Formation/Cours

At the end of the course, the student should be able to:

1. Analyze a context, identify objectives, assess the situation, determine the analysis goals, and produce a project plan for an analysis that leads to actionable scientific or business outcomes.
2. Differentiate between the phases of the cross-industry standard process for data mining
3. Discuss each phase, and identify the crucial tasks of each phase
4. Identify, describe, and explain the mechanics behind major statistical approaches and machine learning algorithms
5. Apply major statistical approaches and machine learning algorithms within the CRISP-DM framework to a real data mining problem
6. Locate, implement, and use data manipulation, statistical, and machine learning software.
7. Carry a project through the phases/tasks of CRISP-DM and synthesize the process and analyses into a well-organized, informative report with actionable outcomes highlighted for the company or organization

Business is emphasizing the power of data analytics to support decision making. Artificial intelligence provides the tool set that can be applied to analyze large-scale business data. To be able to plan, organize, and analyze data to produce useful information to effectively support decision making is a valuable skill. In this course, you will learn basic principles and techniques of artificial intelligence. The course will include hands-on experience using statistics and algorithms for data analysis and decision making. Class exercises and a semester project will give you the opportunity to apply foundational skills in artificial intelligence to create a report that effectively and efficiently illustrates your analysis, conclusions, and recommendations to assist decision making.