Review: The 10 Statistical Techniques Data Scientists Need to Master (Article Critique Sample)

Article Critique
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Article Critique
Data scientists use several statistical techniques to do their work. However, there are specific statistical techniques that every data scientist requires to excel. In the article "The 10 Statistical Techniques Data Scientists Need to Master," Le (2017) discusses ten statistical techniques that every data scientist needs to master. Although the author presented valuable information on the statistical methods that every data scientist needs to master, the author failed to provide more references in the article to enhance credibility.
Le (2017) has discussed ten essential statistical techniques that every data scientist needs to master to excel. The author has highlighted the skills an individual requires to become a data scientist, for instance, critical thinking, statistical skills, and coding ability (Le, 2017). According to the author, classification, linear regression, resampling methods, subset selection, shrinkage, dimension reduction, nonlinear models, tree-based methods, support vector machines, and unsupervised learning are the most important statistical techniques that data scientists should master.
The author has highlighted essential techniques to help aspiring data scientists understand the critical skills required to excel in the field. He has also examined all the necessary statistical methods popularly used by data scientists in day-to-day work. While discussing each statistical technique, the author provided relatable examples and discussed each method in detail, making it easier to understand how and where to apply each technique. For instance, while discussing the classification technique, the author first stated where to use it, then highlighted the significant sub-strategies under classification and how they can be applied accompanied with examples. Providing examples and highlighting how and where the techniques can be used shows the author's in-depth knowledge of the topic.
The information in the article shows that the author conducted extensive research on the topic. He has used clear language and simplified technical terms to enhance amateur data scientists' understanding of the article. Also, the author has presented relevant and accurate examples in the report. Most of the author's examples are relatable, for instance, determining the number of cancer patients (Le, 2017).
However, the author has failed to quote other sources or authors while discussing the statistical techniques. It was essential to cite outside sources and reference them to enhance credibility. A research paper or article is considered unreliable if an author references a single author or fails to mention any. It is essential to incorporate data from various authors and researchers to eliminate bias and improve a paper's quality. There are several articles and researches on the statistical techniques data scientists need to master, and different authors have different opinions. In this regard, incorporating those opinions could have helped Le present a two-sided article highlighting the ten techniques, their importance, the authors who agree they are essential, and the ones that disagree