The Business and Data Analytics tracks consists of a number of advanced courses in the field of Data Science. In order to fulfil their obligations, students have to successfully follow a number of courses that sums up to 30 ECTS per semester. During the 1st and 2nd semester, the track offers 6 courses in each, and students may also select 1-2 courses from other tracks. 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:

CDS107: Data Analytics and Machine Learning

(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

CDS108: Information Systems Management and Innovation

(ECTS: 6)
Course description:
IT Project Management (project scheduling, resource planning, cost planning). Economics of Project Management. Security management of information systems, innovative e-services and supply chain services. Security governance of enterprises and smart ecosystems. Lab hours with MS Project, open-source risk management tools.

Course coordinator: Prof. Nineta Polemi

CDS109: Optimization Techniques

(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

CDS110: Big Data Management

(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

CDS111: Computational Tools for Business Analytics

(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

CDS112: Algorithms and Complexity

(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


Second Semester:

CDS207: Mathematical Methods for Business Analytics

(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

CDS208: Deep Learning (with Applications in Cybersecurity and Analytics)

(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

CDS209: Geospatial Data Management and Analytics

(ECTS: 6)
Course description:
Geographical information models and representation techniques. Spatial database management systems (logical – physical level). Geospatial data analytics (understanding. preprocesssing, 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

CDS210: Visual Analytics

(ECTS: 3)
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

CDS213: Graph and Network Analytics

(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

CDS214: Time-Series Analytics and Forecasting

(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