At the end of this course, the students; 1) They will see general information about Python Programming Language, creating functions and loops, writing basic code, and functions of basic packages (modules). 2) They will see the general use of Spyder and JupyterLab interfaces and the basic code writing on them. 3) Data parsing and merging, 4) Data visualization, 5) They will learn some data mining techniques both theoretically and practically.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
NA
COURSE DEFINITION
It is a course for learning Python programing language and making applications on Basic Data Mining techniques.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction to Python
2nd Week
Data Structures in Python
3rd Week
Conditional Expressions and Loops
4th Week
Input Structure, Functions in Python
5th Week
Some Functional Packages (Modules)
6th Week
Numpy and Pandas Packages
7th Week
Data Manipulation
8th Week
Mid-term examination
9th Week
Data Visualization
10th Week
Introduction to Data Mining, Current Issues on Data Science
11th Week
Data mining technique 1 (theoretical and applied)
12th Week
Data mining technique 2 (theoretical and applied)
13th Week
Data mining technique 3 (theoretical and applied)
14th Week
General review and presentations
RECOMENDED OR REQUIRED READING
"Data Mining: Concepts and Techniques", Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2001. "Data Mining: Practical Machine Learning", Ian H. Witten, Eibe Frank, Morgan Kaufmann, 2000. "Principles of Data Mining (Adaptive Computation and Machine Learning)", D Hand, MIT Press, 2001.
