Syllabus

Requirements for this course¶

Tip

Installation instructions are available in the first session materials.

Definitions: big data, smart data, open data
Understanding the data roles within the company: Chief Data Officer (CDO), Data Engineers, Data Scientists, Analysts, Statisticians, Data Translators, Data Protection Officer
Types of data: structured data and their applications, unstructured data and their applications
Richness of data and where to collect them: internal data, external data, open data, panel and tracking data
From data to action: machine learning, natural language processing, computer vision

Storing data: data warehouse vs. data lake
Data management platforms: relational, non-relational
Cleaning data: why clean data, how to clean data, missing data, outliers, redundant data, naming variables, coding variables
Practical exercise: cleaning, preparing and storing data, basic operations

Data mining techniques according to the company’s goal and type of data: prediction and classification algorithms, linear regression, logistic regression, decision trees, random forests, K-nearest neighbors
Practical exercises with predictive marketing use cases: price prediction, consumer segmentation, recommendation, personalization

Data visualization: how to communicate the message considering the target and the channel
Data storytelling: define and communicate your storyline
Practical exercises with predictive marketing use cases: price prediction, consumer segmentation, recommendation, personalization
Ethical issues with data and AI: bias, transparency, privacy