Data Science Bootcamp

Curriculum

Industry-approved curriculum to support your journey into data science

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Data Science Essentials - 9 weeks

Introduction to Python - 3 weeks

• Apply the basics of programming language methodologies to real world scenarios
• Demonstrate foundational skills for scripting with Python programming language

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Introduction to Data Science - 3 weeks

• Implement foundational statistical measurement with data using scripting
• Demonstrate gathering insights from data with visualizations
• Integrate object oriented programming (OOP) with Python for data cleaning and analysis

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Introduction to SQL - 3 weeks

• Utilize industry standard techniques to analyze data with, programming language (python), structured query language (SQL), and the cloud
• Explore and manipulate data with mathematics, probability, and statistics
• Analyze data for a business problem with visualizations with a dashboard

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Data Science Foundations I - 9 weeks

Cloud Computing, Generative AI,  and Dashboards - 3 weeks

• Create a dashboard using data science methodologies with industry standard tool(s)
• Model exploratory data analysis with tools for multiple data sets. (SQL and SQL table relations)
• Utilize programming techniques to process large data samples with (PySpark and Big Data)

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Inferential Statistics - 3 weeks

• Integrate statistical inference of data using the technical programming
• Implement methodologies for statistical inference
• Utilize mathematics, statistics, and probability for data science methodologies to derive insights

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Regression - 3 weeks 

• Perform logistic regression with data sets using programming techniques, lasso and ridge
• Compare statistical results for different types of regression with data sets, linear, transformations of linear, and multiple linear regressions
• Utilize mathematics, statistics, and probability for data science methodologies to derive insights

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Data Science Foundations II - 12 weeks

Introduction to Machine Learning - 3 weeks 

• Utilize foundational machine learning modeling like decision trees and supervised learning
• Prepare data for machine learning modeling with preprocessing (feature extraction) and normalization
• Utilize mathematics, statistics, and probability for data science methodologies to derive insights

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Machine Learning with Scikit-Learn - 3 weeks 

• Utilize foundational machine learning modeling like decision trees and supervised learning
• Prepare data for machine learning modeling with preprocessing (feature extraction) and normalization
• Integrate mathematics, statistics, and probability for data science methodologies to derive insights

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Natural Language Processing, Time Series, and Neural Networks - 3 weeks

• Develop insights from language, time, and image data using neural networks and Natural Language Processing (NLP)
• Integrate mathematics, statistics, and probability for data science methodologies to derive insights

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Neural Networks and Similar Models - 3 weeks

• Create an advanced neural network application
• Integrate mathematics, statistics, and probability for data science methodologies to derive insights

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Capstone

Data Science Capstone - 15 weeks

• Integrate data science process using at least one method of regression
• Integrate data science process using at least one method of non-regression supervised learning
• Integrate data science process at least one method of non-regression unsupervised learning