Gerhard  Brink

Gerhard Brink

1624271996

Microsoft Excel is Still Relevant in the Age of Data Analysis

In the age of data analysis, Microsoft Excel is still necessary

In recent years, leading companies and organizations are focusing more on content. Businesses are trying to deliver products, services, and content according to the preferences of the customers. But even though the focus is on the final product, the key to achieving that- is the data. Data analysis of the vast amounts of information received daily provides businesses the insights required to understand the market demands and serve the customers accordingly.

Data analysis uses advanced analytics tools like Hadoop, Apache Storm, and DataCleaner. The analytics technology is closely connected to the applications, which manage, analyze, and store the data. One such program, which often goes unnoticed when it comes to the analysis of data, is Microsoft Excel.

Microsoft excel is still relevant in the age of data analysis and advanced technologies. Data scientists who use Excel to store the information are well aware that it is indispensable and is an effective tool.

#big data #latest news #data analysis #microsoft excel is still relevant in the age of data analysis #microsoft excel #data analysis

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Microsoft Excel is Still Relevant in the Age of Data Analysis
Gerhard  Brink

Gerhard Brink

1624271996

Microsoft Excel is Still Relevant in the Age of Data Analysis

In the age of data analysis, Microsoft Excel is still necessary

In recent years, leading companies and organizations are focusing more on content. Businesses are trying to deliver products, services, and content according to the preferences of the customers. But even though the focus is on the final product, the key to achieving that- is the data. Data analysis of the vast amounts of information received daily provides businesses the insights required to understand the market demands and serve the customers accordingly.

Data analysis uses advanced analytics tools like Hadoop, Apache Storm, and DataCleaner. The analytics technology is closely connected to the applications, which manage, analyze, and store the data. One such program, which often goes unnoticed when it comes to the analysis of data, is Microsoft Excel.

Microsoft excel is still relevant in the age of data analysis and advanced technologies. Data scientists who use Excel to store the information are well aware that it is indispensable and is an effective tool.

#big data #latest news #data analysis #microsoft excel is still relevant in the age of data analysis #microsoft excel #data analysis

Gerhard  Brink

Gerhard Brink

1622622360

Data Validation in Excel

Data Validation in Excel

In this tutorial, let’s discuss what data validation is and how it can be implemented in MS-Excel. Let’s start!!!

What Is Data Validation in Excel?

Data Validation is one of the features in MS-Excel which helps in maintaining the consistency of the data in the spreadsheet. It controls the type of data that can enter in the data validated cells.

Data Validation in MS Excel

Now, let’s have a look at how data validation works and how to implement it in the worksheet:

To apply data validation for the cells, then follow the steps.

1: Choose to which all cells the validation of data should work.

2: Click on the DATA tab.

3: Go to the Data Validation option.

4: Choose the drop down option in it and click on the Data Validation.

data validation in Excel

Once you click on the data validation menu from the ribbon, a box appears with the list of data validation criteria, Input message and error message.

Let’s first understand, what is an input message and error message?

Once, the user clicks the cell, the input message appears in a small box near the cell.

If the user violates the condition of that particular cell, then the error message pops up in a box in the spreadsheet.

The advantage of both the messages is that the input and as well as the error message guide the user about how to fill the cells. Both the messages are customizable also.

Let us have a look at how to set it up and how it works with a sample

#ms excel tutorials #circle invalid data in excel #clear validation circles in excel #custom data validation in excel #data validation in excel #limitation in data validation in excel #setting up error message in excel #setting up input message in excel #troubleshooting formulas in excel #validate data in excel

Top Microsoft big data solutions Companies | Best Microsoft big data Developers

An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.

#microsoft big data solutions development companies #microsoft big data analytics and solution #microsoft big data consultants #microsoft big data developers #microsoft big data #microsoft big data solution providers

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1624272463

How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

  • Building/collecting data
  • Cleaning/filtering data
  • Organizing data

#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different