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HU Credits:
3
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Business Administration
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
Mt. Scopus
Course/Module Coordinator:
Dr. Ariel Goldstein
Coordinator Office Hours:
Teaching Staff:
Dr. Ariel Goldstien
Course/Module description:
The course will focus on data science practices. it will involve heavy programming and the development of real world projects.
Course/Module aims:
To teach the students actual data science practices, software engineering methods with strong focus on machine learning frameworks.
Learning outcomes - On successful completion of this module, students should be able to:
1. Learn more advanced data science techniques
2. Learn from a few large scale data science projects
3. Design and implement a meaningful data science product/project
Attendance requirements(%):
70%
Teaching arrangement and method of instruction:
frontal lectures and guided project development
Course/Module Content:
1. Multiprocessing (1 class)
2. Advanced pandas (3 classes)
3. Advanced Classification (1 class)
4. Advanced Time Series Analysis (2 classes)
5. Unsupervised learning (2 classes)
a. Advanced Cluttering
b. Unsupervised Feature Extraction from Text
6. TensorFlow, the most popular open-source Deep Learning library. (2 classes)
7. Keras (2 classes with 8 & 9)
8. Convolutional Nets for machine vision;
9. Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis;
Required Reading:
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition
Python Data Science Handbook: Essential Tools for Working with Data
Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems
Machine Learning, Tom Mitchell
Additional Reading Material:
Grading Scheme :
Essay / Project / Final Assignment / Home Exam / Referat 100 %
Additional information:
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