Table of Contents
Data Analysis
Data Analysis is the gathering of data. The data is being summarized. The Data is gathered on the basics of some logical reasoning and analytical reasoning concepts are also being used and that results in the formation of some patterns, relationships, or new trends. Data analysis is known as a process of analyzing, cleaning, and gathering data for any respective purpose.
Dissertation
A dissertation is described as an academic piece of paper in which is based on research and original data. It is usually written by Undergraduates and Postgraduates students after the research they have done.
Data Analysis Dissertation
It is simply a dissertation written after the analysis of assigned data. You will be handed data and the requirement will be to perform all the necessary analysis and then write a proper dissertation on it.
Tips for writing a Data Analysis Dissertation
You may ask yourself what are the things that demand special attention while writing a data analysis dissertation. Here is all you should keep in mind while writing a data analysis dissertation.
- Understand the requirements
Starting to write without understanding the requirements of the assigned academic task could be a costly mistake. Just imagine that after completing your work you realize that you have not understood the question well and all your hard work has been wasted. You will have to start all over again. So make sure that you read the question multiple times and research objectives are crystal clear in your head before starting your research or writing.
- Stay relevant
While selecting the data for your analysis proper scrutiny is necessary. You have to select data in light of the research objectives. Blindly following the collected data could be a horrible miscalculation. All the selected data should be completely relevant to the topic and research aim. Irrelevant data can give the impression of lack of focus or incompetency. You should also tell the reader that what the academic reasons behind the selection of data are. It makes the reader aware of your critical thinking ability and a sound understanding of the topic.
- Analysis approach
Selection of the analysis methods is very crucial. How do you know that a certain method suits your analysis or not? The method you select should fit both the type of selected data and your research objectives. Again, you will be required to justify the selection of those methods. Surely you don’t want to show that the methods were selected in a hurry. The reader should discover that the best choice was made through research and a critical thinking process.
- Quantitative work
Quantitative data, which is mostly a part of scientific or technical research, requires a thorough statistical analysis. The statistical analysis is done on a small portion of the targeted population; this portion is known as the sample. Once you perform the statistical analysis on the sample, the results could be generalized to the complete set of populations. But you need to make sure that the sample under study is representative of the whole population.
- Qualitative work
Qualitative data is generally non-numerical data. However, this definition may not be always true. Although there are no numbers involved but a proper analysis is still compulsory. Different techniques could be used for this sake. A couple of examples are thematic coding and discourse analysis. Analysis of qualitative data is a time-consuming and at times a complex process. Unlike the quantitative data, the results of qualitative analysis are not generalized to the population. The aim is to unveil deeper knowledge and facts which could be transferred to mankind in an understandable form.
- Decent presentation
It can be a hard job to represent a large amount of data engagingly. To begin with, you can consider all the possible means of representing the collected data. The likes of mathematical formulas, pie charts, graphs, and diagrams can be advantageous in certain situations. Tables too are a fine way of representing either qualitative or quantitative data.
While representing the data, you should consider the reader not yourself. Being a researcher you will be more familiar with a variety of layouts but not many readers will be able to understand all of them. So, your selected way of data representation should be easy and clear for your audience to understand. To address this problem, consider all possible means of presenting what you have collected.
- Strong resources
Referring to the strong resources enhances the impact of your work. So it is a must for the credibility of your work.
- Discussion and findings
You can consider different interpretations of your data. Both the applicability and shortcomings should be discussed. The impact and the significance of the research should also be discussed.
What were the final results that emerged after all the analysis? All of them should be stated clearly, backed by strong critical reasoning.
- Proofread
This is something that you must have heard several times. In brief, proofreading is imperative for the correctness of your work.
If you need further assistance, visit Assignment Studio.
