SABR Analytics Certifications: Levels 1-3
September 2023 - October 2023
The SABR Analytics course provides a comprehensive learning experience in the field of baseball analytics through a series of hands-on exercises and theoretical discussions. Participants are introduced to essential concepts and tools used in sports analytics, with a focus on baseball.
Data Visualization and Analysis:
Learn how to use ggplot, a data visualization package in R, to create visual representations of statistical correlations between different variables. This includes understanding the visual representation of data patterns and relationships.
Statistical Modeling:
The course delves into statistical modeling techniques, including linear regressions and gam (generalized additive models). Participants analyze p-values and r-squared values, essential for evaluating the significance and goodness of fit of their models.
Team and Player Projections: Participants practice generating team and player projections by training and testing data. They employ metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation functions to assess the accuracy of these projections, allowing them to refine their predictive models.
Exploration of Baseball Data and Analytics:
The course covers fundamental aspects of baseball data and analytics, differentiating between descriptive and predictive data. Participants explore various metrics and statistics, understanding their practical applications in the context of player performance analysis and team strategies. Additionally, the course emphasizes player comparisons, enabling students to make informed assessments based on data-driven insights.
Utilization of Statcast Data:
Work with advanced data sources like Statcast, incorporating metrics such as Exit Velocity and Barrel % into their analyses. By leveraging these metrics, participants learn to surpass standard projections like MARCEL, enhancing the accuracy of their forecasts and gaining a competitive edge in player performance predictions.
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SQL for Data Science
July 2022
Introduction to SQL:
The course begins with an introduction to SQL, explaining its syntax, basic queries, and the fundamental principles of database management. Learners get hands-on experience writing SQL queries to retrieve, filter, and sort data.
Data Cleaning and Transformation:
Participants learn how to use SQL to clean and transform raw data. This includes techniques for handling missing data, data validation, and structuring data in a way that is conducive to analysis.
Data Aggregation and Analysis:
The course covers advanced SQL topics related to data aggregation. Learners explore how to calculate summary statistics, create derived metrics, and perform complex aggregations. They gain insights into group functions, subqueries, and joins, which are crucial for in-depth data analysis.
Working with Multiple Tables:
Understanding relational databases is a key component of data science. Participants are taught how to work with multiple tables, exploring concepts such as foreign keys, primary keys, and table normalization. Join operations, including inner and outer joins, are covered extensively.
February 2022
Visual Analytics with Tableau
Introduction to Tableau:
The course begins with an introduction to Tableau, familiarizing learners with its user interface, basic functionalities, and the underlying principles of visual analytics. Participants gain hands-on experience in navigating Tableau's workspace and connecting to various data sources.
Data Preparation and Cleaning:
Participants learn how to prepare and clean data for visualization. This includes understanding data types, handling missing values, and transforming data to make it suitable for analysis. Techniques for data aggregation and filtering are also covered.
Creating Visualizations:
The course guides learners through the process of creating a wide range of visualizations, such as bar charts, line graphs, scatter plots, and geographic maps. Participants explore customization options to enhance the clarity and effectiveness of their visualizations.
Interactive Dashboards:
One of the core strengths of Tableau is its ability to create interactive and dynamic dashboards. Participants learn how to combine multiple visualizations into cohesive dashboards, enabling users to explore data and gain insights through interactive filters and parameters.
Advanced Visualization Techniques:
The course covers advanced visualization techniques, including trend analysis, forecasting, and heat maps. Participants delve into complex visualizations that involve calculated fields, sets, and groups, enabling them to represent intricate patterns and relationships within data.
Storytelling with Data:
Effective data visualization is not just about creating charts; it's about telling a compelling story with the data. Participants learn how to craft data-driven narratives using Tableau, integrating visualizations into a coherent and persuasive storyline that can be shared with stakeholders.
Real-World Applications:
Throughout the course, real-world examples and case studies are used to demonstrate how Tableau is utilized in various industries and contexts. Participants gain practical insights into applying visual analytics to solve complex business problems and make data-driven decisions.
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