Academic courses

Advanced Regression Analysis

Computer-assisted analysis of business data; advanced multiple regression analysis, survey analysis, ANOVA testing for Marketing-type applications and Times Series Analysis methods will be covered.

Applied Modern Statistical Learning Methods

Overview of highly computational modern statistical learning methods; applications of logistic regression, neural networks, LASSO, trees, boosting and GAM, etc., to finance and marketing data.

Blended Data Business Analytics for Efficient Decisions

Build Analytical Models for Classification, Clustering and Association Problems. Leverage third party 'Big Data' for enriching and monetizing data. Develop data mining and business analysis.

Business Analytics

Foundational knowledge for business analytics, including strategies, methods, and tools integrated with hands-on skills for defining business analytics for data-driven decision making and innovation.

Business Models for Digital Platforms

Managing Business models in digital platform ecosystems, designing new products and services for digital platforms, establishing digital platform leadership, assessing emerging niches in digital spaces.

Communication for Management

Internal and external communication, research methods, reports for decision-making, oral presentations and briefings, strategies to assure communication; field studies.

Data Driven Decision Making

Data analysis technologies for business decision making; principles and techniques of statistical inference for business problem solving; foundations of data-driven regression and time series analytics.

Deep Learning for Business Applications

Apply machine learning tools to business. Write code to solve complex pattern recognition. Build strategies, technical planning, research and analyze data. Present complex technical data.

Internship in Data Sciences and Operations

Supervised on-the-job business experience in the student's area of interest.

NoSQL Databases in Big Data

NoSQL; semi-structured and unstructured databases; data storage; data manipulation; distributed databases.

Quantitative Investing

Build, test and implement the types of models in use by quantitative asset managers.

SQL Databases for Business Analysts

SQL; relational database systems; data storage; data manipulation; data aggregation.

Statistical Computing and Data Visualization

Data cleaning and reshaping; good vs. bad graphics; univariate, bivariate, trivariate, hypervariate and time series graphics; interactive graphics; web-related computing.

Text Analytics and Natural Language Processing

Acquire, analyze, visualize and perform natural language processing (NLP) on text data. Apply Python, machine learning packages, statistical methodology and computer code to business decision-making.

The Analytics Edge: Data, Models, and Effective Decisions

Decision making under uncertainty using real data applying the most advanced optimization, statistical and probability methods

Algorithm

Programming computers through structured algorithms, including programming concepts, fundamentals of algorithms, C programming structures, conditional structures, looping structures, and functions.

Algorithms Analysis

Algorithm design techniques which include criteria for the goodness of an algorithm, function growth rate, recursive functions, divide and conquer techniques, greedy techniques, dynamic programming, backward tracing techniques, as well as an introduction to NP-Complete theory.

Artificial Intelligence

Position and scope of artificial intelligence, rational agents, various methods of searching, knowledge representation, and matching, as well as probability-based representation and reasoning techniques (bayesian networks, decision networks, and decision trees)

Bioinformatics

The role of DNA and protein sequence information in understanding processes biology, resources (databases) and applications that are widely used in the field of bioinformatics, algorithms used to solve problems in the field of bioinformatics, especially those related to DNA and protein sequences, such as sequence alignment problems and their data structures, algorithms for phylogenetic trees, and an introduction to the application of machine learning to bioinformatics.

Calculus

Derivatives of functions and their applications; integral function, transcendent functions, integration techniques along with applications of integrals and an introduction to differential equations with more emphasis on computational aspects.

Computer Application

Foundational knowledge computer components including input, output, processing, and data storage equipment, as well as software including application software, operating systems, utility programs, databases, information systems, basics of computer networks and the internet, and computer security.

Computer Ethics

Aspects related to computer crime and computer security, theft of software and intellectual property rights, interference with computers and information systems, invasion of privacy in the workplace and the internet, social implications related to artificial intelligence and expert systems, and information technology marketing issues.

Computer Graphics

Computer graphics techniques include hardware, algorithms, graphic programming techniques for simulation visualization, line rasterization algorithms, transformations, various types of projections, 3D object representation, creating objects with fractals and particle systems, color concepts, lighting, texture mapping, animation, and interactive techniques.

Computer Organization and Architecture

Characteristics of modern computer systems in terms of structure, function and interconnection of the main computer components, namely processor, memory and input/output devices, including the use of assembly language.

Data Base

Comparison between data storage with file systems and databases, general view of database systems, database models, entity relationship (ER) models, relational models, relational algebra, normalization, structured query language (SQL), database programming using stored procedures and triggers, as well as database design and database implementation in various cases.

Data Communication and Computer Network

Computer networks and their services, switching and routing techniques, Internet applications (web, mail, FTP, proxy, DNS), types of services, socket programming, basics of analog and digital communication systems, transmission systems, synchronous communication / asynchronous, symmetric / asymmetric communication, network architecture and protocols, OSI reference model, LAN standards, peer-to-peer, TCP/IP, security, advanced network architecture, as well as the basics of SNMP-based network management and QoS.

Data Mining

Understanding of data mining, data and data exploration, data preprocessing, basic techniques in clustering and outlier detection, basic classification techniques, basic techniques in association rules mining, understanding data warehouse and online analytical processing (OLAP), introduction to data mining techniques in other data types including spatial, spatio-temporal, sequence, web and text data.

Data Structures

Data abstraction in a structure to support data processing in computers as well as several important algorithms related to data processing and abstraction, such as sorting, hash functions, and recursive calculations.

Design of Experiments

Introduction to experimental design and several standard experimental designs, single factor experiments in Completely Randomized Designs, Completely Randomized Group Designs, Latin Square Designs, comparisons between treatments, assumption testing, factored experiments, split plot designs and split group designs, as well as analysis of variance.

