CRC software engineers and CIRE investigators develop new automated search warrant generator
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The Fourth Amendment to the U.S. Constitution safeguards against unreasonable searches. It mandates that search warrants must be based on probable cause and must specifically describe the items to be seized. However, applying this constitutional requirement to cell phones is a significant challenge.
To tackle issues like determining the relevant data to the investigation, time frame covered by search warrants, and the inconsistency of rulings across jurisdictions, the Center for Research Computing (CRC) and the Cybercrimes Investigations, Research, and Education (CIRE) initiated a project to standardize cell phone search warrants.
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Python and R are two of the most popular programming languages for data analysis and scientific computing. As data sets grow larger and analyses become more complex, it is important to optimize your workflow in Python and R to improve efficiency. Here are some strategies to streamline your workflow:
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Data preprocessing: Before you start analyzing your data, take some time to clean, transform, and organize it. This will help to ensure that your analysis is accurate and efficient. There are a number of powerful libraries available for data manipulation in both Python and R, such as pandas and dplyr. These libraries can help you to quickly and easily load, clean, and explore your data.
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Selecting the right libraries: There are many different libraries available for data analysis in Python and R. Not all of these libraries are created equal, so it is important to choose the ones that are most appropriate for your specific task. For example, if you are working with a large dataset, you may want to use a library that supports parallel processing. There are a number of libraries that can help you to parallelize your code, such as multiprocessing in Python, and parallel in R.
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Using vectorized operations: Vectorized operations are a way of performing computations on entire arrays or data frames at once, rather than looping through individual elements. This can significantly speed up data processing, leading to a more efficient workflow. For example, if you need to calculate the mean of a column in a data frame, you can use the mean() function in Python or mean() function in R. These functions will automatically vectorize the operation, so you don't have to worry about looping through each element in the column.
- Optimizing functions: Writing efficient functions is another important way to optimize your workflow. There are a number of techniques that you can use to improve the performance of your functions, such as using Numpy (a library that provides high-performance numerical computing capabilities) in Python.
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Employing parallel processing: For computationally intensive tasks, employing parallel processing can effectively leverage multiple CPU cores. This can greatly reduce processing times and enhance workflow efficiency. There are a number of libraries that can help you to parallelize your code, such as multiprocessing and concurrent.futures in Python, and parallel in R.
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By following these strategies, you can optimize your workflow in Python and R and improve the efficiency of your data analysis.
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Research Computing Internships
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The CRC recruits and mentors paid undergraduate and graduate internships on a rolling basis. If you have interests to join our research team please contact us at crcsupport@nd.edu.
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| Every Wednesday and Thursday in August
2:00 – 3:00 p.m.
Flanner Hall, CRC Training Room 812 (map)
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This training is available to new users and current users interested in a refresher course on how to use CRC resources. Attendees learn the basics of accessing CRC resources and submitting jobs on the CRC clusters. This course is a co-requisite when receiving a CRC account. Learn more.
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