APPLIED DATA ANALYTICS

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Single discipline educational activity

Course Sheet

Course code:

ADAN21

Academic Year of enrolment:

2022

Year of supply:

2022/2023

Type of Course:

Related/Supplementary

Degree:

Course of 2nd Cycle Degree in DIGITAL MARKETING

Disciplinary Sector:

Data Processing Systems

Language:

Italian

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

MORBIDONI Christian

Cycle:

Second four months

Hours of classroom activity:

Individual study hours:

171

Prerequisites:

Knowledge of the basic concepts of statistics and of linear and logistic regression and clustering techniques are recommended for the correct understanding of the analysis processes that will be implemented. The aforementioned knowledge, which is the subject of the "Marketing statistics and metrics" course, will in any case be recalled and briefly discussed during the course.

Objectives

The course aims to provide the basic knowledge and tools to select and apply different data analysis techniques in order to satisfy different information needs of digital marketing.

Contents

The course first introduces the basic concepts relating to data representation, query and analysis, then focusing on the practical tools and methodologies used in marketing, and in particular in digital marketing.
The practical application of concepts and methods illustrated will be supported by demonstrations and exercises with commercial and open software, such as Brandwatch, for monitoring and analyzing Social Media, Excel for manipulating and visualizing data of various kinds and KNIME for the application of basic Machine Learning techniques.

Extended Syllabus

Data Analytics: Introduction to the concepts and representation of data
- The data analysis process, data-driven decision and types of analysis
- Data types and representation formats, structured and unstructured data
- Principles of relational databases and SQL

Web and Social Media
- Specific data analytics tools for digital marketing: Google Ads, Analytics, Trends, etc.
- Social media listening/monitoring tools:
- Extract data from the web and social networks: APIs, scrapers and open data

Data visualization and descriptive analytics
- Data visualization principles
- Exploratory analysis, graphs, reports and interactive dashboards, fundamentals and practical cases with Microsoft Excel.

Data Analytics workflows
- Introduction to KNIME, data preparation and composition of analysis workflows
- Review of regression and clustering analysis
- Examples carried out in class: Regression analysis to predict marketing variables (e.g. churn/buy probability), marketing mix
- Examples carried out in class: Cluster analysis to segment customers and the social audience

Recommended Bibliography

The lecture slides, which will be made available on the University's e-learning portal, and the resources (articles, tutorials or parts of books) which will be indicated by the teacher during the course, constitute study material.

Methods of Provision

Conventional

Teaching Methods

The course includes 54 hours of lessons.
The course will be organized in modules, each of which consists of theoretical lessons, demonstrations and guided exercises and practical group projects.

The course includes a group project in which students will apply the tools introduced in the course.

Evaluation methods

Verification of learning:

The final evaluation will be expressed out of thirty. The exam will consist in the evaluation of the group project and in a test.

For attending students:
The evaluation of the group project will constitute 40% of the final grade and will consider:
1) Discussion of the project
2) The project documentation report produced
The evaluation of the test will constitute 60% of the final grade. The test will be divided into quizzes and/or open questions and practical tests relating to the software tools demonstrated in class.

For non-attending students:
The project carried out during the course may be replaced, in agreement with the teacher, by a different project concerning the course content, or by an oral exam on the entire course programme.
The test, on the other hand, must be taken in the same manner adopted for attending students.

Other courses

Course year: 1

DIGITAL CONSUMER BEHAVIOUR
6 Credits - Characterising
DIGITAL INNOVATION
9 Credits - Characterising
DIGITAL MARKETING PRINCIPLES
9 Credits - Characterising
ECONOMICS OF STRATEGIC COMPETITION
9 Credits - Characterising
MARKETING STATISTICS AND METRICS
9 Credits - Characterising
APPLIED DATA ANALYTICS
9 Credits - Related/Supplementary
DIGITAL MARKETING AND USER EXPERIENCE
6 Credits - Elective Course
INTERNATIONALIZATION STRATEGIES AND DIGITAL INNOVATION
6 Credits - Elective Course
MACHINE LEARNING AND DATA SCIENCE
6 Credits - Elective Course

Course year: 2

BUSINESS AND SOCIAL MEDIA STRATEGIES
9 Credits - Characterising
DIGITAL LAW
6 Credits - Characterising
ECONOMICS OF DIGITAL GOODS AND ON-LINE MARKETS
6 Credits - Characterising
PERFORMANCE MANAGEMENT
6 Credits - Characterising
ADVANCED ENGLISH
6 Credits - Related/Supplementary
DIGITAL MARKETING AND USER EXPERIENCE
6 Credits - Elective Course
INTERNATIONALIZATION STRATEGIES AND DIGITAL INNOVATION
6 Credits - Elective Course
MACHINE LEARNING AND DATA SCIENCE
6 Credits - Elective Course
STRATEGIC MANAGEMENT
6 Credits - Elective Course
FINAL EXAM
18 Credits - Language/Final Examination
INTERNSHIP
6 Credits

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APPLIED DATA ANALYTICS