Data Analysis

A live look at analysis

Course description

Course logistics

• Course code sharif-da2026
• Term Fall 2026
• Lectures Sundays & Tuesdays, 7:30–9:30
• Office hours / meet window 7:00–7:30 (before lecture)
• Implementation focus Python
• Contact mohammad.khalooei@sharif.edu

Prerequisites

Syllabus — six core blocks

Detailed topics

1. Problem Understanding and Strategic Thinking with Data
2. Data Understanding, Collection, and Quality Assessment
3. Exploratory Data Analysis (EDA)
4. Data Preparation and Feature Engineering
5. Pattern Discovery and Unsupervised Learning
6. Clustering and Dimensionality Reduction
7. Predictive Modeling for Regression Problems
8. Classification and Data-Driven Decision Making
9. Model Evaluation and Experimental Design
10. Model Interpretability and Insight Extraction
11. Time-Series, Event, and Anomaly Analysis
12. Unstructured Data Analysis: Text, Audio, and Multimodal Data
13. AI and Large Language Models for Data Analysis
14. Correlation, Causality, and Decision Making
15. Data Privacy, Anonymization, Ethics, and Governance
16. Real-World Industry Case Studies and Data Challenges

Coursework & grading

Assignments, exams, and late policy

Use of LLMs

Implementation stack

Course staff

Instructor

Teaching assistants

Industry collaboration