Let me provide you with a brief update on the Excel Analytics OS course. I've been receiving inquiries about the Certificate of Completion, and I'm pleased to share that I've crafted a special certificate for you, which will be sent to you once you've successfully completed 100% of the course. Keep up the excellent work as you strive to achieve your goals!

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Today, I'll discuss the critical importance of data set format in the business environment.

Why Data Set Format is Crucial in Business Analytics?

From my extensive experience, I've observed that many individuals invest time in learning Excel functions and features, often overlooking the fundamental aspect of analytics - data set format.

This oversight can limit their progress, even if they possess decent Excel skills.

Data set is the flour. With the right format of data, you have the potential to craft a wide variety of insights and outcomes. Conversely, if you start with a specific "Bread" dataset, transforming it into other "Bread " form can be a challenging task. Let alone performing analytics.

I categorize data sets into three main types:

• 1. Data List:

  • Description: This is raw data in its purest form.
  • Format: Headers are positioned at the top, and all data fields and values align vertically beneath their respective headers.

 2. x/y Table:

• • Description: This is a semi-processed dataset commonly found in business reports.
  • Format: Unlike a list format, an x/y table uses the y-axis to represent the first field and the x-axis for the second field, with values intersecting in the middle. In simple terms, it resembles a PivotTable.

• 3. Unorganized Data:

  • Description: Any data set that doesn't fit the "Data List" or "x/y Table" criteria falls into this category.

Beginners, use "Data List" format. Here's why:

1. Ease of Use: Working with a data list requires relatively fewer Excel skills.

2. Flexibility: Data lists are like flour; they are highly adaptable and can be transformed into various forms and formats to meet specific business needs.

3. Direct Analysis: Data lists allow you to create PivotTables and charts directly for quick insights.

Advanced users, consider adding "x/y tables" to your toolkit. But you should have:

1. Advanced Skills: Manipulating x/y tables demands a higher level of Excel proficiency.

2. Function Familiarity: To work with this type of table, you should be well-versed in Excel functions, ideally around 70 for business applications. This knowledge will enable you to extract and analyze numbers for further analysis or assembling new reports.

You should never use unorganized data as your source:

1. Ambiguity: Unorganized data lacks structure, making it challenging to understand and work with effectively.

2. Error-Prone: Due to its chaotic nature, unorganized data often leads to errors in analysis and reporting.

3. Inefficiency: Using unorganized data can be incredibly time-consuming, as it necessitates extensive cleaning and preparation before analysis.

In summary, understanding how to classify these three types of data set formats is crucial for clarity and avoiding data-related issues in your analytics projects.

In upcoming newsletters, I will delve deeper into which functions and features to use and what you should learn to enhance your analytical skills. Stay tuned, and I hope you find these insights valuable.

For those eager to learn more in advance, check out my full curriculum on the Excel Analytics OS course.

If you have any questions, just send me an email. I'm open to answer you.

That's all for this week.

See you next week. 

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Whenever you're ready, this is the way I can help you:

• Digital course: Excel Analytics OS for business professionals

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