How Excel is used in Real-World Data Analysis

Introduction

The definition of Microsoft excel according to a simple google search is " A powerful electronic spreadsheet application developed by Microsoft, used to organize, analyze, and store data in rows and columns." I have used it to organize, analyze, and visualize all sorts of data in my science career. excel shines because everyone knows how to use it , it requires no coding skills, and is quicker in data analysis compared to other data analysis software ( may be because it is 'lighter' for the processor) coupled with the fact that it requires no dependencies, environments and deployments.

How is it used in real-world scenarios

Excel is applied in a myriad of sectors to support in decision making.

• In health care public health professionals are able to collect and store data from health facilities and public agencies using excel. Analysis of the data can estimate the number of people showing up in hospitals with particular infections or cancers and they can use that data to guide them on prioritization during allocation of resources.

• In banks and financial institutions operations, excel is used for budgeting, financial modeling, forecasting, and risk analysis. The business development team uses excel in account opening tracking, performance dashboards, and market analysis.

Features I Have Learned and Their Applications

Data cleaning: This is the first process for any data set. Here we try to understand our data set by checking the type of data each column/cell has. we then align the data type to the operation we want to do with the data. for example if the format of a cell with a number is not appropriate calculations involving that cell will bring erroneous value. By right clicking and changing the format of the cell or column. this is corrected.

NB/: A column with more than 60% blanks should be completely excluded from the dataset.

Formulas & Functions (e.g., SUM IF, VLOOKUP/XLOOKUP): Used to automate calculations, categorize data, and retrieve information efficiently.

Consider the image below to illustrate the features I am going to explain:

• I would use sumifto get the total value of sales before a particular date. =SUMIF(A2:A10, "Apple", B2:B10) sums values in
  if corresponding cells in B2 to B10 is equal "Apple".

• Countif can be used to count cells that meet a single specific criterion e.g =COUNTIF(A2:A10, "Apple") counts how many cells in the range A2:A10 contain "Apple". this should return a value of 3 in the cell you create the formula

• Vlookupand Xlookupto find a specific value in a column or whole data set. this is particularly helpful when handling blanks in a dataset.=VLOOKUP("Apple", A2:B9, 2, FALSE) would bring up 100

• CONCAT is used to combine text from multiple cells. =CONCAT(A1, " ", B1) will combine a name in cell A1 and cell A2 leaving a space between the words. i.e Apple 100

• MAX/MIN: Returns the highest/lowest value. =MAX(B2:B9) 200

Pivot Tables: can be used to summarize datasets without writing formulas. It is crucial when identifying trends. see example below:

Personal experience

Before my training, I viewed datasets as rigid structures with very limited possibilities for manipulation. Now, I understand that data is flexible. Even blank cells can be meaningfully handled and included in analysis. I’ve also learned how to create automated formulas that reduce workload and deepen my understanding as I analyze data.

Most importantly, Excel has been a stepping stone into data science, helping me build a strong foundation in working with structured data and translating it into meaningful insights.