Course Outline
Introduction to Data Science
We will begin by defining data science and covering the data science workflow, demonstrating how it addresses real-world business problems. The chapter concludes with strategies for structuring your data team to meet your organization's needs.
Analysis and Visualization
In this section, we will discuss methods for exploring and visualizing data via dashboards. We will examine dashboard components and how to formulate targeted dashboard requests. The chapter also covers ad hoc data requests and A/B tests, which serve as potent analytical tools to mitigate decision-making risks.
Data Collection and Storage
With a clear understanding of the data science workflow, we will focus on the initial step: data collection. We will identify the diverse data sources available to your company and learn how to store the data once it has been gathered.
Prediction
In this final chapter, we will tackle one of the most prominent topics in data science: machine learning! We will cover supervised and unsupervised learning, as well as clustering. Then, we will proceed 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.
