What skills will be developed in the course?

The Complex Data Mining Extension Course (MDC) aims to train professionals for the current job market, with an emphasis on: (1) improving data management with speed, capacity and scalability in mind; (2) develop techniques for visualizing these data; (3) finding new business opportunities; (4) improving the data analysis capacity; and (5) create predictive models using the most modern machine learning methods.

INF-0610

Extension course

Email: mdc@ic.unicamp.br

Phone Number: (19) 3521-5883

Presented by:

Overview

Format

The Extension Course in Complex Data Mining (MDC) is composed of 9 subjects that teach the main concepts required by the job market, making a total workload of 180 hours, with 144 hours of classes and 36 hours of supervised activities (mentoring and services), completely online (via Zoom).

Digital Certificate

Students who pass the 9 subjects will be entitled to the certificate of the Extension Course in Complex Data Mining (MDC), issued by the Unicamp Extension School (see the Certificate Template).

Faculty

The teaching staff of the Extension Course in Complex Data Mining (MDC) is made up of professors and researchers, all with doctorates, with extensive experience in the area.



  • Disciplines

  • DATA ANALYSIS (INF-0612 - Messing with Data)
    Teacher: Zanoni Dias
    Introduction to Data Analysis using the R Language. Data types (vectors, lists, matrices, data frames, etc.). Predefined functions. Implementation of functions in R. Treatment, analysis and visualization of data.
    Classes: 27/01/2024, 03/02/2024, 10/02/2024 and 17/02/2024, from 08:30 am to 12:30 pm.

    INFORMATION RECOVERY (INF-0611 - Gathering Data)
    Teacher: Lin Tzy Li
    Introduction to information retrieval. Ranking evaluation techniques. Unstructured data recovery concepts. Text recovery. Image recovery by content. Video recovery. Techniques for improving ranking quality.
    Classes: 27/01/2024, 03/02/2024, 10/02/2024 and 17/02/2024, from 13:30 pm to 17:30 pm.

    MACHINE LEARNING NOT SUPERVISED (INF-0613 - Exploring Data)
    Teacher: Hélio Pedrini
    Discovery of knowledge. Understanding and prospecting for information. Exploratory data analysis. Anomaly detection. Association rules. Dimensionality reduction. Attribute selection. Grouping techniques.
    Classes: 24/02/2024, 02/03/2024, 09/03/2024 and 16/03/2024, from 13:30 pm to 17:30 pm.

    SUPERVISED MACHINE LEARNING I (INF-0615 - Learning from Data)
    Teacher: Anderson de Rezende Rocha
    Classification problems. Decision boundaries. Linear and non-linear classifiers, logistic regression, decision trees and random forests. Overfitting and validation. Ensemble methods: bagging, boosting and stacking. Cross validation. Imbalance, diagnosis of bias and variance. Evaluation measures. Interpretation of models (X-AI) and classification in open scenario (open-set).
    Classes: 24/02/2024, 02/03/2024, 09/03/2024 and 16/03/2024, from 08:30 pm to 12:30 pm.

    VIEWING INFORMATION (INF-0614 - Viewing data)
    Teacher: Celmar Guimarães da Silva
    Theoretical and practical aspects of Information Visualization (InfoVis). Representation of data in a graphic and interactive way. InfoVis reference model. Characterization of data. Recommendations for visual mapping. Visualization of multidimensional data. Visualization of texts.
    Classes: 23/03/2024, 06/04/2024, 13/04/2024 and 20/04/2024, from 08:30 am to 12:30 pm.

    SUPERVISED MACHINE LEARNING II (INF-0616 - Thinking with Data I)
    Teacher: Esther Luna Colombini
    Vector Support Machines (SVMs): kernels (linear and non-linear), SVRs and one-class SVM. Regularization techniques. Grid-search and random-search. Neural networks: types of networks, forward and backward propagation, and activation functions. Statistical tests.
    Classes: 23/03/2024, 06/04/2024, 13/04/2024 and 20/04/2024, from 13:30 am to 17:30 pm.

    BIG DATA (INF-0617 - Big Data)
    Teacher: Lucas Francisco Wanner
    Introduction to parallel and distributed computing. Parallel data processing in Python. Distributed data processing with Map-Reduce and Hadoop Streaming. Introduction to tools for analyzing and processing data with Hadoop and Spark.
    Classes: 27/04/2024, 04/05/2024, 11/05/2024 and 18/05/2024, from 08:30 am to 12:30 pm.

