MS Data Science & Statistics: Computing Preparation

Computing Preparation for MS in Data Science & Statistics

To succeed in Binghamton University’s MS in Data Science & Statistics program, students are expected to be comfortable with using computers and programming for data analysis. This includes working with statistical software, writing code, and visualizing data.

Hardware Requirement

Students are expected to bring a personal laptop for use throughout the program. Most statistical and data science software used in courses will be compatible with both Windows and macOS systems.

Key Software Tools

Students in the MS in Data Science & Statistics program are expected to become familiar with the following tools used across coursework, research, and communication:

Programming & Data Science Tools

  • R & RStudio – for statistical modeling and visualization
  • Python – for data science, machine learning, and scripting
  • SQL – for managing and querying structured databases
  • Jupyter Notebooks – for writing, running, and sharing live code and analysis
  • MATLAB – for numerical analysis and linear algebra
  • SAS – for applied statistics and data handling
  • Tableau – for interactive data visualization and dashboard creation

Reproducible Reporting & Documentation

  • LaTeX – for writing technical reports, assignments, and papers with mathematical content
  • Overleaf – a cloud-based LaTeX editor for collaborative writing and document management
  • knitr / R Markdown – for integrating code and analysis into formatted PDF or HTML reports using R
  • Jupyter Book or Quarto – for publishing computational narratives combining code, equations, and prose

Most LaTeX tools and environments (e.g., Overleaf, TeX Live, MiKTeX) are free and compatible with the university's academic workflows.

Version Control & Collaboration

  • Git & GitHub – for managing code versions, collaboration, and reproducible research workflows

Other Useful Tools

  • Linux/Unix Shell – for working in terminal environments and accessing HPC clusters (optional but encouraged)
  • Excel – for basic data wrangling and interfacing with statistical tools
  • Virtual Lab or Remote Desktop Software – for accessing university-licensed software and computing environments remotely

Recommended Online Courses

To prepare for the program, we recommend completing one or more of the following optional online courses before your first semester:

Programming in R

Python for Data Science

SQL for Data Management

Statistics & Math Refresher