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Unidades Curriculares
  1. Semestre Primavera

    Data Science and Machine Learning
    In this course unit, students will be exposed to the key concepts in data acquisition, preparation, exploration, and visualization. Also, machine learning theory combined with practical scenarios and hands-on experience on machine learning models will be covered. Practical application-oriented examples will be presented on how to build and derive insights from these models using Python.

    Managing Relational and Non-Relational Data
    This course unit’s goal is to present relational and non-relational databases to students. It starts by providing a good understanding of the Transact-SQL language and SQL Server. In the second part of the course unit, students will explore the basics of NoSQL and the storage options in cloud platform-based NoSQL DBs.

    Programming for Data Science
    This course unit introduces Python programming, from its basic syntax to data analysis applications. Students will be presented to variables, data structures, matrices, data frames and lists, IO operations, to local files and cloud-based databases. In the final, simple analytics and data visualization using Python libraries will be presented.

    Statistics for Enterprise Data Analysis
    This course unit aims to provide students the ability to, when presented with a business problem, uncover the associated research question, select the appropriate research process and the appropriate statistical model, methodology to resolve the problem and present the results and conclusions. This course unit’s goal is to provide students the ability to:

    1. Identify the most common pitfalls in the statistically-based results reported in popular media, deciding on the reasonability of the conclusions presented.
    2. Select the most appropriate descriptive methods for the data in hand, compute and plot them, synthesizing the relevant conclusions.
    3. Recognize and explain the central role of variability in the field of statistics.
    4. Recognize and explain the central role of randomness in designing studies and drawing conclusions.
    5. Select the most appropriate model to address a specific research question.
    6. Formulate the problem in R language, perform the necessary operations in R, examine the results and evaluate, using the appropriate measurements, the quality and adequacy of the model.
  2. Semestre Outono

    Analysing Big Data
    The Big Data Analytics course is designed for data scientists that want to start working with big data technologies. The course is focused on 3 main objectives to give students the capability to start their own Big Data Analytics projects:

    1. Give a clear understanding of the differences between the Big Data projects and technologies, to the traditional relational approaches;
    2. Give a solid and practical understanding of what Spark is, with practical data integration use cases (both structured and unstructured) to prepare data for Data Science processes;
    3. Explore the SparkML library for Data Science use cases.

    Analyzing and Visualizing Data
    This course unit’s goal is to provide students the ability to:

    1. Gather and transform data from multiple sources;
    2. Discover and combine data;
    3. Explore, analyze, and visualize data;
    4. Transform data into insights;
    5. Create and share dashboards;
    6. Use natural language queries;
    7. Create real-time dashboards.

    Big Data Foundations
    The main goal of the course unit is to introduce students to the batch, near real time and real times Big Data stacks, allowing them to build processing solutions to gather, cleanse, reshape and store data for analysis. The course unit will cover technologies within the core Hadoop ecosystem but also modern streaming solutions, based on managed platforms. The course has a practical, hands-on approach and students will learn to implement low-latency real world solutions.

    Deep Learning Neural Networks
    This course unit introduces deep learning. Students taking this course will learn the theories, models, algorithms, implementation and recent progress of deep learning, and obtain empirical experience on training deep neural networks. Specifically, the course will cover basic concepts in optimization, neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN). By the end of the course, it is expected that students will have a strong familiarity with the subject and be able to design and develop deep learning models for a variety of tasks.

    Enterprise Data Science Bootcamp
    The main goal of the Enterprise Data Science Bootcamp is to give the students an intensive team challenge that will give them practical, hands-on experience working directly with enterprise scenarios. Enterprise partners will provide the teams with challenges based on real world scenarios. The teams should apply analytics and Big Data competencies covered in the program to uncover actionable insights and drive innovation.