Object-oriented Programming for Data Science

After Attending this course students will be able to

• apply the advanced principles and concepts of Object Oriented Programming and develop rograms for basic data analytics,
• design and write object oriented programs using advanced Object Oriented constructs in Python,
• modify an existing Object-Oriented program,
• extensively test an Object-Oriented program,
• construct Program Libraries,
• demonstrate an understanding of the underlying principles and concepts of Object-Oriented Programming,
• document an Object-Oriented program, and
• perform basic data analytics,

by

• understanding abstract data types, classes, objects, messages, Instance variables, methods, encapsulation, private and public access, class variables, constructors, class interface, class implementation.
• understanding classes and objects, private and public class members, constructors, initialisation list, static data members, overloading, inline, separation of interface and implementation.
• understanding Data structures, iterators and containers.
• using error handling and debugging practices

in order to

• solve data science tasks by developing fast and reliable object-oriented software, and
• be qualified for a professional career as automation engineer developing digitalindustrial solutions.

a) Classes, Abstract Classes, Methods, Variables

• Abstract data types, classes, objects, messages, Instance variables, methods, encapsulation, private and public access, class variables, constructors, class interface, class implementation.
• Classes and objects, private and public class members, constructors, initialisation list, static data members, overloading, inline, separation of interface and implementation.

b) Composition

• Data structures, iterators and containers.
• Design, code and test a series of object-oriented programs to re-enforce lecture content.

c) Destructors

• Exception handling.
• Function overloading. Operator overloading.
• Generic Types, Static and Dynamic Binding, Polymorphism, Overloading.
• Inheritance: Types of Inheritance, Construction, Destruction, Multiple Inheritance.

d) Data analysis functions

• Data Preprocessing (Outlier Removal, Filters, Smoothening, etc.)
• Regression functions
• Classification methods
• Machine Learning models i.e. Random Forest
• AI models i.e. Neural Networks

Seminaristischer Unterricht, Projektarbeit, Fallstudien

• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (ISBN: 1491957662)
• Einstieg in Python: Die Einführung für Programmieranfänger, inkl. Objektorientierung (ISBN: 3836273799)
• Machine Learning mit Python und Keras, TensorFlow 2 und Scikit-learn (ISBN: 374750213X)