INTRODUCTION
Our world in its entirety can be broken down into numbers. Nearly every aspect of our lives can be subjected to either qualitative or quantitative analysis and it is for this reason that data analysis is a crucial skill to have. In the simplest of terms, data analysis can be described as the practice of working with data to derive meaningful information from it. It is a useful skill to have not just for career reasons but personal as well. Think of it this way, the ability to analyze your monthly needs and allocate funds to satisfying the needs is a good example of data analysis that is often overlooked.
DATA ANALYSIS TECHNIQUES
Data analysis techniques can be broadly grouped into Quantitative and Qualitative methods. Quantitative data is based on numbers and information that can be countable while qualitative data is based on interpretation. It therefore follows that these two methods use different techniques to analyze the data in them. Some of the data analysis techniques that fall under Quantitative methods are;
Inferential Statistics; this techniques enables an analyst to come to conclusions and make predictions about a population dataset based on a sample of the same dataset using t-tests, regression analysis and hypotheses testing.
Data Mining; Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve problems using algorithms. Data mining techniques and tools help enterprises to predict future trends and make more informed business decisions.
Descriptive Statistics; a descriptive statistic is a summary statistic that summarizes features from a collection of information. The way that datasets can be summarized is by use of mean, median, mode and percentages.
Experimental design; the process of carrying out research in an objective and controlled fashion so that precision is maximized. The major intention with experimental design is to determine causal relationship between variables.
For qualitative data, some of the most commonly used methods of analysis include;
Content analysis; this is s a research tool used to determine the presence of certain words, themes, or concepts by studying documents and communication artifacts which may be in various formats such as picture, audio or video.
Thematic analysis; thematic analysis technique identifies recurring themes or patterns in qualitative data by categorizing the data.
Narrative analysis; Examines stories or narratives to understand experiences, perspectives, and meanings.
Grounded Theory; Develops theories or frameworks based on systematically collected and analyzed data, allowing theory development to be guided by the analysis process.
With the above in mind, we now need to be familiar with the software solutions designed to handle various aspects of data evaluation including collection, processing and analysis, in layman's terms analytics tools. Some of the most prominent data analysis tools are SQL, Python, Excel and tableau to name a few. It is imperative for a data analyst to be acquainted with as many tools as possible.
CONCLUSION
Data analysis tools and techniques are broad in their applications and this calls for anyone who intends to be an analyst to constantly train and practice whichever technique they want to major in.
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