Digital Circuit

Number systems, forms of binary code, understanding binary logic, forms and workings of logic gates, Huntington's Postulates and basic theory of Boolean Algebra, simplification of Boolean functions using basic theory of Boolean Algebra and Karnaugh Map (K-Map), types of integrated circuits , combinational circuits, sequential circuits, and how counters and storage elements (registers) work.

Digital Image Processing

Characteristics of digital images, digital image processing includes image formation, image restoration, image quality improvement, image transformation in frequency space, image compression, image segmentation, image morphology as preparation for image recognition.

Discrete Structure

Basic principles of counting, logic, set theory, relations and functions, sigma and phi notation, principles of induction, properties of integers, introduction to computational theory, recursive relations and introduction to graph theory, as well as basic algorithms in graphs.

General Economics

General overview of economics, economic actors, demand, supply, budget lines and indifference curves, production and costs, market structure, key macroeconomic variables, national income, changes in national income, fiscal policy and monetary policy.

Human and Computer Interaction

Basic theories underlying human-computer interaction, principles and application of human-computer interaction to user interface design, importance and role of usability and evaluation in system design, issues related to user diversity, different system types, styles interaction, tools and environment.

Information Systems

Definition of information systems, their role in organizations, types of information systems based on organizational levels and functional areas, stages of information systems development, issues (ethical, social and political) that arise from the implementation of information systems, information technology infrastructure (hardware, software, data and communication networks), and business process integration.

Intelligent System

Machine learning concept with an algorithmic approach. The concepts discussed include computational intelligence paradigms (artificial neural networks: MLP, Radial Basis, SOM and LVQ; fuzzy systems; colony and evolutionary intelligence-based optimization), transformation-based classification techniques and constrained optimization (support vector machine), and prediction techniques using Monte Carlo simulation.

Intoduction to Geospatial Technology

Technology used to capture, store, query, analyze and display geospatial data, theory of location determination, coordinate systems, spatial and cartographic data modeling, as well as geographic information systems (GIS) that implement web-service architecture.

Introduction to Probability

Sample and event spaces, combinatoric analysis, probability axioms and probability postulates, conditional probability and Bayes' Theorem, random variables and their distribution functions, joint probability distribution.

Introductory Mathematics

Mathematical logic (truth of propositions, arguments, propositions with quantifying terms, mathematical induction), combinatorics (law of multiplication, law of addition, permutation and combination), matrices, systems of linear equations, inequalities and absolute values, functions and models as well as limits and continuity with more emphasis on the calculation aspect.

Linear Algebra

Real vector space; linear transformation; orthogonality and eigenvalues.

Numerical Computation

Basic principles of numerical methods, design and analysis of algorithms for numerical methods in solving numerical computing problems, as well as implementing these algorithms efficiently using certain programming languages.

Object Oriented Systems Developments

Object-oriented system development concepts and techniques include objects, classes, encapsulation concepts, inheritance, information hiding, polymorphism, object-oriented analysis and design using the Unified Modeling Language, design patterns, antipatterns, object-oriented system testing, object persistence, and measurement. object-oriented goodness.

Operating Systems

Basic elements of computer systems and instruction execution, operating system structure, process management, multithreaded programming, process scheduling, synchronization, deadlock problems and their handling, memory management which includes swap memory, paging, segmentation, and virtual memory, storage management, file systems, and I/O management.

Optimization Techniques for Computing

Fundamental topics in calculus, as well as its application to simple computing problems. Fundamental topics in calculus that are discussed include the introduction of functions, related to sets of functions with single variables and multivariables, function derivation terminology and its application, multi-variable function derivation terminology as well as an introduction to methods for solving solutions of functions without constraints and functions with a constraint.

Programming Language

Principles of programming language design including syntax, naming, types, semantics, and functions, programming language paradigms, including imperative programming, functional programming, logic programming, and object-oriented programming, comparison of basic principles and implementation of various programming language paradigms.

Quantitative Methods

Basics and analysis techniques in experiment design, data collection techniques, linear modeling techniques, dimension reduction and clustering techniques, introduction to artificial neural networks, introduction to fuzzy logic and kernel functions for parameter estimation.

Regression Analysis

Concept of relationship between variables (qualitative vs quantitative, stochastic vs deterministic), linear relationship between two variables (correlation vs regression), simple linear regression models (parameter estimation, interpretation of regression coefficients, hypothesis testing, prediction, and coefficient of determination), checking assumptions ( residual plot, normal plot), regression without intercept, regression with a matrix approach, multiple regression, polynomial regression, model testing (including general linear hypothesis), sequential test, partial test, regression with dummy variables, and procedures for selecting the best regression model.

Sampling Techniques

Several sampling techniques and parameter estimation. Chance sampling includes simple random sampling, stratified random sampling, systematic random sampling, clustered random sampling and two-stage clustered random sampling.

Software Engineering

Software engineering, software definition, process-oriented and object-oriented software development methods, stages in software engineering which include requirements analysis, modeling analysis results, design, implementation with selected programming techniques, testing both black box and white box and maintenance.

Software Project Management

Characteristics of software systems and project management principles which include requirements elicitation, estimating software development efforts, professional ethics, quality control, planning and scheduling in the software process life cycle, team work and risk management.

Statistics

Basic principles of statistical methods and some simple analysis methods. The topics covered in this course are statistical descriptions, probability, principles of estimating and testing hypotheses, estimating and testing hypotheses regarding proportions, estimating and testing hypotheses regarding mean values, correlation, simple linear regression, and contingency tables.