Introduction to Data Science and Machine Learning
The course is an introductory course that covers a wide variety of topics at the intersection of data science and machine learning with a good balance between theory, numerical methods (coding), and applications. Designed for students and beginners seeking comprehension of the concepts, statistics, and mathematics underpinning data science and machine learning algorithms. The course starts from the basics, requiring no prerequisites for participants to begin their learning journey. The course covers basics related to data science, data visualization, regression methods, classification methods, unsupervised learning, and spectral clustering methods. You will gain familiarity with various data science and machine learning model concepts, including data journalism, data visualization and storytelling, linear and
non-linear regression, decision trees, random forests, neural networks, and clustering techniques.
Students who successfully complete this course will be able to:
• Apply quantitative modeling and data science analysis techniques to the solution of real-world problems.
• Communicate findings and effectively present results using data visualization techniques.
• Demonstrate knowledge of statistical data analysis techniques utilized in applied engineering problems.
• Apply principles of Data Science to the analysis of engineering problems.
• Employ cutting-edge programming languages and computational tools to analyze Big Data.
• Understand key concepts in the machine learning domain along with some in-depth understanding of classical machine learning algorithms
• Apply classic machine learning algorithms to build machine intelligence.
