The postgraduate degree in Data Science with which you will boost your career
100% guaranteed internships in the best companies. Lead the most demanded profession in the market
Today's market requires professionals who know how to handle, analyze and interpret data to serve business objectives. Companies need these specialized profiles that combine analytics and strategy with the technical part, so that training in this discipline becomes a differential value for recent graduates.
At MIOTI we prepare you for this promising reality. With us you will learn from basic concepts of data pre-processing, Artificial Intelligence and Python programming, to the latest models of deep neural networks and image recognition. You will work with real datasets applying machine learning and solving business problems in class and in practice.
After our training and experience in the company, you will be prepared for any challenge in the workplace, you will not need an adaptation period.
What you are going to learn in the Master of Data Science & AnalyticsIntroductionIntroduction to MIOTI, introduction to the platforms to be used during the program and introduction to the course.Python for BeginnersIntroduction to programming and preparation for its application in Data Science.Data Science fundamentalsIntroduction to fundamental concepts of data science. Presentation of the general frame of reference.Data Science with PythonPython as a framework for the data science specialist. Notebook development, use of pandas, numpy, matplotlib. Data processing from structured sources (CSV, REST, SQL, Logs) and unstructured (Web, Spark, Cassandra).Statistics for DSReview of the fundamentals of statistics necessary to master data science.Data PreprocessingHow to properly preprocess the data? Filter application, data anonymization, attribute selection, sampling and dimensionality reduction.Data VisualizationTools for data visualization. Introduction to the most used techniques and libraries.Predictive AnalyticsIntroduction to time series analysis, review of the best available algorithms. Development of use cases for anomaly detection and series prediction.Machine LearningIntroduction to classification and clustering problems. Construction of data sets and evaluation of results.Machine Learning IIReview of the main supervised learning algorithms bayes, support vectors, regressions, and unsupervised and their application.EntrepreneurshipUnderstanding of the new business models based on data science that are emerging in the business and industrial sector, and the techniques to implement ideas based on this technology.Deep learningIntroduction of fundamental concepts of deep neural networks. Theoretical and practical tour, learn to use the most important tools and implement solutions from scratch (antagonists) for data management.Computer VisionIntroduction to fundamental concepts of Computer Vision techniques. Theoretical and practical tour of the main techniques.Natural Language PreprocessingIntroduction to fundamental concepts of the mechanisms used for communication between people and machines through natural language. Know the interactions and their application in the field of artificial intelligence.Entrepreneurship IITo delve into the new business models based on data science that are emerging in the business and industrial sector, and the techniques to implement ideas based on this technology.Kaggle ChallengeYou will choose and develop a challenge to measure yourself with the best professionals in the world and thus assess what has been learned during the master.Machine Learning IIIApplication of convolutional networks and deep recurrent models such as TensorFlow in practical applications with images. Implementation and design of neural models for problem solving modeling / classification and design of GAN (antagonistic generative models) for data management.Reinforcement LearningIntroduction to reinforcement learning concepts. Know the ways to calculate moving averages and means, Markov decision processes, dynamic programming, learning time difference and approximation methods.Big Data for DSFundamental concepts of Big Data solutions. Reference architectures and adoption models with the main current technologies including data ingestion, analysis and visualization processes in real time.New TechnologiesInitiation to Blockchain, Industry 4.0, Internet of Things and Robotics.DS for BusinessPractical applications of AI for business, Algorithm Driven Companies, Skills Transformations, Data Driven Companies.Soft SkillsProfessional experts will give a master class on how to present projects and speaking and negotiation skills.Project ManagementKnow the development and implementation phases of projects, identify those elements to take into account to facilitate execution, minimizing the foreseeable incidents found in this type of project.Final ProjectDevelopment of a final project to consolidate the knowledge acquired during the program.
