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Data-Driven Transformation (18CBA11000)

General information

Type:

OB

Curs:

1

Period:

S semester

ECTS Credits:

3 ECTS

Teaching Staff:

Group Teacher Department Language
Sec: A Manu Carricano Operaciones, Innovación y Data Sciences ENG

Group Teacher Department Language
Sec: B Manu Carricano Operaciones, Innovación y Data Sciences ENG

Prerequisites

Pre-programme in Business Analytics (PBA)

Workload distribution

Lectures and participatory session represent 30h and approx. 50% of the total workload. The other 50% being assigned to pre-readings and cases preparation.

Course Learning Objectives

Big Data & Data Sciences are transforming almost every industry. Having a grasp on Big Data fundamentals will provide executives with the basic notions of analytics, performance metrics, and data-driven decision-making.

In the next five years, we expect a shortage of 200 000 data scientists? and 1.5m data savvy managers (US figure only). The course is oriented towards participants willing to increase their skills to become catalysts of the analytical transformation.

More precisely, the participants of the course will be able to answer the following questions:

- How to design a sustainable & efficient data architecture?
- How to collect and integrate a variety of data sources?
- How to develop new knowledge from such information?
- How to use this information to improve both strategic decision-making and business performance

CONTENT

1. Session 1: Introduction: The Analytical Transformation

2. Session 2: The Journey towards Data Driven Decisions

3. Session 3: Building Successful Analytical Products

4. Session 4: Data Quality and Preparation

5. Session 5: Data Visualization for Superior Impacts I

6. Session 6: Session 5: Data Visualization for Superior Impacts II

7. Session 7: Putting Data Science to Work I (Exploration)

8. Session 8: Putting Data Science to Work II (Exploitation)

9. Session 9: Conclusions: Road to AI

10. Session 10: Final Exam

Methodology

The course aims at offering an applied, problem-based approach to data-driven decisions, blending theory and practice. Thus, it relies on various methods for individual and group learning, with three main approaches:

- REQUIRED READINGS & CASES: a limited amount of reading is mandatory to learn the key concepts and tools related to the topic of each session. All required readings will be made available on the course webpage.
- HANDS ON: Concepts are fine, but application is better. We have developed an innovative solution - the Esade Data Lab - in order to allow you access a full ecosystem of Big Data & Advanced Analytics Tool in one single place, thanks to 1st class partnerships (Oracle, Trifacta, Tableau, Qlik, IBM Data Science Experience, Dataiku,, among others). All data sets will be available in the course webpage on Moodle.
- GUEST SPEAKERS PRESENTATIONS: These presentations (presencial or remote, depending on speakers availability to travel) will either introduce course topics for discussion or present managerially relevant theoretical and analytical frameworks, by professionals who have implemented Big Data Solutions, and lead Big Data Transformations. The approach is interactive; participants are invited to contribute their own experiences (in a concise manner).

Assessment criteria

Evaluation system has three components and will be presented in details during the course introduction:

Participation in class: 30 %
Mid-term Test ( * ): 20%
Final Exam ( ** ): 50 %

A minimum grade in the final exam of 4 out of 10 is required to pass the course.

( * ) Mid-Term Test will take place during session 6
( ** ) The Final Exam will be case-based and will take place in the last class session

Timetable and sections

Group Teacher Department
Sec: A Manu Carricano Operaciones, Innovación y Data Sciences

Timetable Sec: A

From 2019/2/7 to 2019/4/4:
Each Thursday from 9:00 to 12:00. (Except: 2019/2/28 and 2019/3/28)

Tuesday 2019/3/12 from 9:00 to 12:00.

Monday 2019/4/8 from 9:00 to 12:00.

Group Teacher Department
Sec: B Manu Carricano Operaciones, Innovación y Data Sciences

Timetable Sec: B

From 2019/2/7 to 2019/2/19:
Each Thursday from 15:30 to 18:30. (Except: 2019/2/7)
Each Tuesday from 16:15 to 19:15. (Except: 2019/2/12)
Each Thursday from 14:00 to 17:00. (Except: 2019/2/14)

From 2019/3/7 to 2019/4/4:
Tuesday and Thursday from 15:30 to 18:30. (Except: 2019/3/19, 2019/3/26, 2019/3/28 and 2019/4/2)

Monday 2019/4/8 from 14:00 to 17:00.