Introduction to data analysis in Python

Code:

IADP01

Acronym:

IADP

Keywords
Classification	Keyword
CNAEF	Engineering and related techniques

Instance: 2022/2023 - 2S (of 08-03-2023 to 31-07-2023)

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Introduction to data analysis in Python

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
IADP	1	Syllabus	1	-	1,5	-	4
L.BIO	17	Syllabus	2	-	1,5	-	4
L.EA	9	Syllabus	1	-	1,5	-	4
L.EC	92	Syllabus	1	-	1,5	-	4
L.EEC	62	Syllabus	2	-	1,5	-	4
L.EGI	6	Syllabus	1	-	1,5	-	4
L.EM	25	Syllabus	2	-	1,5	-	4
L.EMG	17	Plano de estudos oficial a partir de 2008/09	2	-	1,5	-	4
L.EQ	3	Syllabus	2	-	1,5	-	4

Teaching language

English

Objectives

Students are expected to acquire transversal skills in solving management problems and data analysis using the Python language and its libraries, namely using data series available at Pordata and INE.

Learning outcomes and competences

It is intended that students develop the following skills at the end of the UC:

• Use of Python and its data analysis support libraries.


• Understanding the essential concepts of data analysis and management.


• Solving problems involving a large volume of data, namely using data series available at Pordata and INE.

Working method

B-learning

Program

In this Curricular Unit, students are expected to acquire transversal skills in solving management problems and data analysis using the Python language and its libraries.
The program, structured in five sequential modules, provides for the approach of the following topics:

• Python programming.


• Use of Python libraries (pandas, numpy, matplotlib, etc).


• Python interface with spreadsheets (MS-Excel).

Mandatory literature

Peter Wentworth; Learning with Python 3 (RLE)
Charles R. Severance; Python for everybody

Teaching methods and learning activities

This curricular unit is based on an elearning approach supported by the Moodle platform. The following types of activities are planned:

1- Viewing videos and consulting documents on different topics, as well as accessing bibliographic references and various links;

2- Resolution of proposed exercises;

3- Distributed assessment (online) on the topics presented

4- Support and feedback sessions (optional);

5- Final assessment

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Teste	100,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	28,50
Frequência das aulas	12,00
Total:	40,50

Eligibility for exams

rating greater than 85% in the transition test

Calculation formula of final grade

• The assessment methods will be based on two components:




• A distributed component, in the form of answers to questions (quizzes) in moodle: throughout the curricular unit, and in order to advance in the different modules of the UC, students will have to answer sets of questions (quizzes); students will be admitted to the “n+1” module, if they have obtained a score above 85% in the “n -> n+1” transition test.


• A face-to-face component, in the form of an exam lasting 1h30m in a computer room at FEUP




• Final grade calculation formula = 100% exam


• According to GR 02/11/2020, Cap II, art 13, point 1, the final classification will be expressed in an integer numerical scale from 0 to 20 values.

Special assessment (TE, DA, ...)

Obtaining approval for this CT in a special season exam requires approval in the following two components:

- Distributed component (if not performed during the semester), which may be replaced by the completion of the "Quiz Global" in Moodle (with the respective approval) in the 15 days prior to the exam date;

- Final exam component, to be carried out through a in class test, lasting 1:30h in a computer room at FEUP. Only those who have previously passed the distributed component of the CT have access to the final exam.