture and analysis. For further exploration, the Data Visualization (DATAVIZ) course will cover data presentation, while the Machine Learning (ML) course delves into the development of predictive models and data products.

Objectives

The objectives of this course are (i) to introduce data science, and (ii) to show, through examples, how data scientists use the Python language.

Evaluation

The grading is based on class participation (1/3) and individual assignments (2/3).

Participation in class goes beyond just speaking up; it also includes punctuality, attendance, and professional conduct. Being late or leaving early will result in significant penalties, as will unexcused absences. In-class mini-assignments, questions, or quizzes contribute to your participation grade. The final grade structure and letter grading are at the discretion of the instructor.

Instructor

Prof.: Enric Junqué de Fortuny

E-mail: ejunque [at] iese [dot] edu