Course Outline
Introduction to Data Science
The course begins by defining data science. We will examine the data science workflow and its application to real-world business challenges. The chapter concludes with guidance on structuring your data team to meet your organization's specific needs.
Analysis and Visualization
In this section, we discuss techniques for exploring and visualizing data via dashboards. We will cover the key components of a dashboard and how to formulate effective requests for one. This chapter also addresses making ad hoc data requests and conducting A/B tests, which serve as powerful analytics tools to mitigate decision-making risks.
Data Collection and Storage
With a solid understanding of the data science workflow established, we will delve deeper into the initial step: data collection. You will learn about the various data sources your company can utilize and the methods for storing that data once collected.
Prediction
In this final chapter, we tackle one of the most prominent topics in data science: machine learning! We will cover both supervised and unsupervised machine learning, as well as clustering. We will then explore specialized machine learning topics, including time series forecasting, natural language processing, deep learning, and explainable AI!
Testimonials (1)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
