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08 July 2022
How to complete a master's thesis in a short time?

  How to complete a master's thesis in a short time? As we all know, most papers need a long time to prepare, so what should we do? Next, I'll share some ideas with you.

  It is very difficult to complete in a very short time. On the one hand, the master's thesis needs to be innovative and supported by a large amount of data. On the other hand, the master's thesis is often long in length and usually needs to form a complete discussion system.

  According to historical experience, even if graduate students have published many academic papers, it will take at least a few months to prepare their graduation thesis. To speed up the writing of master's thesis to the greatest extent, we need to make preparations in the following aspects:

  First, innovative achievements have been made. Master's thesis is usually carried out according to their own research results, so it is important that innovative achievements have been made before writing master's thesis. In addition, when writing a master's thesis, it often needs to be expanded and enriched on the basis of "small papers", so the quantity and quality of small papers also have a very direct impact on the graduation thesis.

  Second: the collection and collation of basic data have been completed. The writing of master's thesis needs a lot of data as support, and the time of data collection is often relatively long. For science and engineering majors, it often needs to accumulate data through a large number of experiments, while for management majors, it also needs to complete a large number of practical data (industry data) collection, and there needs to be a process of integration and analysis after data collection. Therefore, whether there is complete data is one of the key points to finish the master's thesis quickly.

  Third: relevant knowledge has been sorted out. Master's thesis will involve a lot of relevant knowledge, especially some interdisciplinary, so it is very important to sort out the relevant knowledge. Taking big data as an example, it usually involves three parts: mathematics, statistics and computer, as well as specific industry domain knowledge, which is closely related to specific research directions.