Topic outline
Course Information
🎓 UEM531: Foundations of Data Science
Teaching Unit: Methodology
Course Title: Foundations of Data Science
Coefficient: 02
Credits: 03
🎯 Course Objective
The main goal of this course is to provide students with a solid foundation in data science, emphasizing essential mathematical and computational tools, particularly linear algebra.
By the end of this course, students will:
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Understand the core concepts and processes of data science.
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Gain theoretical and practical skills for analyzing and interpreting data.
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Be prepared to pursue advanced courses in Data Analysis and Machine Learning.
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🧠 Recommended Prerequisites
To follow this course effectively, students should have prior knowledge of:
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📊 Statistics
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🧮 Mathematics
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💻 Programming
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🔢 Linear Algebra
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🧾 Assessment Method
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🧠 Final Exam: 60%
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🧪 Practical Work (Labs): 40%
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💡 Teaching Format
This course combines:
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Interactive lectures with real-world data science examples.
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Hands-on practical sessions (labs) using Python and data analysis tools.
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Guided exercises and case studies to bridge theory and practice.
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Chapter 01 | Introduction to Data Science
This is a warm-up to prepare you for the practical work in the Foundation of Data Science course. Just warm up to your new friends, Jupyter Notebook, Python, numpy and pandas libraries. Don't forget they will be your best friends if you want to be a data scientist. Install them on your local machine and keep them behind you in all practical work. You will find the file "Introduction to Python and the Working environment.pdf" that will guide you in getting to know your new friends.
Python Programming Fundamentals - Full Course
Playlist14 vidéos by Dan Kornas on Python pragramming Fundamentals
Chapter 02 | The Data Science Process
Practical Work 1: Hands-On Introduction to Data Science and the Data Science Process