IPLUSO 814

Traineeship

• ApresentaçãoPresentation

  The "Internship" curricular unit is intended to provide students with practical experience in professional environments related to Data Science. The field of action covers companies and organizations that apply data analysis in their operations, from technological startups to large corporations and research institutions. The area of ¿¿expertise involves the application of techniques and tools learned throughout the course, including exploratory analysis, modeling, visualization and data interpretation. The intervention domains vary, allowing students to specialize in specific niches, such as business intelligence, machine learning or predictive analysis. This UC is vital in the study cycle, as it connects theory and practice, facilitating students' transition to the job market and reinforcing the relevance and applicability of their acquired knowledge.
• ProgramaProgramme

  Introduction to the Professional Environment: Contextualization of the role of Data Science in the organization, main departments and stakeholders involved. Tools and Platforms: Deep dive into specific software and tools used in the company or institution, which may vary from those covered in the course. Project Management in Data Science: Life cycle of a project, definition of objectives, collection and processing of data, implementation and evaluation of solutions. Communication of Results: Preparation of reports, dashboards and presentations; storytelling techniques with data. Ethics and Privacy: Practical approach to ethical issues, legislation and best practices in data processing and analysis. Networking and Professional Development: Strategies for building and maintaining professional relationships, and tips for career advancement Note: There are fortnightly seminars to support the program and monitor students.
• ObjectivosObjectives

  Knowledge: Understand the real functioning and dynamics of an organization or company that uses Data Science in its daily operations. Familiarize yourself with the specific tools and platforms used in the professional environment. Skills: Practically apply the techniques and concepts learned throughout the course, adapting to the specific needs and challenges of the internship location. Develop communication skills to present findings and insights derived from data analysis. Skills: Work as a team and integrate into an organizational structure, respecting hierarchies, deadlines and guidelines. Demonstrate initiative, proactivity and adaptability, responding effectively to the challenges presented. Establish professional networks that can be valuable for future career opportunities.
• BibliografiaBibliography

  Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer. McKinney, W. (2012). Python for data analysis. Sebastopol, CA: O'Reilly Media, Inc.  
• MetodologiaMethodology

  Project-Based Learning (PBL): Students work on real challenges proposed by partner companies, promoting the direct application of knowledge and skills. Mentoring: Experienced professionals in the field of Data Science act as mentors, offering guidance, feedback and practical insights into the sector. Interactive Platforms: Use of online platforms that simulate real data analysis environments, allowing students to experiment and learn in a practical context. Peer Learning: Students are encouraged to teach and learn from each other, leveraging each other's different skills and perspectives.
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  30
• NaturezaNature

  Mandatory
• EstágioInternship

  Sim