2. Professors
  3. RARITA' Luigi
  4. Teaching

ALGORITMS AND MARKETING FORECASTING LAB

Luigi RARITA' ALGORITMS AND MARKETING FORECASTING LAB

0323200015
DEPARTMENT OF POLITICAL AND COMMUNICATION SCIENCES
EQF7
DIGITAL MARKETING
2025/2026

OBBLIGATORIO
YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2024
AUTUMN SEMESTER
CFUHOURSACTIVITY
945LAB
LUIGI RARITA' T Curriculum
Objectives
THE COURSE AIMS TO PROVIDE STUDENTS METHODS, METHODOLOGIES AND TECHNIQUES THAT ALLOW TO PROCESS AND INTERPRET DATA DEALING WITH THE CUSTOMERS’ BEHAVIOUR, PREFERENCES AND NEEDS.
STUDENTS WILL ACQUIRE PRACTICAL SKILLS ON QUANTITATIVE TECHNIQUES AND OPERATIONAL RESEARCH MODELS TO ANALYSE DIGITAL DATA, IMPLEMENT PREDICTIVE MARKETING STRATEGIES, CARRY OUT INVESTIGATIONS ABOUT THE BEHAVIOUR OF CONSUMERS, USEFUL TO UNDERSTAND MARKET PROBLEMS, IDENTIFY EMERGING TRENDS AND CHANGES IN THE DEMAND OF CONSUMERS, PROVIDING USEFUL INFORMATION TO ADAPT MARKETING STRATEGIES AND PLAN FUTURE ACTIVITIES.
THE COURSE WILL FOCUS ON THE PRACTICAL APPLICATION OF SUCH METHODS THROUGH THE USE OF SOFTWARE TOOLS AND REAL-WORLD CASE STUDIES.

KNOWLEDGE AND UNDERSTANDING
THE STUDENT WILL ACQUIRE KNOWLEDGE ABOUT TECHNIQUES AND MODELS AIMED AT STUDYING THE CONSUMER AND THE ABILITY TO COLLECT AND SYNTHESIZE QUANTITATIVE AND QUALITATIVE DATA DEALING WITH MARKETING PROBLEMS.
APPLYING KNOWLEDGE AND UNDERSTANDING
THE STUDENT WILL BE ABLE TO:
-APPLY THE ACQUIRED KNOWLEDGE IN REAL CONTEXTS;
-CHOOSE THE MOST SUITABLE TECHNIQUES FOR THE RESOLUTION OF THE PROPOSED PROBLEMS;
-GROUP ELEMENTS WITH SIMILAR CHARACTERISTICS BY BUILDING AND INTERPRETING CLUSTER ANALYSIS MODELS;
-IMPLEMENT PREDICTIVE MARKETING STRATEGIES;
-CARRY OUT INVESTIGATIONS ABOUT THE BEHAVIOUR OF CONSUMERS;
-PERFORM ANALYSES USING STATISTICAL SOFTWARE.

MAKING JUDGEMENTS
THE STUDENT WILL BE ABLE TO:
-IDENTIFY THE MOST APPROPRIATE METHODS TO EFFICIENTLY SOLVE PROBLEMS IN A WORK CONTEXT;
-EXPRESS AUTONOMOUS EVALUATIONS ABOUT THE VALIDITY AND FEASIBILITY OF DIFFERENT TECHNIQUES AND UNDERSTAND THEIR IMPACT ON THE RESULTS OF THE ANALYSES.

COMMUNICATION SKILLS
THE STUDENT WILL BE ABLE TO COMMUNICATE THE RESULTS OF THE INTERPRETATION OF DATA AND OF THE CONDUCTED ANALYSES BOTH TO PROFESSIONALS IN THE SECTOR AND TO NON-EXPERTS IN THE SUBJECT.

