Abstract
Abstract. Large part of the project manager's work can be described in terms of retrieving, processing, analysing and synthesizing various types of data from different sources. The types of information become more and more diverse (including participants, task and financial details, and dates) and data volumes continue to increase, especially for large international collaborations. In this paper we explore the possibility of using the python programming language as a tool for retrieving and processing data for some project management tasks. python is a general-purpose programming language with a very rich set of libraries. In recent years python experienced explosive growth leading to development of several libraries that help to efficiently solve many data related tasks without very deep knowledge of programming in general and python in particular. In this paper we present some of the core python libraries that can be used to solve some typical project management tasks and demonstrate several real-world applications using a HORIZON 2020 type European project and as example.
Highlights
Project management deals with many information flows that should be found, processed, reorganised and visualized
Difficult and time consuming is the step of data extraction and parsing as data preparation can take large portion of the work required to complete a data-related task. When it comes to data extraction and parsing many project managers rely on automatic pre-existing tools (e.g. MS Excel import) that, often are source of systematic errors and fail to import the right data
Recognising the growing need for a tool to process data for project management tasks and the capabilities of python, the main objectives of this paper are: 1. Identify some project management tasks related to data extraction and processing that can be automatized through programming
Summary
Project management deals with many information flows that should be found, processed, reorganised and visualized. Difficult and time consuming is the step of data extraction and parsing as data preparation can take large portion of the work required to complete a data-related task When it comes to data extraction and parsing many project managers rely on automatic pre-existing tools (e.g. MS Excel import) that, often are source of systematic errors and fail to import the right data. Python gained popularity in scientific data analysis and data processing (Lin, 2014) resulting in the development of a considerable amount of quality code packages (libraries) and documentation available Another advantage of python that we are going to actively explore in this paper is the well established platform for writing code and data analysis in the browser, Jupyter Notebook, which further improves python learning experience (Jacobs et al, 2016).
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