At the end of this course, the students; 1) Learn the basics of data science. 2) Learn the use of R and Python programming languages ??in data analysis. 3) Learn basic statistical methods and machine learning techniques required for big or small data analysis. 4) Learn basic data analysis techniques (data collection, cleaning, modeling and presentation. 5) Design and run experimental tests to evaluate hypotheses about data. 6) Learn and apply the use of deep learning techniques in biological sciences.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
In this course, it is aimed to convey the important properties of datasets, basic statistical modeling, web programming and basic techniques of data visualization. Throughout the semester, Python and R programming languages ??are taught through theoretical and applied courses and used in homework. It is aimed to teach machine learning and deep learning techniques in data analysis.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction to Data Science
2nd Week
Data Manipulation
3rd Week
Exploration of Data
4th Week
Statistics with R Programming
5th Week
Statistics with R Programming
6th Week
Statistics with R Programming
7th Week
Data Visualization
8th Week
Midterm Exam
9th Week
Feature Extraction and Selection
10th Week
Machine Learning
11th Week
Machine Learning
12th Week
Machine Learning
13th Week
Deep Learning
14th Week
Deep Learning
RECOMENDED OR REQUIRED READING
Biostatistics with R: An Introduction to Statistics Through Biological Data, 2012th Edition, Babak Shahbaba, Springer, 2011 Python Programming for Biology, First Edition, Tim j. Stevens, Wayne Boucher, 2014. An Introduction to Statistics with Python: With Applications in the Life Sciences, First Edition, Thomas Haslwanter, Springer, 2016. Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining, First Edition, Mario Cannataro, Pietro Hiram Guzzi, Giuseppe Agapito, Chiara Zucco, Marianna Milano, Elsevier, 2021.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Lecture,Discussion,Questions/Answers,Practice
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
25
Assignment
2
20
Practice
1
25
Total(%)
70
Contribution of In-term Studies to Overall Grade(%)
70
Contribution of Final Examination to Overall Grade(%)
30
Total(%)
100
ECTS WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
2
2
Preparation for Quiz
Individual or group work
Preparation for Final exam
1
30
30
Course hours
13
2
26
Preparation for Midterm exam
1
20
20
Laboratory (including preparation)
Final exam
1
2
2
Homework
2
10
20
Performance Practice
13
2
26
Total Workload
126
Total Workload / 30
4,2
ECTS Credits of the Course
4
LANGUAGE OF INSTRUCTION
English
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
