Ciência de Dados Aplicada ao Marketing

Prior mastery of statistical concepts, as well as basic skills in information technology, is recommended.

The following teaching methodologies are used in this course unit:

1. Expository method: explanatory method where theoretical foundations and concepts are presented by the teacher and discussed with the class. Concepts and information will be presented to students through, for example, slide presentations or oral discussions. It will be used in classes to structure and outline the information.

2. Demonstrative method: based on the example given by the teacher of a technical or practical operation that one wishes to be learned. It focuses on how a given operation is carried out, highlighting the most appropriate techniques, tools and equipment. It will be used, for example, in practical and laboratory classes.

3. Interrogative method: process based on verbal interactions, under the direction of the teacher, adopting the format of questions and answers. It allows for greater dynamics in the classroom and consolidates learning. It will be used, for example, to remember elements of previous classes and in revisions of the lectured content.

4. Active methods: pedagogical techniques will be used in which the student is the center of the learning process, being an active participant and involved in his own training. The teacher assumes the role of facilitator, stimulating critical thinking, collaboration, creativity and student autonomy. They will be applied in classes to achieve a dynamic and more lasting learning environment.

At the end of the course unit, the student will be able to:

1. Introduction: Differentiate between Business Intelligence and Data Science concepts applied to Marketing. Describe the No-Code tools ecosystem for management. Explain ethical standards and the impact of GDPR on customer data processing.

2. OSEMN Methodology: Define the constituent phases of the OSEMN methodology. Design analytical workflows. Validate iteration cycles for results in a Marketing project.

3. Data Acquisition: Identify various Marketing data sources. Import data files into analysis tools. Categorize the different types of variables.

4. Data Cleaning: Evaluate the quality of the collected data. Handle missing values and duplicate records in databases. Detect behavioral anomalies and outliers within the sample. Execute variable recoding and normalization.

5. Data Exploration: Apply descriptive statistical measures to characterize the sample. Analyze measures of association and correlation between variables. Implement inferential statistical tests to validate hypotheses. Differentiate between the application of parametric and non-parametric tests.

6. Models: Distinguish between supervised and unsupervised learning models. Implement regression and classification algorithms. Develop clustering models for market segmentation.

7. Evaluation: Calculate performance metrics for regression and classification models. Interpret technical results in light of marketing objectives. Translate analytical findings into executive decision support strategies.

1. Introduction to fundamental Data Science concepts applied to Marketing. Business Intelligence vs. Data Science. No-Code tools ecosystem. Ethics, Privacy, and GDPR in customer data processing.

2. OSEMN Methodology (Obtain/Scrub/Explore/Model/iNterpret). Phases of the OSEMN methodology. Workflow design. Iteration cycle and results validation.

3. Data Acquisition (Obtain). Data sources. Data import and structuring. Variable types: numerical, nominal, and ordinal. Connecting to Open Data sources.

4. Data Cleaning (Scrub). Data quality. Handling missing values and duplicate records. Detection of outliers and behavioral anomalies. Variable transformation, normalization, and recoding.

5. Data Exploration (Explore). Exploratory Data Analysis (EDA). Descriptive Statistics. Measures of central tendency, dispersion, and distribution. Inferential Statistics. Independence Tests. Measures of Association. Correlation Measures. Parametric Tests: t-test, ANOVA. Non-Parametric Tests: Wilcoxon, Mann-Whitney, Kruskal-Wallis.

6. Data Science Modeling (Model). Supervised learning. Regression: linear, non-linear. Classification: KNN, Decision Trees, Random Forests, Logistic Regression, SVM. Unsupervised learning. Clustering: K-means, Hierarchical Clustering, DBSCAN. Dimensionality Reduction.

7. Model evaluation and results interpretation (iNterpret). Performance metrics. Regression: MAE, MSE, RMSE, RAE, RSE, R2. Classification: Confusion Matrix, Precision, Sensitivity (Recall), Specificity, F1-Score, AUC (ROC Curves). Technical results translation. Data storytelling for executive decision support.

Regarding the evaluation, the following methods are considered:

1. Periodic Evaluation

Final Grade = tests (10/20) + assignments (10/20)

a) each of the evaluation components has a minimum grade of 45%, without rounding, in order for the student to obtain approval in the course unit;

b) the completion of the assignments involves the delivery of reports regarding course unit topics through the academic management platform (Inforestudante), and their respective defense;

c) carrying out the different evaluations (e.g. tests, defense of assignments) depends on prior registration through the academic management platform

2. Exam Evaluation (Normal Period, Resit Period, Special Period)

Final Grade = exam (10/20) + assignments (10/20)

a) each of the evaluation components has a minimum grade of 45%, without rounding, in order for the student to obtain approval in the course unit;

b) the completion of the assignments involves the delivery of reports regarding course unit topics through the academic management platform (Inforestudante), and their respective defense;

c) carrying out the different evaluations (e.g. exam, defense of assignments) depends on prior registration through the academic management platform

NAO