SEDS 501 Introduction to Data Science

A.1. Homepage: https://tolgaayav.gitlab.io/courses/seds501/
A.2. Lecture: Friday 16:30 - 19:15
A.3. Credits: 3+0
A.4. Instructor: Prof. Dr. Tolga AYAV (Telephone: 750 7878)
A.5. Lab Assistants:
A.6. MS-Teams Code: 8lwuevl

B.1. Objective of the Course:

To introduce the topics of data science.

B.2. Prerequisites:

No prerequisite.

B.3. Recommended or Required Reading:

The coding assignments will be given in Python so a basic knowledge of Python is required.

C.1. Course Contents:

This course covers the fundamentals of statistics, data preparation, feature engineering, classification and regression techniques, clustering, neural networks, introduction to deep learning, machine learning and NLP tehcniques.

C.2. Course Schedule:

(TENTATIVE)

Week 1 Introduction
Week 2 Preliminaries (Mathematics and Statistics)
Week 3 Data Exploration
Week 4 Data Preparation
Week 5 Feature Engineering (1)
Week 6 Feature Engineering (2)
Week 7 Regression (1)
Week 8 Regression (2)
April 26th. Midterm Exam
Week 9 Classification (1)
Week 10 Classification (2)
Week 11 Neural Networks
Week 12 Metrics and Evaluation
Week 13 Text mining and NLP

Midterm Exam: April 26^th, 2024.
Final Exam: . ^th, 2024.

D. Lecture Notes:

E. Grading:

Written Midterm Exam: 35%
Written Final Exam: 45%
Assignments: 20%

F. Books:

Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python by Lau, Gonzalez, and Nolan, 2023.

Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python by Jason Brownlee, 2020.

Suggested Reading:

G. Assignments:

H. Project: