About this Course

This course, as a part of the Data Science MicroMasters program, is designed to equip learners with the necessary skills to handle various types of data and perform predictive and descriptive analysis. Utilizing a blend of supervised and unsupervised learning algorithms, you will understand the underlying theory and practical implementations. Through real-world case studies, the course covers classification of images, topic identification in documents, personality profiling, and semantic structure capture in texts. The primary programming language for the course is Python, and all practical exercises are conducted in Jupyter notebooks.

Learning Outcomes

Students enrolled in this course will learn the following:

• Techniques in classification, regression, and conditional probability estimation.
• Differences between generative and discriminative models.
• Advanced linear models and non-linearity with kernel methods.
• Ensemble methods including boosting, bagging, and random forests.
• Representation learning including clustering, dimensionality reduction, autoencoders, and deep nets.

Prerequisites

Before enrolling in this course, learners should have:

1. Completed the previous courses in the MicroMasters program: DSE200x and DSE210x.
2. A strong foundation in:
   • Multivariate calculus
   • Linear algebra

Course Content

• Understanding and implementing supervised and unsupervised learning algorithms.
• Application of theory to real-world data through case studies.
• Developing predictive models to analyze document contents and image recognition.
• Using Python and Jupyter notebooks for data science projects.

Who Should Enroll

This course is well-suited for:

• Individuals aiming to pursue a career in data science.
• Experienced professionals looking to harness machine learning in data analysis.
• Academics seeking to understand real-world applications of theoretical concepts.

Real-World Application

Skills gained in this course can be applied in various real-world scenarios:

• Development of advanced machine learning models for predictive analytics in industries like finance and healthcare.
• Enhancement of user experience through personalized content recommendations in applications like streaming services.
• Improvement of operational efficiency by automating tasks and data handling in business operations.