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A person who has received instruction considered sufficient by the college and meets any of the following conditions may be admitted to a program of studies leading to an Attestation of College Studies:
Ref.: art. 4 College Education Regulations
CDI College's Artificial Intelligence Specialist - LEA.E3 program is designed to train qualified professionals capable of assuming, at the entry level of the job market, all the tasks in connection with data analysis in a business intelligence context and the development of computer applications in an organization. The analyst must essentially navigate between computer and business languages. The business intelligence analyst, using his knowledge of technologies, analyzes data exhaustively, thus enabling him to match the information to the organization's needs. The analyst collects data and identifies performance indicators in order to improve the quality of processes in each sector. He advises users who base their actions and strategic decisions on the information generated.
Graduates can work in small, medium and large companies, government agencies (federal, provincial and municipal), professional offices and educational institutions. In addition to having a good command of the various programming languages, the student will be able to communicate clearly using the appropriate terminology. They will have developed their ability to observe, analyze and synthesize, as well as a sense of professional ethics. The latter is particularly important because of the need to ensure the protection of information, data security and the ethical use of artificial intelligence technologies. Finally, he will be able to demonstrate rigor in the execution of the tasks entrusted to him and will have developed skills in a teamwork context.
To graduate, students must obtain the required pass mark in each course.
"The best part about learning from Practitioner Instructors is that they have worked in the job that you’re going into so they know the “ins and outs” and the little tricks of the trade, so to speak. They know what they’re talking about and have firsthand experience."
Background. Work functions of the artificial intelligence specialist. Practice of the profession in different work environments. Role of the artificial intelligence specialist and those of related professions. Artificial intelligence (Trends, Usefulness, Risks, Issues and Challenges). Professional ethics at work. Major hardware and software components of a computer. Windows™ desktop and file system. Basic concepts of word processing software (Creating Microsoft Word™ documents, using basic layout elements, tables, graphics, spell checking). Basic concepts of a database management software (Application of key concepts of a database management system, use of Microsoft Access™ to create a database, creation of queries, forms, reports and statements, tables, relationships, normalization, primary and secondary keys, use of interfaces for managing data inputs and outputs, design, modeling and normalization of relational, object-oriented and distributed databases, design of user-friendly graphical user interfaces). Use of the Internet for communication and research. Work methods and ergonomics.
Identification of input and output data. Determination of relevant entities and their attributes. Sketch of the design. Identification of concepts related to data, operators and functions. Priority of mathematical operators. Distinction of basic data types, variables and constants. Evaluation of expressions using operators. Algorithm development. Creation of algorithms for the use of tables. Representation of logic using pseudocodes and flow charts. Translation of algorithms into a programming language. Logic (decision and loops, etc.). Use of code-debugging tools with development tools to generate web applications (Microsoft Visual Studio).
Possibilities of an object-oriented programming language. Adaptation of algorithms and pseudocodes to an object-oriented programming language. Language, syntax and semantics of graphic modeling based on pictograms. Declaration and use of variables, parameters and constants. Use of operators and expressions (inheritance and polymorphism). Coding of different control structures. Use of a code library to produce management applications with a rich graphical interface. Declaration and use of complex variables (arrays, enums and structures). Writing functions. Writing error handlers. Compilation and debugging tools for the development environment (locating compilation errors, correcting compilation errors). Software architecture. Validation of results. Correction of algorithms and or pseudocode. Application of test cases. Analyze the results of the test cases. Validation of the program operation. Documentation.
Creation, modification and exploitation of a relational database or of another nature (SQL). Theory of the relational model. SQL and NoSQL approach. Queries and subqueries and advanced SQL. Good coding practices (comments, checkpoints, script documentation, etc.) Data replication. Data management (triggers, stored procedures, user-defined functions) Optimization of data access through indexes. Optimization of data access through joins. Designing a security plan for a database. Interpretation and design of conceptual, logical and physical data models. Basic database administration operations. Scripts and batches. Code blocks and control structures. Structure nesting. Repeat structures. Entities, attributes, cardinality and relationships. Database practices (normalization, denormalization, star schema VS snowflake, data warehouse VS data lake, etc.) Software architecture content elements (Tables, views, facts VS dimensions, etc.). ETL and ELT (Extract - Transform - Load) principles. Creation of forms and addition of interface elements (Buttons, choice list fields, text fields, numeric fields). Design of the various sections of a report. Modify the layout of a report. Advanced formatting. Reproduce formatting and automatic formatting. Creating subforms. Formatting of controls. Production of user documentation specific to the developed application (Glossary, data dictionary, guide, comments in the code, etc.).
Installation, administration and support of a Linux operating system. Available documentation. Tree structure, directories and file locations. Manipulation and editing of files. Administration of user accounts, groups and access permissions. Introduction to Bash script development. Tests, conditions and main components of a script. Log management, loops and conditional functions. Shell environment and physical environment. Installation, management and compilation of applications. Linux system configuration with data backup and archiving. Use of Linux system tools.
Introduction to dynamic web content. Setting up development servers. Object-oriented imperative language (PHP). Expressions and flow control in PHP. Functions and objects in PHP. Tables in PHP. Relational database management system (MySQL). Access MySQL using PHP. Forms Management. Cookies, sessions and authentication.
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.
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.
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