Business and Data Analytics (BDA)
The Business and Data Analytics track consists of a number of advanced courses in the field of Data Science. In order to fulfill their obligations, students have to successfully follow a number of courses that sums up to 30 ECTS per semester. During the 1st semester the track offers 7 courses and during the 2nd semester, the track offers 6 courses (both compulsory and elective courses). The students may also select 1-2 courses from other tracks as elective courses, in order to sum up to 30 ECTS per semester. The 3rd semester is devoted to the Diploma Thesis in a state-of-the-art Data Science topic, supervised by one of the instructors of the track.
Track Courses:
First Semester:
(ECTS: 6)
Course description:
Introduction to data analytics (principles, pipeline, pre-processing). Common Machine Learning methods (classification, clustering). Neural networks and Deep Learning. Advanced clustering techniques (DBSCAN, OPTICS, etc.). Applications on Text / audio / video data mining. Lab hours with Python, R.
Course coordinator: Prof. Aggelos Pikrakis
ECTS: 3
Course description: IT Project Management (project scheduling, resource planning, cost planning). Economics of Project Management. Lab hours with MS Project, open-source management tools.
Course coordinator: Prof. Dimitris Apostolou
(ECTS: 6)
Course description:
Introduction to mathematical modeling and optimization. Constrained optimization. Convex and non-convex data hulls. Optimization techniques for data analysis (Frontier analysis and data envelopment analysis models and applications). Data envelopment analysis with streaming data. Lab hours with EMS, LP Solve.
Course coordinator: Prof. Dimitris Despotis
(ECTS: 6)
Course description:
Introduction – review of relational and object-relational databases. Modern trends in database design. Non-traditional data types (text, multimedia, spatial information). Non-traditional database architecture (sensor networks, data streams, distributed, in the cloud). The “big data” era (MapReduce architecture, etc.). Lab hours with PostgreSQL, MongoDB, Spark (Batch Processing, Streaming, MLib).
Course coordinator: Prof. Yannis Theodoridis
(ECTS: 3)
Course description:
Business analytics with Python (processing and storing of data, use of machine learning and optimization algorithms, visualization of results). Methods, algorithms and case studies of business analytics for portfolio management and optimisation. Methods, algorithms and case studies for business analytics in Industry 4.0. Lab hours with Pyomo (optimization modeling language), SymPy (Perform symbolic math computations), Gurobi Optimizer (mathematical programming solver).
Course coordinator: Prof. Dimitris Apostolou
(ECTS: 3)
Course description:
Basic techniques for algorithm design and analysis. Complexity classes. Algorithms for computational intractable problems. Elements of algorithmic game theory. Machine learning- based algorithm design.
Course coordinator: Prof. Charalampos Konstantopoulos
ECTS: 3
Course description:
The course presents common and state-of-the-art Deep Neural Network architectures, including: Multilayer Perceptrons, Convolutional architectures, Recurrent Neural Networks, Attention mechanisms and Transformers. In addition, the course discusses training related concepts and issues that are crucial for gaining a firm understanding of the underlying principles of the most important training procedures, like the mechanics of the backpropagation algorithm, parameter tuning and convergence/overfitting issues. The course evolves across five lectures and student progress is assessed by means of a team project deliverable.
Course coordinator: Prof. Aggelos Pikrakis
Second Semester:
(ECTS: 6)
Course description:
Analysis of Variance (ANOVA). Regression analysis. Principal component analysis, Factor analysis. Convex optimization. Integer programming and combinatorial optimization. Nonlinear programming (heuristic methods, parametric techniques, approximation algorithms). Dynamic programming. Lab hours with MATLAB.
Course coordinator: Dr. Gregoris Koronakos
(ECTS: 3)
Course description:
Neural network concepts (perceptron, feed-forward networks, cost functions, training and validation). Deep NN architectures (MLPs, Convolutional, Recurrent, etc.). Applications in business and data analytics. Applications in cybersecurity and embedded systems. Lab hours with Tensorflow, Keras, PyTorch.
Course coordinator: Prof. Aggelos Pikrakis
(ECTS: 3)
Course description:
Geographical information models and representation techniques. Spatial database management systems (logical – physical level). Geospatial data analytics (understanding. preprocessing, storage, knowledge discovery, visualization). Mobility data processing and analytics. Data Science challenge (Kaggle). Lab hours with PostGIS, Apache Sedona (ex- GeoSpark), Python (GeoPandas, MovingPandas).
Course coordinator: Prof. Yannis Theodoridis
(ECTS: 6)
Course description:
Introduction to data and information visualization. Data visualization design process and models – Visual problem solving. Visualizing patterns over time, visualizing proportions, visualizing graphs and networks, visualizing geographical data on maps. Interactive visualization techniques. Visualization systems and techniques for Big Data and AI. Lab hours with JS (D3, ChartJS, HighCharts), Python (Matplotlib, Plotly), R (ggplot2).
Course coordinator: Dr. George Papastefanatos
(ECTS: 3)
Course description:
Introduction. Graph theoretic centrality measures. Community detection algorithms. Network evolution models. Co-authorship networks. Twitter streaming API. Sentiment analysis. Opinion formation models. Lab hours with MATLAB, Python.
Course coordinator: Prof. Dionisios Sotiropoulos
(ECTS: 3)
Course description:
Introduction – basic concepts of time-series. Common time-series models (linear, autoregressive, ARMA, ARIMA, etc.). Forecasting with Neural Networks (e.g., LSTM models). Selected advanced methods (e.g., Facebook’s Prophet). Forecasting validation and quality measures. Data Science challenge (Kaggle). Lab hours with R, Python (scikit-learn), TensorFlow (Keras), PyTorch.
Course coordinator: Prof. Aggelos Pikrakis