Central Time CT
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. Day 2 of the Data Science Bootcamp provides an overview of the practices that data scientists use to build, validate, and deploy these machine learning and AI models, and teaches students how to communicate analytics-driven insights to others.
Students will receive an overview of common statistical techniques and machine learning algorithms used in analytic models, including what they are and how they find patterns—without an in-depth treatment of the mathematics. 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 also learn how the models are tuned, validated, and deployed.
Data science teams leverage data in many formats with various levels of quality and often in high volume. Visualization techniques help make sense of this sea of information at every stage of a project—from establishing a business goal to identifying source data to validating analytic models. Communication with stakeholders is also a key part of the data science process, which requires calibrating visualizations for the skill level of the audience. Students will learn foundational principles for visualizing data and communicating data-driven insights.
You Will Learn
- The purpose of machine learning, deep learning, and AI, and how they are matched to business challenges
- How analytics models are trained, tuned, and validated
- 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
- Key concepts such as sample selection, training, bias, over-fit, and drift
- Best practices for applying visualization in each stage of a data science project
- Exploratory data analysis (EDA) techniques that support problem framing and source selection
- Methods for visual interpretation of data science results
- How to communicate data science insights to technical and non-technical stakeholders
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.