Data analytics employs different methodologies when it comes to analyzing qualitativeand quantitative data. When working with quantitative data, descriptive statics is the first step. Range, mean, median and mode are absolute number representations of the data set. They do not specify the logical reasoning behind those numbers. Descriptive analysis is mostly used for comparing single variables in a finite data set, for example, the populations of two different regions. Inferential statistics are used when several different values are under consideration.
This method generalizes the conclusions of data analysis to a broader range. Inferential analysis may use correlation, wherein the mutual relation between two different quantities is used for research. The two quantities can be mutually independent. It also uses regression, which analyzes two different variables which are mutually related. Mean variance is a technique that examines a range of quantities to see how one value affects the range of the second value.
TECHNIQUES USED IN QUALITATIVE DATA ANALYSIS
Qualitative analysis also employs different methods. Content analysis is a method where responses from feedback forms, interview answers or from any documented material are used to fuel research objectives. This method is most often used. Narrative analysis is another method where people’s testaments, interview responses and similar forms of narrative are used for analytics. This method may obtain data from any source. Discourse analysis is a technique that breaks down the users most common habits and allows researcher’s to derive conclusions. Grounded theory explores a variety of similar situations to scientifically infer the interrelation between them.
THE IMPROVEMENTS NEEDED IN DATA ANALYTICS
Data analytics relies on data. While this data is beneficial, it must also be highly secure. There have been increasing debates on the privacy policy of consumers when accessing the internet. Third- party applications often exploit the available data in the wrong way. It becomes essential for companies to secure data better and respect the user rights.
The company must ensure that their available data is useful and reliable. The data analytics tools and techniques often are expensive and may result in losses if the data provided is manipulative. As a data analyst, an individual must have a knack for selecting the most productive sets of data and tools. If used ineffectively, it may increase the load on the company. There is also an existing confusion between facts and opinions in data analysis. Facts are indisputable, concrete that are supported by concrete evidence. Opinions are in the form of estimates which may vary depending on the scenario. Data analysts should heavily rely on facts since they produce results with fewer errors. Data analysis may also be rendered ineffective by cognitive bias.
This means that an individual looks for facts asserting his conclusion rather than arriving at it in a neutral way. Innumeracy is an issue, whereas the data analysts are not able to effectively communicate business numbers to others. There are many strategies which are developed to overcome innumeracy and cognitive bias problems.Data science course
