Titular Professors
Professors
R1. Understand what is an algorithm and the different types
R2. Understand what is the cost of algorithm
R3. Understand what are the parameters to optimize the cost of an algorithm
R4. Understand the different ways to structure data
R5. Understand the Python environment: Jupyter and Spider
R6. Manipulate and codify simple algorithms using Python
R7. Manipulate and codify different structures of data using Python
R1. Understand what is an algorithm and the different types:
What is a data? What does the concept of 'data structure'mean?
Which attributes should have the data to be good for an algorithm?
What is Python ? And what are the different types of data available in Python?
What is an Algorithm? What are the different types of algorithm? Give examples
What is an heuristic? Give examples
R2. Understand what is the cost of algorithm
What is a Learning Algorithm? What is the difference with a simple algorithm? What is the computation error of an algorithm? How to calculate it?
What are computation time and costs? How to reduce them?
R3. Understand what are the parameters to optimize the cost of an algorithm:
Data types: numeric, alphanumeric, vector, matrix
R4. Understand the different ways to structure data:
Function type: Import, visualize, analyse, communicate
R5. Understand the Python environment: Jupyter and Spider:
Basic code, examples, script
R6. Manipulate and codify simple algorithms using Python:
Basic code, examples, script
R7. Manipulate and codify different structures of data using Python
This course is structured between presentations held by the professor and flipped presentation structure held by the students.
In the first weeks, groups will be formed, and work/project will have to be prepared/presented by group. To prepare for the presentations and discussion, students will be provided guidelines in advance.
Besides the midterm and final exam, work in groups will be used to assess the student's learning progress and will be part of the final grade evaluation. This requires students to maintain a diligent follow-up of the topics being covered as well as regular check on the required reference material for the course.
Revision date: after each evaluation (midterm/final exams) and once grades have been posted, there will be a time slot for the revision of the exam. There will be no revision of exams outside that time slot.
In order to evaluate if the student has achieved in an adequate degree the objectives pursued in the subject, different evaluation activities are used.
The following table shows the percentage of evaluation of each activity on the final grade:
R2 - 15% - 15% - CLASS ACTIVITY (HOMEWORK)
R4 - 35%
- 15% - WORK IN GROUP
- 20% -1st MIDTERM
R5 - 15% - 15% WORK IN GROUP
R6 - 35%
- 15% GROUP PROJECT
- 20% FINAL EXAM (ORDINARY CALL)
Retake policy: Should you fail the course overall, you will have the opportunity to sit a retake exam, as long as assignments and projects have been presented.
The retake grade will then be: 40% the retake exam and 60% the continuous assessment obtained during the course.
- HadleyWickham - R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2021).
- Laudon, Kenneth C. and Laudon, Jane P. (2014). Management Information Systems: Managing the Digital Firm. Global Edition. 13th edition.
- Davenport, T & Harris, J. Competing on Analytics: The New Science of Winning (2014, 2017). Havard Business School.
- Foster Provost and Tom Fawcett. Data Science for Business. O' Reilly (2013).
- Carl Anderson. Creating a Data-Driven Organization. O' Reilly (2015).
- D J Patil. Building Data Science Teams. O' Reilly (2011).
- D J Patil and Hilary Mason. Data Driven: Creating a Data Culture. O' Reilly (2015).