Data Analysis

• Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
• Types of Data Analysis: Text Analysis, Statistical Analysis, Diagnostic Analysis, Predictive Analysis, and Prescriptive Analysis
• Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover a pattern in large data sets using databases or data mining tools.
• Statistical Analysis shows “What happen?” by using past data in the form of dashboards. Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data.
• There are two categories of Statistical Analysis – Descriptive Analysis and Inferential Analysis.
• Descriptive Analysis: Analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.
• Inferential Analysis: Analyze sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.
• Diagnostic Analysis: shows “Why did it happen?” by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data.
• Predictive Analysis shows “what is likely to happen” by using previous data. The simplest data analysis example is like if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses.
• Prescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a current problem or decision. Most data-driven companies are utilizing this analysis because predictive and descriptive Analysis are not enough to improve data performance.
• The Data Analysis Process is nothing but gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it. Based on that information and data, you can make decisions, or you can get ultimate conclusions.
• Data Analysis consists of the following phases:

  • Data Requirement Gathering , Data Collection, Data Cleaning , Data Analysis , Data Interpretation , Data Visualization
• Data Requirement Gathering


  In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this Analysis.
• Data Collection


  Collect your data based on requirements. Once you collect your data, remember that the collected data must be processed or organized for Analysis
• Data Cleaning


  The data should be cleaned and error free. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.
• Data Analysis


  Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data.
• Data Interpretation


  After analyzing your data, it’s finally time to interpret your results. You can choose the way to express or communicate your data analysis either you can use simply in words or maybe a table or chart.
• Data Visualization


  Very common in your day to day life; they often appear in the form of charts and graphs. In other words, data shown graphically so that it will be easier for the human brain to understand and process it.