Course Information

Course Description

The Diploma in Data Science with a focus on Data Mining, Machine Learning, and Artificial Intelligence aims to equip learners with the necessary knowledge, skills, and understanding of key concepts and techniques in these fields. The course is designed to provide a comprehensive understanding of data-driven methodologies and their applications in various domains. Students will delve into topics such as data collection and preprocessing, predictive modeling, pattern recognition, and intelligent decision-making systems.

COURSE OUTLINE

  • Introduction to Data Science
  • Fundamental Principles of Data Mining
  • Machine Learning Algorithms and Techniques
  • Artificial Intelligence Fundamentals
  • Data Preprocessing and Feature Engineering
  • Predictive Modeling and Pattern Recognition
  • Intelligent Decision-Making Systems

 

STUDENT ACQUISITIONS

Upon completing the course, students will:

  • Understand the fundamental concepts and principles of data mining, machine learning, and artificial intelligence.
  • Develop proficiency in applying various data mining algorithms and machine learning techniques to extract insights from large datasets.
  • Evaluate the performance of predictive models and understand their practical implications in real-world scenarios.
  • Gain awareness of ethical considerations and biases in data science applications, particularly in machine learning and artificial intelligence.
  • Apply data preprocessing techniques and feature engineering methods to improve the quality of data for analysis.
  • Design and implement intelligent decision-making systems using machine learning and artificial intelligence frameworks.
  • Stay updated with the latest trends and advancements in data science, machine learning, and artificial intelligence domains.

 

LEARNING METHODOLOGIES

The course employs a variety of learning methodologies, including:

  • Engaging with theoretical concepts to build a solid foundation in data science, machine learning, and artificial intelligence.
  • Encouraging hands-on practice through lab sessions and projects to reinforce learning and develop practical skills.
  • Facilitating critical analysis and interpretation of data science research papers, case studies, and industry reports.
  • Promoting collaborative learning through group discussions, workshops, and peer reviews.
  • Providing mentorship and guidance to students for effective problem-solving and project implementation.
  • Enhancing communication skills for presenting complex technical concepts and findings in a clear and concise manner.