Big Data and Data Science Fundamentals

In this course, students will understand the pillars of Big Data, including volume, velocity, variety, veracity, and value, as well as structured, semi-structured, and unstructured data types. Storage architectures such as Data Lakes and Data Warehouses are covered, along with distributed and in-memory processing technologies like Hadoop and Spark, complemented by Cloud Computing for scalability and efficiency.

The module also explores the role of Data Science, detailing the analytical workflow from defining objectives, collecting, storing, and cleaning data through visualization, highlighting the use of Machine Learning (supervised, unsupervised, reinforcement, and Deep Learning) to extract patterns and generate strategic forecasts. Communicating results is emphasized through Data Storytelling, interactive dashboards, and clear charts, making the analysis accessible and actionable for decision-making.

Finally, students will study practical applications at companies like Amazon, Netflix, and Uber, as well as sectors such as healthcare, finance, and agriculture, covering ethics, privacy, combating algorithmic bias, and compliance with LGPD and GDPR. The module reinforces that data only becomes real value when analyzed, interpreted, and communicated responsibly, with attention to innovation and emerging trends like Generative AI and AutoML.