DATA ANALYSIS
Data analysis is the process of cleaning, changing, and processing raw data and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs. A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the past or future and making a decision based on that analysis. It’s not uncommon to hear the term “big data” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information.
Statistical analysis:
Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.
Descriptive analysis: Descriptive analysis looks at numerical data and calculations to determine what happened in a business. Companies use descriptive analysis to determine customer satisfaction, track campaigns, generate reports, and evaluate performance.
Inferential analysis: Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large.
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Text analysis:
Text analysis, AKA data mining, involves pulling insights from large amounts of unstructured, text-based data sources: emails, social media, support tickets, reviews, and so on. You would use text analysis when the volume of data is too large to sift through manually.
Inferential analysis:
Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large.
