DEGREE CURRICULUM BIG DATA ANALYTICS SPECIALITY. MASTER in Informatics Engineering

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1 DEGREE CURRICULUM BIG DATA ANALYTICS SPECIALITY MASTER in Informatics Engineering

2 Module general information Module name BIG DATA ANALYTICS SPECIALITY Typology Optional ECTS 18 Temporal organization C1S2 and C2S1 Language English Degree Master in Informatics Engineering Coordination Carles Mateu addres Learning objectives At the end of the module, students will possess the skills necessary for utilizing tools (including deploying them on Hadoop/MapReduce) to handle a variety of big data analytics, and to be able to apply the analytics techniques on a variety of applications. This main aim can be divided in the following more specific learning objectives: Acquire a general understanding of the challenges of Big Data Analytics. Decide and adequate distributed architecture designed to address a problem that involves Big Data. Choose a suitable technology to implement that architecture. Decide and design a suitable persistence paradigm to respond to a problem involving Big Data. Choose the appropriate database technology to implement this paradigm. Understand and apply statistical techniques of data mining. Use appropriately statistical packages for data analysis. Understand and apply statistical techniques related to the operation of Big Data. Use appropriately statistical packages for such exploitation. Proposes adequate visualization of the analysed information that facilitates understanding. Know the common applications in the area of big data and be able to develop solutions for these problems. Systematically deploy the set of common techniques for solving big data problems. Communicate effectively the results of the project to technical partners and customers. 2/5

3 Significant competences CG4. Capacity to mathematically model, calculate and simulate in technological companies and engineering centres, particularly with regard to research, development and innovation tasks in all fields related to computer engineering. CG8. Capacity to apply the knowledge acquired for solving problems in new and unfamiliar situations within broader and more multidisciplinary contexts, and to be capable of integrating this knowledge. CB3. Students are able to integrate knowledge and handle complexity, and formulate judgments based on information that was incomplete or limited, include reflecting on social and ethical responsibilities linked to the application of their knowledge and judgments. CB4. Students can communicate their conclusions -and the knowledge and rationale underpinning these, to specialist and non-specialist audiences clearly and unambiguously. CE1. Capacity for the integration of technologies, applications and computer engineering systems, in general and in wider and multidisciplinary contexts. CE4. Capacity to model, design, define the architecture, implant, manage, operate, administer and keep applications, networks, systems, services and computer contents. CE10. Capacity to understand and apply advanced knowledge in high-performance computing and numerical or computational methods to problems of engineering. CE12. Capacity to apply mathematical, statistical and artificial intelligence methods, design and develop applications, services, intelligent systems and systems based on knowledge. Module Contents This course proposes the analysis and interpretation of information characterized by its large volume and / or high flow of acquisition and / or diversity. This analysis is usually done to improve decision-making, discovering underlying information and the optimization of processes of an organization. In this module the architectures, techniques and tools that facilitate their approach are taught. Some of the areas where these techniques are applied are called Business Intelligence, Web Analytics, Smart cities, analysis of health data, public transport, blogs, etc. Moreover, one of the applications of these techniques with special interest in the economic environment of the city of Lleida is AgroSmart, a scientific area oriented to data analysis in the food sector. The Big Data Analytics module has been divided into three subjects of 6 ECTS each to help achieve the learning outcomes mentioned above. The contents of these subjects are: 3/5

4 I. Massive data processing: A. Data Gathering & formating: i. Introduction ii. Open Data & Linked Data iii. Internet Data Collection iv. IoT as data source v. Data Crowdsourcing vi. Data formats vii.data correcting and cleanliness B. Data storage and processing: i. Hadoop ii. MapReduce Paradigm iii. Introduction to Apache Spark II. Data mining A. Techniques and algorithms (data preparation, classification, aggregation, association rules) B. Techniques and algorithms associated with Big Data (regression, dimension reduction, bootstrap, unstructured data, statistical sampling for Big Data, use of the application R for Big Data problems) C. Information visualization. III. Big Data project: A. Big Data applications (eg., AgroSmart, Linked Open Data, Business Intelligence...). B. Big data project development. The students have to tackle with the deployment phases of a Big data project: (1) Problem; (2) Data collection; (3) Solution Implementation; (4) Analysis; (5) Presentation of results. C. Analysis of Big data Challenges and opportunities. 4/5

5 Methodology and Training activities Training activity Hours Presentiality (%) Lectures Laboratory practices Tests Seminars and conferences Oral expositions and debate Reports 20 0 Autonomous study 82 0 Autonomous work mentored Autonomous problem resolution 50 0 Autonomous practices resolution Evaluation Methodology Evaluation system Minimum weight Maximum weight Written tests Practices and exercises Oral presentations /5