Data Analysis with Excel is a comprehensive course that gives you a good understanding of Microsoft Excel’s latest and most powerful capabilities. It teaches in detail how to use MS features Excel to execute various data analysis procedures. The guide includes a lot of screenshots that show you how to utilize each function step by step.
This course is for anyone who uses MS-Excel to create charts, tables, and professional reports with complex data. It will benefit all readers who use MS-Excel to examine data on a regular basis.
The readers of this article should be familiar with the basic functions of Microsoft Excel.
Data Analytics Using Excel
Data analysis is the act of analyzing, cleansing, manipulating, and modeling data in order to find usable information, draw conclusions, and aid decision-making. Companies would always prefer good data science knowledge in upcoming years. If you already know data analysis using excel and study further at an advanced level then you can enroll in a business analysis course by Great Learning.
There are a variety of data analysis approaches that cover a wide range of areas, including business, science, and social science and go by a variety of names. Here we will discuss the most common data analysis methods are:
- Data Mining
- Business Intelligence
- Statistical Analysis
- Predictive Analytics
- Text Analytics
Data Mining:
Data mining is the system of analyzing considerable quantities of statistics with a view to discover previously unknown patterns, anomalous statistics, and dependencies. It’s essential to recall that the motive is to extract styles and insights from tremendous amounts of statistics, now not to extract data itself.
Computer technological know-how techniques at the intersection of synthetic intelligence, system studying, records, and database structures are utilized in facts mining analysis.
Data mining patterns can be thought of as a summary of the input data that can be used for further analysis or to help a decision support system provide more accurate prediction results.
Business Intelligence:
Business intelligence techniques and tools are used to help find, develop, and create new strategic corporate possibilities by acquiring and transforming massive amounts of unstructured business data.
The purpose of business intelligence is to make it simple to comprehend big amounts of data in order to find new opportunities. It assists in the implementation of an effective strategy based on insights that can give organizations a competitive market advantage as well as long-term stability.
Statistical Analysis:
Statistics is all about the study of data collection, analysis, interpretation, presentation, and organization is known as statistics.
For data analysis here we used two statistical approaches:
1) Descriptive Statistics: Data from the full population or a sample is summarised with numerical descriptors such as in descriptive statistics.
- Mean, Standard Deviation for Continuous Data
- Frequency, Percentage for Categorical Data
2) Inferential statistics: It makes inferences about the represented population or accounts for randomness using patterns in the sample data. These implications can be interpreted in some other ways.
- Hypothesis Testing: answering yes/no questions about the data.
- Estimation: estimating numerical characteristics of the data.
- Correlation: describing associations within the data.
- Regression Analysis: modeling relationships within the data.
Predictive Analytics: Predictive analytics analyses current and historical data to make predictions about future or otherwise unknown events using statistical models. In enterprise, predictive analytics is used to discover dangers and opportunities to aid decision-making.
Text Analytics: The approach of extracting amazing records from text is known as textual content analytics, occasionally referred to as text mining or textual content data mining. Text mining often involves structuring the input text, discovering patterns within the structured data using techniques like statistical pattern learning, and then analyzing and interpreting the results.
Data Analysis Process: According to statistician John Tukey in 1961, the statistics analysis manner includes “techniques for analyzing facts, techniques for decoding the results of such approaches, ways of planning the collection of information to make its evaluation easier, greater specific, or accurate, and all of the machinery and outcomes of (mathematical) statistics which apply to reading facts.”
As a result, statistics evaluation is the technique of acquiring massive amounts of unstructured data from a number of sources and remodeling it into statistics that can be used for lots of purposes.
- Answering the questions
- Test hypotheses
- Decision-making
- Disproving theories
Data Analysis with Excel: Microsoft Excel offers a variety of tools for analyzing and interpreting data. The information can come from many of places. The data can be formatted and converted in a variety of ways. 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, and other Excel commands, functions, and tools can be used to analyze it.
These Excel data analysis techniques will be taught in two sections.
- Data Analysis using Excel
- Advanced-Data Analysis using Excel
Data analysis is the process of gathering, converting, cleaning, and modeling data in order to get the information needed. The results are shared, with conclusions suggested and decision-making aided. Data visualization is sometimes used to represent data in order to make finding relevant patterns in the data easier. Data Modelling and Data Analysis are interchangeable words.
Here we will discuss the iterative Data Analysis Process :
- Data Requirements Specifications.
- Data Gathering and Processing
- Cleaning of data
- Communication of Data Analysis
Data Requirements Specification: The information needed for analysis comes from a question or an experiment. The data required as inputs to the analysis are identified based on the requirements of those guiding the analysis (e.g., Population of people). Specific population factors (such as age and income) can be supplied and obtained. The information can be numerical or categorized.
Data Gathering/Collection: The process of acquiring information on specific variables designated as data requirements is known as data collection. The emphasis is on ensuring that data is collected accurately and honestly. Data collection guarantees that the information acquired is correct, allowing for valid judgments to be made. Data collection gives a baseline against which to measure progress as well as a goal to strive for.
Data is gathered from a variety of sources, including organizational databases and web page information. The resulting data could be unstructured and contain irrelevant information. As a result, the obtained data must undergo Data Processing and Data Cleaning.
Data Processing: For analysis, the obtained data must be processed or structured. This includes reorganizing the data to meet the needs of the various Analysis Tools. For example, in a Spreadsheet or Statistical Application, the data may need to be organized into rows and columns in a table. It’s possible that a Data Model will be required.
Data Cleaning: The data that has been processed and arranged may be incomplete, duplicated, or contain errors. The act of preventing and correcting these problems is known as data cleaning. Depending on the nature of data, there are different forms of data cleaning. Certain totals might be evaluated against reliable published amounts or established thresholds, for example, while cleansing financial data. Quantitative data methods can also be used to find outliers that will be eliminated from further investigation.
Data Analysis: The data would be ready for analysis once it had been processed, organized, and cleansed. To analyze, evaluate, and develop conclusions based on the criteria, a variety of data analysis using excel techniques are available. Data visualization can also be used to evaluate data in a graphical style in order to get greater insight into the data’s messages.
To find the relationships among the data variables, statistical data models such as correlation and regression analysis can be utilized. These data-descriptive models aid in the simplification of analysis and communication of outcomes.
Additional Data Cleaning or Data Collection may be required as part of the process, therefore these activities are iterative in nature.
Communication: The records analysis consequences have to be provided in a format that the customers require with a view to assist their decisions and moves. Additional evaluation may be required because of consumer feedback. Data analysts can use records visualization tools like tables and charts to assist humans recognize the message greater honestly and correctly.. The analysis tools allow you to highlight important information in tables and charts using color codes and style.
