Data Analytics Immersive Bootcamp for Active Duty Service Members

Begin your journey into data analysis by learning essential coding skills and foundational tools.

• Contribute to Software and Analytics Projects – Learn to collaborate effectively using version control tools like Git and GitHub, essential for any software or analytics project.
• Develop Efficient Project Workflows – Establish foundational knowledge in Python to create structured and efficient workflows for software development or data analytics.
• Data Processing and Manipulation – Leverage powerful libraries like NumPy and Pandas to process, manipulate, and clean real-world data.
• Communicate Complex Results – Use Matplotlib and Seaborn to visually communicate your findings, making complex data insights understandable and actionable.
• Query SQL Databases – Access and manage data from relational databases using modern SQL techniques, a crucial skill for any data analyst.

Case Studies:

• Week 1 – Text Processing: Apply Python fundamentals to identify, correct, and preprocess text for further analysis.
• Week 2 – Data Analysis: Use Python tools to extract meaningful insights from large and complex datasets.
• Week 3 – Data Visualization: Visualize insights derived from three different real-world datasets.
• Week 4 – SQL: Use SQL and Python to query complex relational databases and analyze the results.

Synthesize the skills you’ve acquired by finding, extracting, and analyzing key data to draw important insights.

• Master the art of transforming unstructured, messy data into actionable insights with real-world applications.
• Learn to effectively communicate complex data to non-technical stakeholders.
• Uncover critical gaps and leverage data visualization tools to showcase key insights

Building on the Python fundamentals, this unit delves into the analysis of more complex real-world data using classical statistical techniques and introduces foundational machine learning concepts.

• Structuring Problems Probabilistically – Transform real-world problems into well-defined probabilistic models, a key skill in data-driven decision-making.
• Hypothesis Testing – Measure outcomes in datasets with hypothesis testing to determine the significance of your findings.
• Foundational Machine Learning Models – Gain hands-on experience with linear and logistic regression models, which are essential tools for predicting and understanding data trends.

Case Studies:

• Week 6 – Hypothesis Testing: Help guide decisions by determining whether observed differences in data are statistically significant or merely due to chance.
• Week 7 – Linear Regression: Predict values and assess their impact on target outcomes using linear regression.
• Week 8 – Logistic Regression: Use logistic regression to predict the likelihood of success or failure of specific events.

This unit focuses on practical applications in deploying and sharing your data science models, covering everything from model packaging to creating accessible APIs. By the end of this unit, you’ll know how to containerize, deploy, and communicate with models, making your work accessible and scalable for real-world applications.

• Model Deployment Basics: Learn the various methods of deploying data science models.
• Docker for Data Science: Understand how to use Docker to containerize your models and streamline the deployment process.
• API Development: Build and execute API calls to make your models accessible to other applications and users.
• Tools and Frameworks: Get hands-on with key deployment tools like Streamlit and FastAPI, enabling you to deploy interactive applications and APIs.

Case Studies:

• Week 9 – Combine mathematical modeling with Streamlit to create an interactive model that can be integrated into web or local dashboards.
• Week 10 – Learn how to deploy already created models into cloud services like Amazon using tools like FastAPI and API endpoints.

The Capstone Project is the pinnacle of your learning experience, where you’ll apply everything you’ve learned to solve a real-world problem. You will:

• Identify and analyze critical data to accomplish mission goals.
• Extract actionable insights to inform leadership decisions.
• Present your findings in a Capstone Presentation to peers and leadership, demonstrating your ability to translate data into strategic decisions.

This curriculum not only equips you with technical skills but also empowers you to make impactful contributions in the field of data analytics from day one.