Course Outline
Introduction to Data Science
We commence the course by defining data science. We will cover the data science workflow and its application to solving real-world business problems. The chapter concludes with guidance on structuring your data team to meet your organization's needs.
Analysis and Visualization
In this section, we discuss techniques for exploring and visualizing data via dashboards. We will examine the components of a dashboard and how to formulate specific requests for them. This chapter also addresses ad hoc data requests and A/B testing, powerful analytics tools that mitigate risk in decision-making.
Data Collection and Storage
With an understanding of the data science workflow established, we will explore the initial step in depth: data collection. We will identify the various data sources available to your company and learn how to store the data once collected.
Prediction
In this final chapter, we tackle the most prominent topic in data science: machine learning! We will cover supervised and unsupervised machine learning, as well as clustering. Subsequently, we will advance to 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.
