Level: Beginner to Intermediate
At the heart of the data science process is the analytic model, which provides deep insights that improve decision-making and deliver business value from data. This course provides an overview of the practices that data scientists use to build, validate, and deploy these machine learning and AI models.
In the context of data science project stages, students will learn key principles of analytic modeling, with an emphasis on techniques such as classification, regression, and clustering. Students will receive an overview of common statistical techniques and algorithms that are used in analytic models, how they are matched to business objectives and available data, and how the models are tuned and validated. The course will also cover key technologies that enable model development and management, and examples will reinforce key concepts.
This is part of an optional Data Science Bootcamp. Learn more about the courses offered, or attend this individual course.
You Will Learn
- What analytic models are, and how they are developed to support business solutions
- The statistical and algorithmic basis of analytic models
- The purpose of machine learning, deep learning, and AI
- Common analytics techniques such as classification, clustering, association, sequencing, and more
- Statistical methods such as linear regression and their role in data science
- Common algorithms such as k-means and neural networks, and how they are used in data science
- How models are tuned and validated
- Key concepts such as sample selection, training, bias, over-fit, and drift
This course is geared to technical and non-technical professionals getting started with data science, including:
- Business analysts
- Business stakeholders
- Data scientists
- Analytics practitioners
- Data engineers
- Analytics project leads
- BI and data management professionals
Experienced data scientists will find this course to be a review, but they will find it valuable if they have not been formally exposed to key principles and practices.