- Course Number: CS2091
- Professor: Tomer Libal
- Office: SD-09
- Office Hours: By appointment
- Class Schedule: Mon/Thu 15:20-16:40
- Credits: 4
The 21st century has seen a big increase in the amount of data which is made accessible. Social media such as Facebook, online shops such as Amazon and many others, are all gathering raw data. But what can be done about this data? Data Science covers tools and methods around the extraction of knowledge from data. Such tools cover its collection, storing, processing and analysis. In this course we will learn about several of the most important tools in the above flow and will apply them to real-world examples.
| Week (Dates) | Topic | Lecture | Reading (chapter in book) | Assignment |
|---|---|---|---|---|
| 1 (21,24/1) | Introduction, Cause and Effect, Tables | Demo 1 Demo 2 | 1,2,3 | Lab 1, HW 1 (due 30/1) |
| 2 (28,31/1) | Data types, Building tables, Charts | 4, 5, 6.1, 6.2, 7.1 | Lab 2, HW 2 (due 6/2) | |
| 3 (4,7/2) | Histograms, Functions | 6.3, 6.4, 7.2, 7.3, 8, 8.1 | Lab 3, Lab 4, HW 3 (due 13/2) | |
| 4 (11,14/2) | Groups, Joins, Table example | Class 7 Class 8 | All chapter 8 | HW4 (due 20/2), Project 1 (due 28/2) |
| 5 (18,21/2) | Iteration, Chance, Sampling | Class 9 Class 10 | 9, 10 | HW 5 (due 27/2), Lab 5 |
| 6 (25,28/2) | Models, Comparing distributions, Decisions and Uncertainty | Class 11 Class 12 | 11 | Lab 6, HW 6 (due 20/3) |
| (18/3) | Midterm exam | |||
| 7 (21/3) | A/B Testing | Class 13 | 12 | HW 7 (due 27/3) |
| 8 (25,28/3) | Causality, Confidence intervals, Interpreting Confidence | Class 15 Class 16 | 13 | Lab 7, HW 8 (due 3/4) |
| 9 (1,4/4) | Center and Spread, The Normal Distribution, Sample Means | Class 17 Class 18 | 14 | HW 9, Project 2 (due 18/4) |
| 10 (8,11/4) | Designing Experiments, Correlation, Linear Regression, Least Squares | Class 19 Class 20 | 15, 15.1, 15.2 | Lab 8, HW 10 |
| 11 (15,18/4) | Residuals, Regression Inference | Class 22 | 16 | Lab 9 |
| 12 (25/4) | Classification and Classifiers | Class 23 | 17,17.1,17.2,17.3, 17.4 | HW 11 , Final project (due 9/5) |
| 13 (29/4,2/5) | Decisions, Conditional probabilities and Ethics | Class 25 | 17.5,17.6, 18 | HW 12, Lab 10 |
| 14 (6/5) | ||||
| (16/5) | Oral exam on final project |
Attendance is mandatory in all classes and attendance affects the grade. In case of four unexcused absents, a grade of "F" is automatically given.
IN ALL CASES OF MISSED COURSE MEETINGS, THE RESPONSIBILITY FOR COMMUNICATION WITH THE PROFESSOR, AND FOR ARRANGING TO MAKE UP MISSED WORK, RESTS SOLELY WITH THE STUDENT.
Whether an absence is excused or not is ALWAYS up to the discretion of the professor or the department. Unexcused absences can result in a low or failing participation grade. In the case of excessive absences, it is up to the professor or the department to decide if the student will receive an "F" for the course. An instructor may recommend that a student withdraw, if absences have made it impossible to continue in the course at a satisfactory level. Students must be mindful of this policy when making their travel arrangements, and especially during the Drop/Add and Exam Periods.
- 20% Midterm exam
- 10% Lab
- 20% Homework
- 20% Projects 1 and 2
- 30% Final project and exam
Students will learn in parallel the fundamental ideas of Data Science as well as tp use the Python language and Jupyer Notebook. Students should be able to digitally obtain and parse datasets and to model them in various ways in order to be able to draw information from the data.