360dissertations understands how tough Masters level projects can be. The standard of a post graduate course is higher than a bachelor’s degree.
Data occupies the ultimate position in the research process. A research procedure involves collection, analysis and interpretation of data. In the current era, one can easily collect plenty of information from various sources. However, not all data gathered will be useful and relevant to a study. One should thoroughly inspect and decide which information would help them in conducting their study. This is when the data mining process comes into the picture.
Data mining process involves filtering and analysing the data. It uses various tools to identify patterns and relationships in the information, which is then used to make valid predictions.
This process involves several techniques such as clustering, association, classification, prediction, decision tree, sequential pattern techniques have been developed which are widely used in various fields.
To conduct data ming process without any hassle, plenty of tools are available in the market. Among them, the most popular software trusted by the research community include:
Weka - This machine learning tool, also called as Waikato Environment, is built at the University of Waikato in New Zealand. Written in the JAVA programming language, this software supports significant data mining tasks such as data processing, visualisation, regression, and many more. Additionally, Weka is best suited for data analysis as well as predictive modeling. It consists of algorithms, visualisation software that support the machine learning process and operates on the assumption that data is available in the form of a flat-file. Weka has a GUI to give easy access to all its features in addition to SQL Databases via database connectivity.
Orange - This component based software aids data mining and visualisation process. Written in Python computing language, its components are known as ‘widgets’. The widgets range from data visualisation, preprocessing to the evaluation of algorithms and predictive modeling. The widgets offer characteristics such as presenting data table & enabling to choose features, reading the data, comparing learning algorithms, etc. Data in Orange gets formatted to the desired pattern swiftly and can be easily moved by simply flipping/moving the widgets. It also allows the user to make smarter decisions by comparing & analyzing the data.
KNIME - This tool is considered as the best integration platform for data analytics. Operating on the theme of the modular data pipeline, KNIME uses the assembly of nodes to preprocess the data for analytics & visualisation process. It constitutes different data mining and machine learning components embedded together. This tool is popularly used by the researchers for performing a study in the pharmaceutical field. KNIME includes some excellent characteristics, such as quick deployment and scaling efficiency. Additionally, predictive analysis is made accessible to even naive users.
Sisense - Considered as the best suited BI tool, Sisense has the potential to manage and process small as well as a large amount of data. Designed specially for non-technical users, this software enables widgets as well as drag & drop features. Sisense produces reports that are highly visual and lets combining data from different sources to develop a common repository. Further various widgets can be selected to develop the reports in the form of line charts, pie charts, bar graphs, etc. based on the purpose of a study. Reports can be drilled down merely by clicking to investigate details and comprehensive data.
DataMelt - DataMelt, also called as DMelt is a visualisation and computation environment offering an interactive framework to perform data mining and visualisation. DMelt is written in JAVA programming language, is designed mainly for technical users and for the science community. It is a multi-platform utility and can work on any operating system that is compatible with Java Virtual Machine (JVM). DMelt consists of scientific libraries to produce 2D/3D plots and mathematical libraries to develop curve fitting, random numbers, algorithms, etc. This software can also be utilised for analysis of large data volumes or statistical analysis.
SAS data mining - SAS or Statistical Analysis System is developed by SAS Institute for the purpose of analytics & data management. This tool can mine data, modify it, and handle data from various sources and conduct statistical analysis. It allows the user to analyse big data and derives precise insight to make timely decisions. SAS offers a graphical UI for non-technical users and is well suited for text mining, data mining, & optimisation. The added advantage of this tool is that it has a highly scalable distributed memory processing architecture.
IBM SPSS modeler - Owned by IBM, this software suite is used for data mining & text analytics to develop predictive models. IBM SPSS modeler consists of a visual interface that lets the user to work with data mining algorithms without any need for programming. It offers additional features such as text analytics, entity analytics etc. and removes the unnecessary hardships faced during the data transformation process. It also allows the user to access structured as well as unstructured data and makes it easy for them to use predictive models.
Data mining tools are important to leverage the existing data. Adopt trusted & relevant tools, use them to the fullest potential, uncover hidden patterns & relationships in data and make an impact for your research.
One question that irks most PhD scholars is the time their PhD would take to complete and it is the question that at the same time remains as the most unavoidable question because a PhD programme never comes with a time tag attached to it, unlike various other courses that we do prior to it. The moment you have been in the programme for few semesters, the question starts hovering above your head. How much time would it take? When would you finish? And these never ending questions come from sources that are never ending. Starting from your family to your colleagues, people in the university, bank, gym and everywhere want the answer to this particular question and ironically you would not have the answer to this question, most of the times.
No supervisor would every say that you can do without editing your work. Editing is an essential part of thesis writing and surely as important and difficult as writing it is. How much ever perfect you think your writing skills are, you would always find scope to remove errors upon editing and proofreading. Researchers have their own set of choice between self-editing and hiring a professional editor for doing the work. Both options have their own set of advantages and disadvantages. Here are few to share:
Planning a research trip is a common component of the doctoral journey. Amongst all PhD, there is only a small proportion that can be written sitting at one place only on the basis of the material that is found online. Travel is important in research for reasons that can be many. It could be to consult archives, do fieldwork or do some collaboration or consultation with colleagues at other locations. You may have to travel more than one time, over the period of the tenure of your PhD or even take up an extended research trip that could last six months or even longer at time, if your PhD so demands.
We spend years researching and reading on a topic and consequently we become a store house of information. When we sit to pen down those ideas on a piece of paper, it can be very challenging to shrink down that entire information into a stipulated word count that has been given to you. Struggling with accommodating relevant information in a limited word count is a challenge for the writers, particularly at the doctoral level where every research paper, assignment or even the thesis has a certain designated word count. Here are the ways to do it: