The course objective is to equip students with a basic knowledge of the Java programming language. Topics included are basic code writing, data types, variables, arrays, operators, loops and conditional statements.
This course builds on the knowledge acquired during the prerequisite "Introduction to Java programming - fundamentals" course. By the end of this course, students will be familiar on how to program in Java using methods, classes, objects, references and inheritances.
The course starts by describing the different sources of accessible data ( HTML, binary, XML, NoSQL). Subsequently, with the help of examples, the student is guided through the process of importing and elaborating different types of data. Topics include : Java classes for data import : text files/CSV, Java data connectivity (JDBC) and MySQL applications.
This course explores statistical based applications in Java employing the "Apache commons math" library. This is a library of lightweight, self-contained mathematics and statistics components addressing the most common problems not available in the Java programming. Applications include: means, variances and other summary statistics computation, statistical tests, simple and multivariate regression and line fitting.
This course explores statistical based applications in Java employing the "Apache commons math" library. This is a library of lightweight, self-contained mathematics and statistics components addressing the most common problems not available in the Java programming. In this video students, will learn how to generate random samples, random numbers, random vectors and random strings.
The course introduces topics in machine learning algorithm development in Java. Using the WEKA library the most important topics in Machine learning algorithm development are discussed: classification (Neural networks, KKK) and clustering (DB-Scan, K-means).
Using the Apache common maths package, this course provides a few transformers for signal analysis. All transformers provide both direct and inverse transforms.
This is a first course in the programming software R. Using the IDE R-studio, students will be exposed to syntax, software and interface installation and the writing of basic commands. Topics included are functional programming, matrix algebra, lists, data frames, package extension, basic statistics, graphics.
This course covers introductory topics in time series analysis in R. Emphasis will be placed on both theoretical and empirical (example based) applications.
This is a beginners course in python programming. Topics covered are: Installation of python, the python interpreter and the script mode, basic code writing, data types and conditional statements. The student will also be exposed to the installation and functioning of IDE such as Anaconda and Spyder.
Object-oriented programming (OOP) is a programming language model organized around objects rather than “actions” and data. In this course, students will be exposed to the following topics in OOP: classes, inheritance, modules, methods, constructors, exception handling, meta programming and slots.
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