LEARNING SKILLS
THE STUDENT WILL BE ABLE TO APPROACH PROBLEMS CRITICALLY AND APPLY THE KNOWLEDGE AND SKILLS ACQUIRED USING TECHNOLOGICAL TOOLS IN DIFFERENT SITUATIONS THAN THOSE PRESENTED IN THE COURSE.
Prerequisites
FOR AN EASIER UNDERSTANDING OF THE COURSE CONTENTS, BASIC MATHEMATICAL KNOWLEDGE AND SKILLS IN MATHEMATICAL ANALYSIS, LINEAR ALGEBRA, PROBABILITY THEORY AND STATISTICS ARE IMPORTANT.
MANDATORY PREPARATORY TEACHINGS
NONE.
Contents
DATA ANALYSIS TECHNIQUES
REGRESSION
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
SIMPLE AND MULTIPLE LINEAR REGRESSION: ESTIMATION OF THE FUNDAMENTAL PARAMETERS, EVALUATION OF THE DEGREE OF GOODNESS OF THE MODEL. SIMPLE AND MULTIPLE LOGISTIC REGRESSION: ESTIMATION OF THE FUNDAMENTAL PARAMETERS, EVALUATION OF THE DEGREE OF GOODNESS OF THE MODEL. PRACTICAL APPLICATIONS IN MARKETING FORECASTING PROBLEMS.
CLUSTER ANALYSIS
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
GENERALITIES. DISTANCE MATRIX. STEPWISE CLUSTER ANALYSIS. ALGORITHMS AND SOFTWARE TOOLS FOR CLUSTER ANALYSIS. PARTITIONAL ALGORITHMS: SEQUENTIAL CLUSTERING, CENTER-BASED CLUSTERING, MODEL BASED CLUSTERING. AGGLOMERATIVE HIERARCHICAL ALGORITHMS: COMPLETE LINK, SINGLE LINK. USE OF CLUSTER ANALYSIS TECHNIQUES TO IDENTIFY HOMOGENEOUS GROUPS OF CONSUMERS.
FORECASTING TECHNIQUES
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
FORECASTING METHODS: GENERALITIES; ELEMENTS OF CHOICE OF THE FORECASTING METHOD; SALES FORECASTING METHODS; MAIN SELF-PROJECTIVE MODELS.
TIME SERIES ANALYSIS: GENERALITIES; THE DECOMPOSITION PHASES OF A TIME SERIES.
THE MAIN OPERATIONAL RESEARCH MODELS
DESCRIPTIVE MODELS
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
MARKOV PROCESS MODEL: GENERALITIES; METHODOLOGY; APPLICATION TO THE MARKET ANALYSES.
SIMULATION: GENERALITIES; SIMULATION MODEL; SIMULATION WITH DISCRETE MODELS; PROGRAMMING LANGUAGES FOR DISCRETE SIMULATION; THE DYNAMICS OF SYSTEMS AND APPLICATIONS.
WAITING QUEUES MODEL: GENERALITIES; DISTRIBUTION OF ARRIVALS AND SERVICE TIMES; ESTIMATION OF QUEUE’S PARAMETERS.
DECISION-MAKING MODELS
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
MATHEMATICAL PROGRAMMING: GENERALITIES; LINEAR PROGRAMMING; NON-LINEAR PROGRAMMING; DYNAMIC PROGRAMMING; GRAPH THEORY; APPLICATIONS.

TOTAL LECTURE/ PRACTICE/LABORATORY HOURS 10/15/20
Teaching Methods
THE COURSE WILL BE STRUCTURED THROUGH THEORETICAL LESSONS, EXERCISE SESSIONS, PRACTICAL LABORATORY SESSIONS WITH THE USE OF SPECIALIZED SOFTWARE, AND CASE STUDIES.
STUDENTS WILL BE REQUIRED TO WORK IN GROUPS TO APPLY THE LEARNED CONCEPTS TO REAL-WORLD MARKETING SITUATIONS.
ATTENDANCE OF CLASSROOM LESSONS AND EXERCISES, ALTHOUGH NOT MANDATORY, IS HIGHLY RECOMMENDED TO FULLY ACHIEVE THE LEARNING OBJECTIVES.


Verification of learning
THE EXAM, WHICH FORESEES A MARK OUT OF THIRTY, CONSISTS OF THE DEVELOPMENT AND PRESENTATION OF A PROJECT TO BE DEVELOPED AND SOLVED WITH THE MATHEMATICAL AND STATISTICAL TOOLS PRESENTED IN THE COURSE.
STUDENTS WILL BE ASSESSED IN TERMS OF:
-CORRECTNESS OF THE TECHNICAL LANGUAGE;
-FORMALIZATION OF SUITABLE CONCLUSIONS FOR THE ANALYSIS OF REAL CASE STUDIES;
-QUALITY OF THE CONTENTS DESCRIBED DURING THE ORAL TEST.
LAUDE WILL BE AWARDED TO STUDENTS WHO SHOW EXCELLENT KNOWLEDGE OF THE COURSE CONTENTS, EXCELLENT PRESENTATION SKILLS AND THE ABILITY TO APPLY THE KNOWLEDGE ACQUIRED TO SOLVE PROBLEMS THAT HAVE NOT BEEN DESCRIBED DURING THE COURSE.
Texts
THE COURSE TEACHING NOTES WILL BE AVAILABLE IN THE DEDICATED TEACHING SECTION WITHIN THE UNIVERSITY’S E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT), ACCESSIBLE TO COURSE STUDENTS VIA THEIR UNIQUE UNIVERSITY CREDENTIALS.
More Information
THE COURSE IS TAUGHT IN ITALIAN.
Lessons Timetable

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