Data Science & Advanced Python concepts for Neuroscience
Data Science & Advanced Python concepts for Neuroscience
Lecturer Name: Dr. Ossnat Bar-Shira
Department Name: Brain Research Center
Course No: 27-5020-01/02
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Course Type: |
Lecture+ training |
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Scope of credits: |
1 credit points + 1 credit point training |
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Year of study: |
Graduate students |
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Semester: |
A |
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Day & Time: |
TBA |
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Reception Time: |
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Lecturer Email: |
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Moodle Site: |
TBA |
Course description and learning goals
Course Abstract
The course is an advanced Python course for neuroscience. It combines fundamental computer science content, such as computer architecture and complexity, with programming and computational tools that students will need throughout their careers as neuroscience researchers. Additionally, the course teaches problem-solving using computers and dealing with various types of neuroscience related data..______________________________________________________________________
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Learning objectives (expand)
By the end of the course, students will be able to:
Understand the basic architecture and constraints of computers and how to use them for problem-solving, specifically in neuroscience. Handle diverse neuroscience datasets and analyse them effectively. Develop clean, maintainable, and reproducible code using professional-grade tools
Knowledge
Active learning – lessons plan:(expand)
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Lesson No. |
Topic |
Active learning |
Required reading |
Assessment |
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1 |
Intro: computational thinking: problem solving using a computer. Schematic computer. Computer structure, memory representation computing resources. |
Collaborative learning: Installation party: install and configure python dev. environment |
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2 |
Algorithms and complexity |
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3 |
Mathematical & scientific packages |
Collaborative learning: Numpy and scipy Debugging
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4 |
Development environment & Version control |
Collaborative learning: Git party: open account in Github, install git on local machine |
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5 |
Computing resources |
Collaborative learning: Client-server in python |
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6 |
Object Oriented programming |
Collaborative learning: Design and implement an OOP module |
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7 |
Data management packages |
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8 |
Data storage format & Data visualization |
Collaborative learning: matplotlib seaborn and more |
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9 |
Database basics |
Collaborative learning: Pandas |
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10 |
Data pipeline development |
Collaborative learning: scripts & pipelines |
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11 |
Parallel and Distributed Computing |
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12 |
Error handling, Logging and Testing |
Collaborative learning Logging and Profiling |
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13 |
Advanced Python Concepts |
Code Review |
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14 |
Programming in the AI Era |
Projects presentation |
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(In a course that lasts a whole year, the additional sessions should be added)
* There may be changes in the syllabus depending on learning progress and effectiveness
Final grade
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Description of the learning product |
Weight in the final score |
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Home assignment |
40% |
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Final Project |
40% |
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Project presentation |
15% |
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Review |
5% |
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Course number |
Course name |
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Basic Programming in Python |
תאריך עדכון אחרון : 30/01/2025
