Fundamental tools and methods of data analysis and statistics. Visual representations of data. Descriptive statistical measures (Tables and graphs, measures of central tendency, position and dispersion, sampling methods, correlation and interaction between explanatory variables, multicollinearity and heteroscedasticity). Probability distributions and data modeling (Definitions and basic principles, conditional probabilities, independence, mathematical expectation, variance and confidence interval, distributions (e.g., normal-binomial, Gaussian, geometric, hypergeometric, poisson, gamma, etc.), assumptions, postulates and distributions). Sampling and parameter estimation (Central limit theorem, estimation of a mean, estimation of a proportion, hypothesis tests (T, Z, Anova, etc.), use of test VS control groups, statistical significance). Statistical inference (Probabilistic and deterministic models, interpretation of coefficients, residual analysis). Applications to artificial intelligence and business decision making.

Types of variables (Categorical, continuous, ordinal, discrete). Type of data (Structured, unstructured). Data preparation. Variable selection methods. Analysis methods (Descriptive, predictive, prescriptive). Model validation methods (Cross validation, by data set (training, test, validation)) Principles of heuristics. Trend analysis. Regression analysis. Classification and categorization analysis. Forecasting techniques. Data mining techniques (Exploration of available data, profiling and descriptive statistics of data, analytical design and model development, virtuous circle of data mining - CRISP-DM methodology). Model building and analysis. Algorithm design. Simulation and scenario analysis. Results and risk analysis. Linear optimization models. Dynamic optimization models. Longitudinal and time series models.

Introduction to expert systems. Constituents of expert systems (Knowledge, inference engines). Classification of expert systems (Order 0, Order 0.5, Order 1). Knowledge acquisition (Application domains, platforms, development languages). Knowledge representation (Constituents, knowledge representation, features, fact base (control strategies), rule base (semantic networks, conceptual and logical graphs)). Rule-based systems (Variables, conjunctions, disjunctions). Control strategies. Inference engines (forward chaining, backward chaining, mixed chaining). Interface modules. Role of social networks and the Internet of Things in data science. Information system supporting business decision making (Business analysis, data science, artificial intelligence, decision support systems). Machine learning. Megadata (Big Data). Expert systems (Dendralb Mycin, Prospector). Uncertain reasoning (Bayes' theory, inference engine, certainty factors, fuzzy sets). Case studies.

Installation and configuration of the R work environment. Distributed version control systems (Git version control system). Open source language (Vectors, matrices, factors, lists, data frames, arrays, implicit apply loops). R programming language (data import, data cleaning and transformation, data filtering, data sorting, data grouping, data aggregation, data selection, dataset analysis, use of function libraries, data visualization, data export). Designing and writing queries to perform statistical analysis (Introduction to RMarkdown, iIntroduction to Shiny R). Design of machine learning models (Supervised learning, unsupervised learning, simulation and bootstrapping, package creation).

Benefits of effective data visualization. The importance of data visualization for business decision making. Good visualization practices. "Data story telling. Data visualization software (TABLEAU). Workbook (Data sources, spreadsheet). Importing data. Transformation of source data. Visual data exploration and data processing. Connecting to data and presenting connector options. Merging data. Join condition. Introduction to the different types of visualizations (Good practices regarding the choice of visualizations according to the objectives pursued, choosing your visualizations, golden rules for graphics and generating conclusions, diagrams, tables, graphs and maps, infographics, dashboards). Data processing, filters, sorting and organization of data. Creation of calculations and animated charts. Sharing dashboards. Applications to business decision making.

Theory of dashboards. Security in a visualization context (permissions management, data protection). Interactive data visualization tools (Power BI). Installation and configuration of Power BI. Data import (Data collection, connectors, connection options). Data transformations (Data processing, dimensional organization). Data modelling. Visualization in Power BI. Data analysis (Creation of calculated columns and measures). Consulting and sharing data. Use of the R language within Power BI. DAX language. M language (How to choose when to use DAX language or M language). Power Automate language. Creation, configuration, natural language questioning and publishing of reports. Applications to business decision making.

