Luigi TROIANO | COMPUTER SCIENCE LABORATORY
Luigi TROIANO COMPUTER SCIENCE LABORATORY
cod. 0212800021
COMPUTER SCIENCE LABORATORY
0212800021 | |
DEPARTMENT OF ECONOMICS AND STATISTICS | |
EQF6 | |
STATISTICS FOR BIG DATA | |
2024/2025 |
YEAR OF COURSE 3 | |
YEAR OF DIDACTIC SYSTEM 2018 | |
SPRING SEMESTER |
SSD | CFU | HOURS | ACTIVITY | |
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ING-INF/05 | 5 | 30 | LAB |
Exam | Date | Session | |
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TROIANO | 03/02/2025 - 11:00 | SESSIONE ORDINARIA | |
TROIANO | 03/02/2025 - 11:00 | SESSIONE DI RECUPERO | |
TROIANO | 07/04/2025 - 11:00 | SESSIONE ORDINARIA | |
TROIANO | 09/06/2025 - 11:00 | SESSIONE ORDINARIA | |
TROIANO | 30/06/2025 - 11:00 | SESSIONE ORDINARIA | |
TROIANO | 18/07/2025 - 11:00 | SESSIONE ORDINARIA | |
TROIANO | 12/09/2025 - 11:00 | SESSIONE DI RECUPERO |
Objectives | |
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THE OBJECTIVE OF THE COURSE IS TO INTRODUCE THE STUDENT TO THE DEVELOPMENT OF ALGORITHMS FOR BIG DATA PROCESSING. PROBLEMS IN VARIOUS AREAS OF BIG DATA APPLICATIONS WILL BE INTRODUCED, AND STRATEGIES AND SOLUTION MODELS WILL BE STUDIED. KNOWLEDGE AND COMPREHENSION DURING THE COURSE, THE STUDENT WILL ACQUIRE BOTH THEORETICAL AND PRACTICAL KNOWLEDGE OF PROBLEMS RELATED TO BIG DATA PROCESSING IN VARIOUS APPLICATION SECTORS. THIS WILL ENABLE THEM TO ANALYZE THE COMPUTATIONAL ASPECTS AND FIND SUITABLE ALGORITHMIC SOLUTIONS. THE OBJECTIVE IS TO LEARN HOW TO MAKE THE BEST USE OF THE VARIETY OF AVAILABLE SOLUTIONS, GUIDED BY AN UNDERSTANDING OF THEIR CHARACTERISTICS, INCLUDING PRACTICAL EXAMPLES OF APPLICATIONS. ABILITY TO APPLY KNOWLEDGE AND COMPREHENSION THE COURSE AIMS TO DEVELOP IN THE STUDENT AN AWARENESS OF DESIGNING AND IMPLEMENTING COMPUTATIONAL SOLUTIONS FOR BIG DATA THROUGH THEORETICAL STUDY AND PRACTICAL EXERCISES ON ASPECTS RELATED TO MANAGING THE COMPLEXITY OF DIFFERENT APPROACHES AND THE SPECIFIC CHARACTERISTICS OF DIFFERENT APPLICATION DOMAINS. |
Prerequisites | |
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THE TEACHING ASSUMES KNOWLEDGE OF PROGRAMMING; ALGORITHMS AND DATA STRUCTURES; DATA ANALYSIS AND VISUALIZATION; ARCHITECTURES FOR BIG DATA; PROBABILISTIC MODELS FOR DATA ANALYSIS. |
Contents | |
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PYTHON RECAP: 1. VARIABLES, DATA TYPES, AND OPERATORS. 2. CONTROL STRUCTURES: CONDITIONS, LOOPS, AND BREAK/CONTINUE STATEMENTS. 3. FUNCTIONS: DEFINITION, INVOCATION, AND PARAMETERS. 4. EXCEPTION HANDLING WITH TRY-EXCEPT. 5. DATA STRUCTURES: LISTS, DICTIONARIES, AND TUPLES. 6. ADVANCED TOPICS LIKE FILE HANDLING, OBJECT-ORIENTED PROGRAMMING, AND SPECIFIC MODULES. PANDAS: 1. INTRODUCTION TO PANDAS. 2. DATA LOADING AND MANIPULATION. 3. DATA CLEANING AND TRANSFORMATION. 4. DATA ANALYSIS. 5. DATA VISUALIZATION. 6. DATA EXPORTING. SCIKIT-LEARN: 1. INTRODUCTION TO SCIKIT-LEARN. 2. DATA PREPARATION. 3. MACHINE LEARNING MODELS. 4. MODEL EVALUATION. 5. MODEL OPTIMIZATION. 6. INTEGRATING SCIKIT-LEARN WITH OTHER TOOLS. OTHER LIBS 1. DATA SOURCES 2. DATA VISUALIZATION 3. STATISTICS |
Teaching Methods | |
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THE COURSE IS ORIENTED TOWARDS THE PRACTICAL APPLICATION OF TECHNIQUES FOR CODING ALGORITHMIC SOLUTIONS. |
Verification of learning | |
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THE EXAMINATION CONSISTS OF A PROJECT WORK, A LABORATORY TEST AND A WRITTEN TEST. THE PROJECT WORK, WHICH IS CARRIED OUT BY THE STUDENT INDIVIDUALLY OR IN A GROUP, CONSISTS OF A SMALL TEACHING PROJECT IN WHICH THE STUDENT WILL HAVE THE OPPORTUNITY TO TEST HIMSELF/HERSELF WITH THE APPLICATION OF THE TECHNOLOGIES LEARNT DURING THE COURSE AND TO PRESENT THE SOLUTION IN THE EXAM. THE WRITTEN TEST FOLLOWS THE WORKSHOP TEST AND CONSISTS OF 5 MULTIPLE-CHOICE QUESTIONS. IT HAS A DURATION OF 15 MINUTES AND IS DESIGNED TO CHECK THE LEARNING OF THE TECHNICAL AND METHODOLOGICAL NOTIONS EXPLAINED DURING THE COURSE. |
Texts | |
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LECTURE NOTES |
More Information | |
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REGULAR FREQUENCY IS REQUIRED FOR THE COURSE ACCORDING TO THE CRITERIA DEFINED BY THE DIDACTIC AREA. |
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