    DEEP LEARNING (INF-0618 - Thinking with Data II)
    Teacher: Sandra Eliza Fontes de Avila
    Deep learning and convolutional neural networks (CNN). Convolution: padding and stride. Loss functions. Training: activation, pre-processing, data augmentation, weight initialization and parameter optimization functions. Regularization. Learning transfer. Recurrent Neural Networks (RNN). Transformers. Detection and Segmentation. Generative Adversarial Networks (GAN). Interpretability (X-AI). Tools: TensorFlow and Keras.
    Classes: 27/04/2024, 04/05/2024, 11/05/2024 and 18/05/2024, from 13:30 am to 17:30 pm.

    FINAL PROJECT (INF-0619 - Data @ Work)
    Teacher: Zanoni Dias
    Definition of target problem. Data identification and collection. Analysis of the techniques to be employed. Comparative study. Analysis, visualization and presentation of results.
    Classes: 08/06/2024, 15/06/2024, 22/06/2024 and 29/06/2024, from 08:30 am to 12:30 pm.




  • 100% Online Course

  • Our course is not a traditional distance learning (EAD) course (with recorded or pre-recorded classes). All classes will be held and broadcast live (via Zoom), with the participation of students in real time, on the days and times indicated above. Assessments will be carried out through practical assignments. Course material (slides, tutorials, codes, etc.) will be made available to students (via Moodle). Questions will be answered from Monday to Friday, with teachers and monitors, synchronously (via Zoom) and asynchronous (via Slack).



  • Registration

  • Registration closed for the class of the first semester of 2024.

    The following documents are required for registration:

    Registration Form and Term of Commitment digitally signed (documents generated by the Online Pre-Registration)
    Diploma or Certificate of Completion of Undergraduate Course
    RG and CPF
    Résumé
    Cover letter (optional, free format, one page, attach to CV, to be sent through the system)

    The documents listed above must be presented on both sides, whenever there is any information recorded on the back of the document.

    Important:

    Documents must be received by the Extecamp system by 15/12/2023 (Friday).
    In case of doubts about the registration documentation, consult the Extension Secretariat (itext@unicamp.br).
    Late registration will not be accepted.



  • Investment

  • The total cost of the course (R$8.999,95) can be paid in 5 interest-free installments.

    Special discounts (cumulative):

    R$2.000,00 discount for cash payment.
    R$1.000,00 discount for payment in 3 installments without interest.
    R$1.000,00 discount for former Unicamp students.
    R$1.000,00 discount for registrations made between 01/11/2023 and 30/11/2023. [promotion closed]
    R$2.000,00 discount for registrations made until 31/10/2023. [promotion ended]

    Remarks:

    The discounts mentioned above will be applied manually when issuing bank slips, after the selection process (the system may display amounts without discounts at the time of registration).
    The payment of the first monthly installment or the single installment, depending on the payment method chosen, must be made by 10/01/2024.
    To qualify for the early registration discount, all documents must be delivered by the dates indicated.
    To qualify for the discount for Unicamp alumni, the candidate must present, at the time of registration, a diploma or a certificate of completion of an undergraduate or graduate course (master's or doctorate) issued by Unicamp.
    As the discounts are cumulative, it is possible to obtain up to R$5.000,00 of discount (considering the discounts listed above, applying the respective conditions).



  • Details


  • Prerequisite: Full upper level. Basic programming knowledge.
    Target Audience: Computer professionals, trained in Computing or related areas (Engineering or Exact).
    Selection criteria: Analysis of Curriculum and Cover Letter (optional).
    Course type: Extension course.
    Class schedules: Saturdays, from 8:30 am to 12:30 pm and from 13:30 pm to 17:30 pm.
    Required Material: As it is a course with a practical focus, all students must have a computer / notebook with internet access to follow the classes and proposed practical activities.
    Class size: A minimum of 20 and a maximum of 90 students.
    Course coordinator: Zanoni Days.




  • Calendar

  • Data Event
    02/10/2023 até 15/12/2023 Registration period
    15/12/2023 Deadline for submission of registration documents
    22/12/2023 Disclosure of candidates selected for registration
    10/01/2024 Maturity of the first or single installment
    27/01/2024 até 29/06/2024 Course offering period


    • TESTIMONIALS