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CS-2065/5065 - Data Science II: Theory and Practice

Information

• Textbook: Computational and Inferential Thinking: The Foundations of Data Science
• Instructors: Quang Nguyen Luong and Tomer Libal
• Grading:
  • 25% Individual HWs
  • 15% Collaborative Labs
  • 20% * 3 Individual Oral exams
• Classroom: C-302
• Teams: cs2065/cs5065

Schedule

Date	Topics	Resources	Reading	Assignments
16/1	Intro to data and distributions: categorical vs quantitative
19/1	Intro to Python & Jupyter notebooks, Data science, Causality and experiments	Demo	Chapter 1, Chapter 2	Lab 01
23/1	Visualizing, describing and comparing distributions			HW 01
26/1	Programming in python, data types, sequences	Demo	Chapter 3, Chapter4, Chapter 5	Lab 02
30/1	Standard deviation and the Normal model			HW 02
2/2	Tables	Demo	Chapter 6	Lab 03
6/2	Probability and probability models			HW 03
9/2	Visualization	Demo	Chapter 7	Lab 04
13/2	Randomness and collecting data (surveys, experiments,..)			HW 04
16/2	Functions	Demo	Chapter 8	Project 1
20/2	Sampling distribution models			HW 05
23/2	Hypothesis testing
13/3	Confidence intervals			HW 06
16/3	Randomness	Demo	Chapter 9	Lab 05
20/3	The central limit theorem			HW 07
23/3	Sampling, empirical distributions and testing hypotheses	Demo	Chapter 10, Chapter 11	Lab 06
27/3	Regression			HW 08
30/3	Comparing two samples	Demo	Chapter 12	Lab 07
3/4	Inference for regression			HW 09
6/4	Estimation	Demo	Chapter 13	Project 2
13/4	Why the mean mattaers	Demo	Chapter 14	Lab 08
17/4	Multiple Regression			HW10
20/4	Prediction	Demo	Chapter 15	Lab 09
24/4	Multiple Regression			HW11
27/4	Inference and classification	Demo	Chapter 16, Chapter 17	Project 3
4/5

Course Learning Outcomes

• Students will make effective use of probabilistic and statistical thinking
• Students will demonstrate a robust understanding of the fundamental ideas of Data Science
• Students will 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
• Students will demonstrate to be able to use the Python language and Jupyter Notebook
• Develop a positive approach to mathematics and to appreciate its use in modeling the world (CCM LO1)
• Reason with quantitative information –in words, numbers and graphs and charts (CCM LO2)
• Clearly communicate quantitative information in words, in numbers and with graphs (CCM LO3)
• Develop strategies for solving problems (CCM LO4)

Global Liberal Arts Core Curriculum

Some courses at AUP can be used to fulfill the GLACC requirements. As a reminder , the GLACC requirement areas are:

• Integrative Inquiry (CCI)
• Experiential learning (CCX)
• Research, Interpretation, and Writing (CCE)
• Digital Literacy and Communication (CCD)
• Disciplinary Research Methods and Writing (CCR)
• Quantitative Reasoning (CCM)
• Experimental Reasoning Course with Lab (CCS)
• Expression Française (CCF)
• AUP Capstone (CCC)

The GLACC associated learning outcomes are indicated with the designation (CCLO) in the list of learning outcomes. If you see a CCLO in that list then this course can be used to fulfill the GLACC requirement for that area. Courses that have multiple CCLOs can only be used to satisfy one of the requirements listed.