Information Systems (2225.YR.009143.2)
General information
Type: |
OBL |
Curs: |
2 |
Period: |
S semester |
ECTS Credits: |
4 ECTS |
Teaching Staff:
| Group |
Teacher |
Department |
Language |
| Year 2 |
Jordi Tarda Valls |
Operaciones, Innovación y Data Sciences |
CAT, ESP |
Prerequisites
Mathematics Applied to Management
Descriptive Statistics and Probability
Statistical Inference and Data Analysis
Previous Knowledge
Know how to use the computer's file system (Finder/Explorer)
Be able to compress/unzip files in ZIP format and to download files from internet
Workload distribution
Workload distribution:
Lectures: 18 hours
Participatory sessions: 18 hours
Independent study: 56 hours
COURSE CONTRIBUTION TO PROGRAM
The volume of data available today is transforming society and organisations. Machine-learning and Artificial Intelligence provide a series of frameworks, theories and methods to apply data to decision-making processes. This course contributes to the programme in the following ways:
1) Students will acquire the basic knowledge needed to understand machine-learning and its practical application within firms.
2) Students will develop the necessary skills that enable them to use these techniques on their own, applying they Python programming language, the de facto standard used in most companies.
Course Learning Objectives
1. Recognise and fluidly apply scientific language to data-analysis problems
2. Understand and relate mathematical, statistical and computational concepts to resolve Data Science-related problems
3. Use formal reasoning to resolve data-based problems
4. Adopt and apply a mindset focused on using data to resolve analytical problems.
CONTENT
1. Computational mentality Data Science requires having a certain understanding of how computers store and access data. In this subject block we will study different data structures and how to use them to analyse stored data. We will also introduce students to basic Python programming concepts such as functions, conditional statements and loops.
|
2. Linear models Linear models are the simplest and easiest Data Science techniques. In this block we will study linear regression and logistical regression models. We will examine both models from both a mathematical and practical perspective. To this end, we will introduce students to NumPy, a library within Python used to carry out numerical analyses. We will implement both models in Python.
|
3. Machine-Learning One of the primary challenges Data Sciences faces is developing and assessing algorithms and machine-learning models. During the second part of this subject, we will introduce students to the taxonomy and standard methodology used to this end. We will use decision trees to illustrate these models and we will define different metrics to correctly assess their performance and precision. For this, we will use different Python libraries such as Pandas and Scikit-Learn. |
Methodology
The methodology applied in this course combines lectures, problem-solving sessions and solving other problems individually outside of class.
ASSESSMENT
ASSESSMENT BREAKDOWN
| Description |
% |
| Final exam |
40 |
| Mid-term exam |
40 |
| Follow-up quizzes (participatory sessions) |
20 |
Assessment criteria
Assessment consists of a mid-term exam and a final exam as well as a series of short-quizzes taken at the end of each practical session.
To successfully pass this subject, students need to earn a minimum mark of 4 (out of 10) on the final exam.
Attending all lecture and participatory sessions is compulsory. Only students who have attended class will be able to sit the follow-up quizzes.
Bibliography
Short bibliography:
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2014. An Introduction to Statistical Learning: with Applications in R. Springer Publishing Company, Incorporated.
Timetable and sections
| Group |
Teacher |
Department |
| Year 2 |
Jordi Tarda Valls |
Operaciones, Innovación y Data Sciences |
Timetable Year 2
From 2023/2/3 to 2023/3/10:
Each Friday from 8:00 to 9:00. (Except: 2023/2/24)
Each Friday from 9:00 to 10:30. (Except: 2023/2/24)
From 2023/3/16 to 2023/3/31:
Each Friday from 8:00 to 9:00. (Except: 2023/3/17)
Each Thursday from 9:45 to 12:30. (Except: 2023/3/23 and 2023/3/30)
Each Thursday from 9:45 to 12:00. (Except: 2023/3/23 and 2023/3/30)
From 2023/3/24 to 2023/5/5:
Each Friday from 9:00 to 10:30. (Except: 2023/4/7)
From 2023/4/14 to 2023/5/5:
From Thursday to Friday from 8:00 to 9:00. (Except: 2023/4/20 and 2023/5/4)
Each Thursday from 9:00 to 10:30. (Except: 2023/4/20 and 2023/5/4)
Monday 2023/5/22 from 9:45 to 12:00.
