Executive Development Programme in Data Science Implementation
-- ViewingNowThe Executive Development Programme in Data Science Implementation is a certificate course designed to empower professionals with the necessary skills to implement data science in their organizations. This program bridges the gap between traditional management practices and modern data science techniques, making it highly relevant in today's technology-driven world.
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โข Fundamentals of Data Science: Introduction to key concepts of data science, including data mining, machine learning, and big data analytics. Understanding of data types, data management, and data pre-processing techniques.
โข Data Analysis Tools and Techniques: Hands-on training in using popular data analysis tools such as Python, R, and SQL. Emphasis on data cleaning, data visualization, and statistical analysis.
โข Data Management and Infrastructure: Exploration of data storage and processing options, including traditional databases, data warehouses, and cloud-based solutions. Overview of data governance, data security, and data privacy.
โข Machine Learning and Predictive Analytics: Deep dive into machine learning algorithms and techniques, including supervised and unsupervised learning, neural networks, and deep learning. Practical applications of predictive analytics and modeling.
โข Data Visualization and Communication: Techniques for presenting data in a clear and effective manner, including data storytelling, data journalism, and visualization tools such as Tableau, Power BI, and ggplot.
โข Business Intelligence and Decision Making: Overview of business intelligence (BI) concepts, including data warehousing, online analytical processing (OLAP), and reporting. Emphasis on using data to inform business decisions and strategy.
โข Data Ethics and Responsibility: Exploration of ethical considerations in data science, including data privacy, data bias, and fairness. Overview of legal and regulatory requirements related to data use.
โข Leadership and Change Management: Strategies for leading and managing data science projects, including stakeholder management, project management, and change management. Emphasis on building a data-driven culture within an organization.
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