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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.

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, You 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.

In this Repository:

  1. Curriculum - instructions, high level review, lessons, and labs; including content outlines, datasets, starter and solution code, and other resources.

  2. Projects - baseline materials for our course projects; including datasets, detailed instructions, and starter code.

  3. Resources - supplementary resources, including syllabi & documented guidelines.

⑃ Forking and Collaborating

The structure of this repository provides a way for us to organize our information and resources.

On the first day of class, you should fork and clone this repo locally, and add this page as the upstream master:

$ git remote add upstream https://github.com/ga-students/DSI-NYC-1.git

Each morning, we'll update the repo with new materials and projects, so each morning you should update the repo locally by running:

$ git pull upstream master