Boston University's RISE Data Science Practicum Track

Welcome to the official GitHub repository for the Data Science RISE Practicum Track course! This course introduces formal statistical thinking and computational methodologies to empower high school students to become skilled computational scientists. Here, you'll find many of the resources, code, and collaborative materials necessary to succeed in the course and Practicum.

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Course Overview

In this course, students will:

• Learn the principles of formal statistical thinking to gain insights into measurement, data collection, experimental design, and uncertainty quantification.
• Analyze real-world datasets from fields like neuroscience, environmental science, physics, economics, and urban planning.
• Develop skills in computational modeling, scientific reproducibility, and rigorous ethical practices for data-informed research.

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RISE Practicum

The RISE Practicum provides students with hands-on project/research experience in in one of four key areas:

1. Time Series Analysis and/or Prediction
2. Biostatistics-Style Analysis
3. Scientific Software Package Development
4. Teaching a Statistical Technique

The Practicum combines instructor-led morning lectures with collaborative lab work in the afternoon. Students will present their research findings at the RISE Poster Symposium at the program's conclusion.

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Instructors

Meet the dedicated instructors for the RISE Data Science Practicum Track who guide students through this immersive learning experience:

• Course Designer and Morning Lecturer: [Patrick F. Bloniasz, Computational Neuroscience PhD Candidate]

  • Expertise: [Brief description of their expertise and background]
  • Contact: [pblonias [at] bu.edu]

• Lab Lead Lecturer: [Name, Title]

  • Expertise: [Brief description of their expertise and background]
  • Contact: [Email or other contact details]

• Teaching Fellow: [Name, Title]

  • Expertise: [Brief description of their expertise and background]
  • Contact: [Email or other contact details]

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Getting Started

Follow these steps to set up your tools.

1. Create a GitHub Account

If you don’t have one already, sign up here (send username to Patrick at pblonias ( at ) bu.edu with your BU email):
GitHub Signup

2. Install Anaconda

Anaconda bundles Python (≥ 3.7) and most scientific packages you’ll need.

• Download: https://www.anaconda.com/download/success
• Quick Tutorial: YouTube: Installing Anaconda

3. Install VS Code

Our recommended editor with excellent Python support.

• Download: https://code.visualstudio.com/download
• Python Extension: After installation, search for and install the “Python” extension in the Extensions view.

4. Installing Git (if you haven't before) and using GitHub

You’ll need Git and the GitHub extension for this class.

• Download: https://git-scm.com/downloads
• Getting Started: YouTube: Git & GitHub in VS Code

5. Install mamba (a faster conda frontend)

# install mamba into your base conda environment
conda install mamba -n base -c conda-forge -y

6. Download & Open the Course Notebook

We’ll be working on the notebook hosted in Colab.

1. Download the notebook. Go to: https://colab.research.google.com/drive/11UIvGczmvhcR1YdMjXfwDW_iwRbpBQyM

2. In Colab’s menu: File → Download → Download .ipynb and save it into a folder on your machine.

3. Open in VS Code: In VS Code, choose File → Open Folder…, select the folder containing your .ipynb.

4. Click the notebook file in the Explorer panel; VS Code will render it inline.

Make sure your interpreter (bottom-right corner) is set to an environment with jupyter installed.

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