Excel as we know is a spreadsheet tool and over the years, it has transformed into a crucial tool in data analysis. Although there are many more specialized programs for use in data science such as Python, R, SQL, and even Google Spread Sheets, Excel is one of the most effective and simple tools that can be used even by novices and beginners looking for quick answers to their queries.
In this article, I will be focusing on some tips, tricks, and best practices you need to know while using Excel for aspiring data scientists so that you can work with big data, analyze data with more precision, and present your results better.
Data cleaning is a crucial step in data science, and Excel offers several features to help tidy up messy data:
Remove Duplicates: Sometimes it is as basic as removing duplication in your data set – a step that helps to add rigor to your data. This can be easily done by just selecting your dataset and clicking on Tools-Data Remove Duplicates.
Find and Replace: It can also help find a particular error, a missing value, or the data that requires correction. For example, you can easily delete all the occurrences of a wrong spelling of a word and insert the right one.
Text to Columns: This tool is important in dividing data into individual columns for analysis and the results are especially important since most data is entered in columns from a CSV file.
Data Validation: This can be used to define what can or cannot be entered into a cell to minimize the problems associated with data entry.
Data sorting and filtering are among the most frequently used features in Excel for data science.
Sort Function: The “Sort” option to make adjustments that allows your data to be arranged in an order, either from the largest down to the smallest values or arranged in a Alphabetically, depending on what you would prefer. This is even more useful when comparing the data or when one is trying to decide if a figure is an outlier or not.
Filter Option: Criterians assist in removing the unwanted data or ultimately leaving with the relevant data that you might want to work with for instance dates, values, or classes. You can further include filters so that you can view the data from different angles.
Conditional Formatting: This is a tool of visual which enables you to use color scales icons, or data bars to highlight patterns in a very short time. Here, you can conditionally format the cells to color those that only contain values that are greater or lesser than a predetermined value.
Numerous functions in Excel can compute highly complicated formulas and even process data.
Here are a few that are particularly useful in data science:
VLOOKUP and HLOOKUP: These functions assist you in easily finding data and then referring to it in the other sheets or tables. They are most useful in joining tables or in searching for a particular record in massive tuples.
INDEX and MATCH: Easier to manage than Vlookup, these functions enable proactive lookup and can enable one to extract information based on several criteria.
IF Statements: It is particularly useful for this conditional function to give a computed value, which is dependent on the logic implemented. To mention but a few, you can develop a rule that categorizes your data based on certain parameters of your choice.
SUMIF and COUNTIF: These functions enable one to sum or count, values that may meet a particular condition and can, therefore, be very helpful when it comes to summarizing the data.
The first one is what I have come to refer to as the Data Science Workhorse, a term that is quite explanatory in the context of what we need to do in data science.
Pivot tables are one of the most valuable functions for analyzing data in Excel which is described below. They help in sorting capabilities, transformation, and providing summaries and overviews, at a fast glance with additional options available with a simple click.
Here’s how to get the most out of pivot tables:
Grouping Data: In a way, it is possible to categorize data by categories, dates, or ranges and this will enhance the dataset. For instance, when categorizing sales data, it is quite possible to categorize it by month quarter, or semester to check for cyclical trends.
Summarizing Data: Pivot tables provide functionality for a summary of data with the utilization of arithmetic operations like mean, summing, or counting data besides offering a percentage by which the data was encapsulated.
Slicing Data: When presenting your pivot table data, use the “Slicer” tool to filter your data in real-time, that is, paint the different aspects of your data in and out to illustrate them.
After the analysis of data, data presentation is very crucial especially to disseminate your findings to the rest of the people.
Excel offers several tools for creating charts and graphs:
Charts and Graphs: You need to make use of line graphs, bar graphs, pie graphs, and scatter graphs in the presentation of your data. Make sure that you select the most suitable type of chart depending on the information and the information you want to get across.
Pivot Charts: These are useful charts that can be used with pivot tables meaning that it is possible to analyze your data in different ways.
Sparkline Charts: These are trend charts that are minuscule and only occupy one cell in the chart sheet; they offer a quicker way of viewing trends within a dataset. They’re especially useful when you are using small reports For instance, In compact reports they are particularly helpful.
Best Practice: Don’t put too much into your charts; otherwise, things will start to get overwhelmed. As a last tip, make sure that you stick to the key facts or indicators that you would want to present and use appropriate colors as well as labels to enhance the comprehension of the visuals.
If you find yourself performing repetitive tasks, Excel macros can save you time by automating these processes: If you find yourself performing repetitive tasks, Excel macros can save you time by automating these processes:
Recording Macros: Excel has recording capability you could record a set of operations and then play it over and over again if the need arises. This is especially beneficial during the process of working with different datasets where one could run the same operation or apply formatting.
Visual Basic for Applications (VBA): For further automation, you can use functions that are written with VBA, or Visual Basic for Applications. VBA enables you to provide customized solutions to the data-related flows which helps a lot in making the solutions more efficient.
Backup Your Data: It is prudent to back up your data before modifying it in a big way. It also enables you to have your data intact in the event of any mishap or confusion regarding the working of the program.
Organize Your Data: Make it a point to structure your data well; for example, use different sheets for data, computations, and outcomes. It not only makes your workbook easy to locate information but also minimizes the mistakes that might have been made.
Use Named Ranges: So, instead of using the row-column notation to refer to the cells, noting their position in the sheet, for example, the letters A1, B2, C3, and so on are used. g. , A1
You should also note that using named ranges will also help you when it comes to formula writing since they will make it easier for you to manage the formulas involved in your work.
Document Your Process: Document your formulas and macros by placing comments and notes that would describe the action of the task. This is particularly useful when you are working with other people or come back to the data at a later date.
Companies continue to use Excel for data science tasks due to convenience when used by newcomers or, in general, when the analyst wants a quick solution. With this understanding and a working knowledge of macros, Excel can be used to the full to aid in data cleaning, data analysis, data visualization, and automation. If you follow these recommendations and guidelines, you will be ready to perform a host of data analysis activities using Excel’s interface.