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Develop strategies for communicating ethical challenges in data-driven technologies, including stakeholder engagement, crisis management, and media relations to foster trust and mitigate risks.
Explore ethical considerations in data-driven technologies, focusing on bias, privacy, and ethical principles. Learn to advocate for responsible use of emerging tech in AI, machine learning, and data science.
Learn strategies to prepare for, pass, and leverage certification exams, including study tips, scheduling procedures, and post-certification steps to maximize career benefits.
Analyze ethical frameworks, regulations, and standards to integrate them into data-driven solutions, reconciling ethical duties with business practicalities in emerging technologies.
Aprende conceptos básicos de machine learning, prueba hipótesis, y entrena, ajusta y evalúa modelos para clasificación, regresión y clustering. Requiere conocimientos en tecnologÃa informática y programación.
Learn to present data science project results to stakeholders, build a web app for model demonstration, and automate model training and deployment pipelines. Ideal for experienced professionals in the CDSP program.
Learn strategies to identify and mitigate ethical risks in data-driven technologies, covering privacy, accountability, transparency, fairness, and safety concerns in AI development and deployment.
Develop strategies to lead an applied ethics initiative, champion its importance, and promote an ethical culture in data-driven organizations. Learn to implement policies and evaluate their effectiveness.
Explore AI and ML applications in business, learn about tools and resources, and address ethical considerations in implementing these technologies for actionable insights and innovation.
Learn to collect, prepare, and load data from multiple sources for analysis and modeling in this hands-on ETL course for data professionals.
Learn to initiate data science projects, formulate problems, and apply solutions in business contexts. Develop skills to determine project suitability and implement the data science process effectively.
Gain insights through data analysis techniques, statistical methods, and visualizations. Learn to preprocess data for machine learning applications.
Learn to build regression, classification, and clustering models using various algorithms. Gain hands-on experience in model selection, evaluation, and tuning for supervised and unsupervised learning tasks.
Explore the machine learning workflow from problem formulation to model deployment, including data analysis, model training, and automation techniques for recurring business processes.
Learn to build decision trees, SVMs, and neural networks for machine learning and deep learning. Gain practical skills in constructing models to solve regression, classification, computer vision, and natural language processing problems.
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