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Berkeley Global
This course provides students the opportunity to conduct a data science project that centers on a real-world problem of the student’s choosing. The course focuses on data science from a project-based perspective. Throughout this course students demonstrate their knowledge of data science methods and techniques to plan, build models, analyze, interpret and present their findings. The course emphasizes collaboration and problem-solving to promote “hands-on”, applied and experiential knowledge.
Prerequisites:
- An introductory course on Data Science with the Python programming language
- Introduction to Data Science COMPSCI X415.2 or equivalent
- An introductory course in Machine Learning with the Python programming language
- Introduction to Machine Learning Using Python COMPSCI X433.6 or equivalent.
- Basic knowledge of data visualization methods, tools, and techniques
- Data Visualization or equivalent
- Basic knowledge of working with databases using SQL
- Introduction to SQL COMPSCI X451.1, Introduction to Databases COMPSCI X409.1, or equivalent
Learner Outcomes
- Utilize statistics and probability essentials to manipulate and preprocess data for feature transformation, dimensionality-reduction, and model evaluation.
- Utilize essentials of Linear Algebra and Multivariate Calculus in machine learning to preprocess, transform, and evaluate a variety of features, predictors for data models.
- Utilize machine learning algorithms and objective functions like for optimization experiments and predictive modeling. (i.e., cost/objective; likelihood; error; gradient descent).
- Demonstrate mastery of essential programming skills for statistical modeling and data analysis.
- Demonstrate familiarity with essential data analytics methodologies and procedures like data wrangling and preprocessing.
- Utilize a multitude of supporting programming languages for data analytics methodologies and procedures. (i.e., excel, Tableau, Hadoop, SQL, GraphQL and Spark).
- Create data visualizations that utilize essential components like: data, geometric, mapping, scale, labels, and ethical-centricity.
- Perform experiments for continuous variable prediction and discrete variable prediction and make predictions using Machine Learning Algorithms.
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Summer 2024 enrollment opens on March 18!