Statistics and Data Analysis:

• Perform exploratory analysis on data sets;
• Interpret data distribution and conduct hypothesis tests;
• Create graphs and data visualizations appropriate to each situation.

Programming and Projects:

• Python programming language;
• Python libraries focused on Data Science and Machine Learning, such as numpy, scipy, notebooks (jupyter and Kaggle), scikit-learn, Keras, nltk, gensim, spacy among others;
• How to use tools to process data, such as filtering, reordering, searching, cleaning invalid data, among others;
• Creating scripts to train and test machine learning models.

Big Data:

• How to use Non-relational (NoSQL) databases based on documents or graphs;
• Fundamentals and configuration of the Hadoop ecosystem;
• How to collect open data on websites and social networks like Facebook and Twitter.

Machine Learning / Artificial Intelligence: 

• Practical problems in supervised (classification and regression) and unsupervised (clustering) learning. How to conduct experiments appropriately, interpret results, solve problems or optimize models;
• The student will study the most traditional algorithms: Naive Bayes, Linear Regression, Logistic Regression, K-Neighbors, K-Means, Neural Networks and SVM;
• Deep learning techniques: deep neural networks, backpropagation, gradient descent, recurrent networks and LSTM networks, convolutional networks, classification and regression problems. Problems related to computer vision and natural language processing;
• Techniques focused on natural language processing and text mining that can be applied to sentiment analysis, information extraction in unstructured text, search and recommendation systems for similar texts, chatbot development, automatic text generation, among others.