Data Analysis is a process of inspecting, cleaning, transforming and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making.
Several data analysis techniques exist encompassing various domains such as business, science, social science, etc. with a variety of names. The major data analysis approaches are −
Data Mining is the analysis of large quantities of data to extract previously unknown, interesting patterns of data, unusual data and the dependencies. Note that the goal is the extraction of patterns and knowledge from large amounts of data and not the extraction of data itself.
Data mining analysis involves computer science methods at the intersection of the artificial intelligence, machine learning, statistics, and database systems.
The patterns obtained from data mining can be considered as a summary of the input data that can be used in further analysis or to obtain more accurate prediction results by a decision support system.
Business Intelligence techniques and tools are for acquisition and transformation of large amounts of unstructured business data to help identify, develop and create new strategic business opportunities.
The goal of business intelligence is to allow easy interpretation of large volumes of data to identify new opportunities. It helps in implementing an effective strategy based on insights that can provide businesses with a competitive market-advantage and long-term stability.
Statistics is the study of collection, analysis, interpretation, presentation, and organization of data.
In data analysis, two main statistical methodologies are used −
Descriptive statistics − In descriptive statistics, data from the entire population or a sample is summarized with numerical descriptors such as −
Mean, Standard Deviation for Continuous Data
Frequency, Percentage for Categorical Data
Inferential statistics − It uses patterns in the sample data to draw inferences about the represented population or accounting for randomness. These inferences can be −
answering yes/no questions about the data (hypothesis testing)
estimating numerical characteristics of the data (estimation)
describing associations within the data (correlation)
modeling relationships within the data (E.g. regression analysis)
Predictive Analytics use statistical models to analyze current and historical data for forecasting (predictions) about future or otherwise unknown events. In business, predictive analytics is used to identify risks and opportunities that aid in decision-making.
Text Analytics, also referred to as Text Mining or as Text Data Mining is the process of deriving high-quality information from text. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data using means such as statistical pattern learning, and finally evaluation and interpretation of the output.
Data Analysis is defined by the statistician John Tukey in 1961 as "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”
Thus, data analysis is a process for obtaining large, unstructured data from various sources and converting it into information that is useful for −
Microsoft Excel provides several means and ways to analyze and interpret data. The data can be from various sources. The data can be converted and formatted in several ways. It can be analyzed with the relevant Excel commands, functions and tools - encompassing Conditional Formatting, Ranges, Tables, Text functions, Date functions, Time functions, Financial functions, Subtotals, Quick Analysis, Formula Auditing, Inquire Tool, What-if Analysis, Solvers, Data Model, PowerPivot, PowerView, PowerMap, etc.
You will be learning these data analysis techniques with Excel as part of two parts −