Introduction of the interpreted programming environment (Python) (Installation and configuration of Python, Notebook concepts, available platforms (Jupyter, Spider), Anaconda). The interpreter and its environment. Syntax. Control statements (If, else, for, break, continue, pass, etc.). Functions and loops. Data structure. Modules. Inputs and outputs. Errors and exceptions. Classes. Libraries, Dictionaries and Tuples. Operators and indexes. Object types and data formatting. Lists. Importing external files and data. Libraries and basic packages (Pandas, NumPy). Modules and code organization.

Advanced notions of interpreted programming (Python) (Generators, context manager, accessors and descriptors, list comprehensions and regular expressions, iterators, etc.). Artificial Intelligence (Advanced modelling, recommender systems, natural language processing, mathematical and machine learning libraries (Numpy, SciPy, Pytorch, etc.), machine learning, cognitive computing). Megadata (Big Data: Hadoop®, Spark™, NoSQL and IoT, processing and unstructured data). Data mining and visualization with Matplotlib. Applications to business decision making. Integration of artificial intelligence in the workplace.

Client-side programming. Dynamizing a website with scripting language (JavaScript). Tools. Libraries. Platforms. Application logic programming. Information architecture. Management of interactions between the web interface and the user. Animation and manipulation techniques of web page elements. Design of rich graphic interfaces. Creation of an HTML web page. CSS style sheets. Creation of asynchronous requests. Preparation of the environment and the database. Transactional web applications (client and server side). Creation of requests (Clauses, operators, commands, parameters). Data manipulation. Development of supervision and monitoring applications. Development of multiplatform applications. Deployment of applications. Quality control of the application. Writing of documentation. Programming of data acquisition, processing and transmission instructions. Programming of the interactions between the interface and the user. Big Data: IoT. Development of applications applied to the field of artificial intelligence.

Creation of distributed and scalable applications (Hadoop). Working environment. Tools and utilities. Data preparation. Workflow and data management. Data collection. Machine learning. Clustering. Anomaly detection. IT project management (writing user stories, concurrent IT project management approaches (Waterfall, RACE, Scrum, JIRA, Sprint, etc.), introduction to agile method concepts). Development of applications applied to the field of artificial intelligence.

Linear regression (Simple linear regression, multiple linear regression, building a linear regression model, publishing the model). Logistic regression (Logistic regression, building a model, publishing the model). Data analysis and machine learning (Data analysis, data science project life cycle, purpose of machine learning). Learning algorithms (Supervised, unsupervised, reinforcement, deep). Machine learning tasks (Segmentation and clustering, Regression (price prediction, forecasting), regression tree and classification, text mining (sentiment analysis, categorization and text mining techniques) Introduction to different types of models (Basic data preparation, variable and model selection, model publication, model performance measures, data control, life cycle, updating and maintenance). Basic models (Trees, randomized drills, survival analyses, longitudinal and temporal analyses).

Machine learning solution (Data collection, data tuning, solution creation, testing, updating and maintenance of model performance, performance criteria and metrics (Area under the curve, correct classification rate, VIF, etc.). Open source and cross-platform machine learning infrastructure (ML.NET) (Core components, features). Traffic analysis for accident prediction. Artificial neural networks (Principles, utility). Types of neural networks (Supervised learning networks (classification, prediction, regression), unsupervised learning networks, feedforward neural network, recurrent neural network, convolutional neural network, generative adversarial networks). Layers (input, output, hidden). Transfer learning. Named entity recognition. Object detection. Machine translation. Text analysis (Natural language processing, recommendation system). Open source infrastructure (Azure Machine Learning) (Automated machine learning, mode of operation (pipeline), supervised learning task (classification, prediction, and regression), time series prediction (multivariate regression)). Data preparation (Data visualization, data transformation, commonly used modules, statistical functions). Model preparation for deployment (Evaluation of a model, creation and configuration of a Web service, consumption of a Web service). Model applications (Neural networks, natural language processing, recommender systems, others).

Analysis of the client's needs (mandate). Documentation of the needs. Modelling of the application that meets the identified needs. Choice of technology. Design of the application. Implementation of the application. Testing of the application. Deployment of the application. Presentation of the application (Report).

Practice and integration of personal and professional skills necessary to practice the profession. Application of knowledge and strategies learned in class in a business context. Integration into the professional environment. Collaboration with the work team. Participation in meetings. Taking charge of projects. Familiarization with operating tools. Adaptation to a corporate culture. Professional conduct in accordance with the ethics of the profession.