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HU Credits:
3
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
agro informatics
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
Rehovot
Course/Module Coordinator:
Dr. Jonathan Friedman
Coordinator Office Hours:
Appointments should be coordinated by email
Teaching Staff:
Dr. Yonatan Friedman,
Mr. Isaac Kramer
Course/Module description:
In recent years, there has been a dramatic increase in the amount of data generated in the world, and data science has become an major discipline with numerous applications across science and industry.
The course introduces the mathematical foundations and computational tools used in data science by combining theoretical lectures with hands-on experience using Python for the analysis and visualization of datasets related to agriculture, food, and the environment.
Course/Module aims:
The course aims to provide the theoretical background and practical skills required for analyzing and visualizing data in order to gain novel insights.
Learning outcomes - On successful completion of this module, students should be able to:
- Collect, clean, and manipulate data
- Formulate precise, quantitative questions
- Analyze data and detect differences between groups and dependencies between variables.
- Choose and create data visualizations according to the nature of the data and the analysis.
Attendance requirements(%):
Teaching arrangement and method of instruction:
Lectures + students' presentations of their projects
Course/Module Content:
Foundation skills
1. Introduction to data science
2. Data types; reading and cleaning data
3. Organizing and manipulating data
4. Principles of data visualization
Analyzing single variables
5. Distributions and summary statistics
6. Smoothing and interpolation
Analyzing pairs of variables
7. Differences between groups
8. Dependencies between variables.
9. Nonparametric statistics and bootstrapping
Analyzing multiple variables
10. Dimension reduction
11. Clustering analysis
12. Comparing multiple groups
Required Reading:
Data Science from Scratch: First Principles with Python 2nd Edition
Additional Reading Material:
Storytelling with Data: A Data Visualization Guide for Business Professionals
Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 50 %
Assignments 50 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
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