sharif-da2026
sharif-da2026 Sharif University of Technology Fall 2026

Data Analysis

From real data to effective decisions

A live look at analysis

What this course trains

Problem first Strategy before tools
Python-native Hands-on workflows
Real data Industry-ready cases
Responsible AI LLMs for learning

Course description

Who this course is for

By the end of the course, you will be able to

Course logistics

Prerequisites & getting started

Before the first session — environment checklist

Syllabus — six core blocks

01

Problem Framing and Data Strategy

02

Data Understanding and Exploratory Analysis

03

Data Mining and Machine Learning

04

Evaluation, Interpretation, and Decision Making

05

Modern Data Analysis and AI

06

Industry Problems, Privacy, and Hands-on Challenges

Week-by-week plan

Week Theme Milestone
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Week 14
Week 15
Week 16

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

How you'll be graded

Component Weight
5%
30%
15%
20%
20%
10%
Total100%
Exact deadlines are posted on the course announcement channel in week 1.

Assignments & project structure

Late policy

How to submit

Use of LLMs

Implementation stack

Resources

Recommended readings

Tools & environment

Datasets & practice

Course staff

Instructor

Teaching assistants

Pouria Safaei

Teaching Assistant

Abolfazl Moslemi

Teaching Assistant

To be announced

Team roster is being finalized

Frequently asked questions

Industry collaboration

Have a real dataset or challenge?

✉ mohammad.khalooei@sharif.edu