dmarinav/dsi-gitbook

Syllabus for the DSI SEA class

Seattle Data Science Immersive

Welcome to Data Science! We are building a global community of lifelong learners who are excited about using data to solve real world problems.

In this program, you’ll take on real world problems by analyzing data sets for insights and presenting findings using statistics, programming, data modeling, and business knowledge.

Welcome!

Please use the navigation bar to the left to browse our cohort's living syllabus/textbook.

Living link to gitbook

https://bradzzz.gitbooks.io/ga-seattle-dsi/content/

Your Instructors

Brad Zimmerman

Qingqing Gan

Course Value Proposition

This course is designed to give you the deep dive into the world of Data Science, focusing on the ability to analyze and convey data-driven facts in order to predict what happens next using modeling and pattern recognition. Our course prepares students to take full-time roles as Data Analysis, Data Scientists, Business Intelligence Analysts, and other roles that require advanced fluency with data. Our projects immerse students in formal data-driven scenarios in order to help them create a polished portfolio of work showcasing their ability to create and communicate machine learning insights.

What Our Students Learn

• Data Analysis & Python:
• Perform visual and statistical analysis on data using Python and its associated libraries and tools.
• Machine Learning & Modeling Techniques:
• Explore the differences between supervised and unsupervised learning through the application of various modeling techniques such as classification, regression, and clustering.
• Git, SQL, & Relational Databases:
• Gather, store, and organize your data using the data science toolkit: SQL, Git, and UNIX.
• Critical Thinking & Synthesis:
• Apply your analysis and modeling skills to real world data problems in fields like finance, marketing, and public policy.
• Visualization, Presentation, & Reporting:
• Learn to create reproducible presentations and reports and use data visualisation tools to present your findings to key stakeholders.

By the End of This Course, Students Will Be Able To:

• Collect, extract, query, clean, and aggregate data for analysis
• Perform visual and statistical analysis on data using Python and its associated libraries and tools.
• Build, implement, and evaluate data science problems using appropriate machine learning models and algorithms
• Use appropriate data visualization tools to communicate findings
• Present clear and reproducible reports to stakeholders
• Identify big data problems and understand how distributed systems and parallel computing technologies are solving these challenges.
• Apply question, modeling, and validation problem solving processes to datasets from various industries to gain insight into real-world problems and solutions.

Outcomes

Beth Miller and Casey Hills will be your outcome coaches for this cohort. They instruct you just the same as your instructional staff; they are who will be helping you get a job.

Licensing

All content is licensed under a CC­BY­NC­SA 4.0 license. All software code is licensed under GNU GPLv3. For commercial use or alternative licensing, please contact [email protected].