If you’re working for a company or a school project, chances are you have to deal with tremendous amounts of data. However, if you don’t know the methods of analyzing information, the data collection process that you might have spent lots of time in will go in vain. Therefore, here’s a quick guide on how to parse data and obtain helpful information on them so that you can write an essay or inform your decisions.
What Are You Analyzing Exactly?
This step is critical. If you don’t know what data you are facing, you will not know how to inspect information even if you know everything about the appropriate methods. Therefore, if you are unsure, take your time to re-read your research question again. Alternatively, you can re-evaluate what the data is about by reading the first few rows of your data. If you have made documentation about the project, you can also refer to them. You might even find a tutorial written by yourself on how to correctly analyze the data in your project.
Furthermore, the methods below likely require you to have decent programming skills. Depending on what you are analyzing, you need it to conduct complex mathematical operations that may be essential for your project. You probably have already used your coding skills when collecting the data. However, if you do not know programming, don’t be afraid to learn it now!
If you’re still unsure, keep reading, and you might find some valuable data!
Methods of Analyzing Data
- Extracting percentages
- Classifying data
- Finding correlations
- Detecting statistical significance
- Searching for unusual data
- Visual, audio, or text analysis
- Running simulations
1. Extracting Percentages
The first method is straightforward. It calculates the percentages from the data available. This may seem too simple for any data science project, but it is the primary method used for many useful analyses, like comparing the effectiveness of multiple antivirus solutions.
2. Classifying Data
This method, also known as classification, is also very simple. It is most likely an intermediate step to further data analysis like the previous method. It basically involves putting data points in different categories based on their characteristics so that more processing can be done.
3. Finding Correlations
If you are looking for how data points flow based on the value of another parameter, it may help find correlations. For example, let’s say you hypothesize that a specific material decreases its density when the temperature gets higher. After that, you measure the density of the material at different temperatures and make a line graph of it.
There is a correlation coefficient in every dataset that can be represented as a line graph or scatter graph. We are not going to explain how it is calculated since it’s very complicated, but correlation coefficient calculators readily exist, just like the other tools explained in this article. If the coefficient is positive, it represents a positive correlation. Alternatively, if that number is negative, it signifies a negative correlation. A positive correlation is a situation in which when a variable increases, another variable also increases. A negative correlation indicates the opposite.
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For the hypothesis described in the first paragraph in this section, it’s a negative correlation, as everyone knows. The density of a material generally decreases as the temperature increases.
4. Detecting Statistical Significance
In some cases, if you want to determine whether the disparity between two datasets is caused by an actual difference, detecting statistical significance is your best bet. For this analysis, you need to use a null hypothesis, which hypothesizes that the difference between the two datasets is just by chance.
At this point, we’d like to introduce the p-value, which is the determining factor in statistical significance. It is the probability that we see a result as extreme as the result seen in the study. If the p-value is less than 0.05, the datasets are over two standard deviations away, the null hypothesis is rejected, and it’s determined that there may be a genuine reason that causes the two datasets to be vastly different. Again, we will not explain how these values are obtained, but existing functions that calculate p-values from two sets of information also exist.
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5. Searching for Unusual Data
If you want to make discoveries from observations, you need to filter out the normal data. For instance, if you want to discover asteroids after taking images of a particular patch of the sky, you need to first filter out objects that don’t move from image to image. This is because they are stars and not asteroids. Secondly, even if you find moving bright spots, you should still verify if it is a new object by comparing the current location of all known asteroids with the location of the asteroid candidate. If it does not find a match, the system will treat the object as a potentially new asteroid.
6. Visual, Audio, or Text Analysis
Ways of analyzing more abstract data often use machine learning and artificial intelligence algorithms. This includes analyzing images, sound, or text to classify them, determining indexes to help identify correlation, or finding what they’re trying to explain. For instance, if you want to determine whether fake news attracts more people to visit their websites, you shall search for suitable algorithms and train them to analyze the text to see if the article is likely conveying fake information. In that case, the essays can be classified, and further analysis can be conducted.
7. Running Simulations
Sometimes, the only way to get information from chaotic systems is to run simulations. It is because its behavior usually cannot be precisely predicted. For instance, if you need to predict how the Solar System will behave in the next billion years, you will not be able to calculate the exact position of the planets since there are too many gravitationally interacting bodies. Keep in mind that the 3-body problem is still unsolved, let alone systems with many more objects.
As a result, to learn about that, multiple simulations will be run, each with slightly different parameters. Because the simulation only approximates where the planets will go, it is by no means perfect, and results might differ from reality significantly in the long run. Nevertheless, it remains the best method to survey systems where even if a single parameter changes slightly, the entire system will exhibit vastly different behavior.
Conclusion
In this article, we introduced how you can analyze data in your project so that you can write a report about your research. Remember to make graphs after the data has been parsed, and write your article in a way that attracts readers to keep reading!
References and Credits
- Nickolas, S, et al. (n.d.). What Do Correlation Coefficients Positive, Negative, and Zero Mean?. Retrieved March 27, 2022, from https://www.investopedia.com/ask/answers/032515/what-does-it-mean-if-correlation-coefficient-positive-negative-or-zero.asp
- Bevans, R. (2020, July 16). Understanding P-values | Definition and Examples. Retrieved March 27, 2022, from https://www.scribbr.com/statistics/p-value/
- Laskar, J., Gastineau, M. (2009, June 11). Existence of collisional trajectories of Mercury, Mars and Venus with the Earth. Nature 459, 817–819 (2009). Retrieved March 28, 2022, from https://doi.org/10.1038/nature08096