Intermediate Concepts in Data Science

Course Overview

Covering a range of essential topics, starting with a deep dive into Linear Regression, exploring its parameters and limitations, this course is designed for individuals looking to deepen their understanding and skill set in the field of data science beyond the basics.

Participants will learn about Cluster Analysis, including how to divide unlabelled data and its common applications. The course also delves into Intermediate Statistics and Probability, covering concepts such as normal distribution, tendency, skewness, and measures of variability.

A comparison between Supervised vs. Unsupervised Learning will highlight the value of labelled data and its verification. An introduction to Neural Networks will provide insights into their structure and the key tools and platforms for their deployment. Finally, the course will address the practical aspects of deploying machine learning models, focusing on hosting, continued learning, and model improvement in production environments.

This course is ideal for those with a foundational knowledge of data science, aiming to leverage advanced techniques and insights for more complex data science projects.

Learning Objectives

• Gain a comprehensive understanding of Linear Regression, including its applications, limitations, and the required parameters for effective model construction and analysis.
• Develop skills in Cluster Analysis to effectively divide unlabelled data into meaningful categories, understand the support for multiple clusters, and explore common applications of clustering techniques. 
• Enhance knowledge of Intermediate Statistics and Probability, including the ability to work with normal distribution, analyse data tendency and skewness, and calculate standard deviation and variance. 
• Differentiate between Supervised and Unsupervised Learning, understand their respective applications, and learn how to use unsupervised learning to confirm the accuracy of labelled data. 
• Obtain foundational knowledge of Neural Networks, including their structure, how to define network nodes, and familiarise with key tools and platforms for neural network development. 
• Learn the practical aspects of deploying machine learning models, including strategies for hosting, ensuring continued learning, and updating or improving models in a production environment.