Do data analysts use Excel? – Data Science Training Pune

We have been using excel spreadsheets for decades. They are still one of the best ways to manipulate and review data. Excel yields results faster than other options. It has lots of functions and features that can be used by data analysts to pivot, aggregate, clean, and graph data.

Microsoft Excel is an important tool in data analytics. This is an application you may have already used during work. For data analysts, the spreadsheet program needs to be mastered. There are different ways in which Excel is used by analysts. You should understand the formulas in the program if you want to use them well. The formulas operate based on values found in a cell(s). This produces information that is quite specific, like the lowest value of the cells selected or the sum of the cells. Understanding the formulas makes things easier for a data analyst, and so, yes, data analysts use Excel.

Some of the formulas and features that are useful to data analysts include:

CONCATENATE

This is one of the most important formulas that allow the combination of different cell values into one. It is used for data analysts and usually combines numbers, dates, and text. It can also combine URL components or address lines, among others.

VLOOKUP

This is another familiar formula for those who have had the chance to work with Excel. This formula helps to look up data arranged in vertical columns. In case you are making a presentation of profits earned per month, you can use VLOOKUP for this starting from the month you pick.

LEN

In data analysis, LEN displays character numbers in specific cells. It can be used to create text that gives character limits or when identifying product number differences.

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SUMIFS

This function is important in data analytics. It is a formula that helps add cell values meeting the selected number. You can be very specific about what you want to be added, like numbers within a cell with a value higher than five.

Network days/days

Here, =days determine the days between two specified calendar dates. This is a good way to determine contract periods or the life cycles of products. =networkdays is also useful and calculates the working days that are in between 2 dates.

SUBSTITUTE

This function is good when you want to update many cells simultaneously. It substitutes content, and it is a great choice when you want to update your URLs or remove any spaces that are unintentional and have spelling errors. It is a handy tool in data analytics.

MINIFS/MAXIFS

This formula helps to identify the lowest and highest values. Apart from that, it sorts out the values according to set criteria. It can be used to sort out the youngest and oldest ages from samples of women and men. In such a data set, the values may be displayed in terms of gender as well.

TRIM

It eliminates any unwanted characters and spaces from text. In data analysis, when you want to work with any data, it is important to clean it first. =TRIM helps and is a handy formula for data analysts.

COUNTIFS

This formula counts the number of times a value appears based on set criteria. The number is sent to the COUNTIFS cell when a criterion is met.

LEFT and RIGHT formulas

This is a good way of getting data from static cells. =LEFT usually returns the desired character numbers from the cells’ beginning. The RIGHT is the opposite of that. If you want area codes from phone number lists, this formula can be very useful.

Pivot charts and pivot tables

This is a feature we cannot ignore. The pivot character is used to visualize the data that a pivot table expresses. This helps us get some insight with just one glance. Pivot tables are a good approach that can be used to reformat rows and columns, transforming them into summaries, statistics, and groupings. Charts can be created along with the table by making use of the of the feature that is found under insert. The pivot chart and table need to be populated. Additional filters and dimensions can be added by dragging some fields into the boxes that correspond. It takes some clicks to aggregate your data and then use Excel to visualize it.

Conditional formatting

This is another feature that is useful for data analysts. Conditional formatting makes it easy to hide or highlight cells based on the specified rules. The rules can be applied to multiple or single cells within a worksheet. You can highlight patterns, duplicates, or outliers, which helps you understand data better. It is easy to edit and create new rules. You can also add multiple rules to a spreadsheet as required.

Remove duplicates

Some data sets are messy, and it helps when you can remove any duplicates for better outcomes. With conditional formatting, duplicates can be highlighted for review and deletion. The remove duplicate feature is found under data tools.

IFERROR

This function creates custom error messages in case formula results in an error. It can be used to wrap the XLOOKUP function to give a clear message when an ID is not found. This is a relatively simple syntax.

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MATCH

This function is quite similar to lookup functions in that it can be used when you want the value position within a range instead of the particular value. In this case, you need to know the match type that should be used. It has defaults with the available options being -1, 0, or 1. -1 can find the smallest value, which is equal to or greater than the LOOKUP value. 0 finds the 1st value equal to the lookup value. 1 finds the largest value that is equal to or less than the lookup value.

Conclusion

The list above is not exhaustive of the functions offered by Excel that data analysts can find useful. These functions and features help in data cleaning and analysis without loading data to an SQL server or using Python. The value that Excel brings to the table cannot be ignored, which is why it is still one of the most widely used tools for data analytics. Data analysts need to know Excel regardless of the role held. The spreadsheet can help save time.

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