This is an intensive 20-hour course based on a hands-on approach using Jupyter notebooks, all material is motivated by specific information retrieval and data analysis questions and each thematic unit concludes with a small project. The course provides basic training in data analysis and machine learning with Python and R.
Even with little programming background, this course will give you basic tools to get started with data science, from data exploration to building your predictive models and extracting insights. Main programming language will be Python, with a final touch of data science with R coding.
The course is addressed to:
- Master and PhD students
- Researchers and academics
No previous skills are needed, but some statistics background and a basic knowledge of programming in Python of R would be helpful.
Students will have access to materials and videos well in advance of the online sessions, so that classes can concentrate on resolving doubts and conducting hands-on implementations and exercises.
- Ungraded: Short coding exercises to be solved by students in class, with the support of TA
- Ungraded: Short quizzes may be sent to students at the end of each day to evaluate their level of comprehension
- Graded: Daily project assignment to practice what has been taught in class in a real use case. This will be an individual assignment to which students should dedicate 2-4 hours after class, with 1 hour of online TA Q&A session, and turn in on the same day.
- Optional: One extended project assignment. Students will have 30 days to deliver a more complete version of the same daily project assignments. For the extended project, we recommend that students work in teams of 2-3 people and submit just one project per group.
- As an alternative, for an additional fee, students may propose their own use cases using their own data and work on them after the Winter School with support from a Teaching Assistant. Please email the organizers for more details.
Tools we will use in this course
- Jupyter Notebooks will be typically used to share contents and code. Students may use a local installation of Anaconda, and/or use Google Colaboratory.
- Google Classroom and Google Drive will be used to share materials and feedback.
- Google Meet will be used for online meetings.
- Google Jamboard / SketchBoard will be used as dashboards.
- Slack may be used to communicate with the assigned TA and also as a forum for discussion.
- Kaggle will be used to create use cases and internal competitions.
Get started with Data Science, from data exploration to building predictive models and extracting insights, in just four days.
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About the School
Barcelona Graduate School of Economics is an institution dedicated to academic rigor, open-minded thinking, and scientific impact. We do this by bringing together four of Europe’s top Economics resear ... Read More