Almost every Nigerian is familiar with Excel but the ultimate question is what exactly can you do with MICROSOFT EXCEL?

Microsoft Excel is one of the most widely used tools in any industry but a lot of individuals limit themselves to the Data Entry aspect. The ability to analyze data is a powerful skill that helps you make better decisions and in this era of digital transformation, there’s a lot of data to work with.

The major steps to analyzing Data starts with understanding some Microsoft Excel functions:

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• The Find & Replace
• Using Formulas To Clean Data
• Finding Errors with Go to Special Constants
• Finding Blank Cells In Excel With A Color
• Removing Duplicates in an Excel Table
• Using Formulas To Clean Data
• Text To Columns: Dates

Microsoft Excel is basically the foundation for anyone who wants to kick start in the Data Analytical space and the built-in pivot tables are arguably the most popular analytic tool.

There are over 650 Million people using Excel around the globe and that’s because Excel is the foundation of all data insight, from storage to cleaning and visualization.

It’s safe to say, Big Data is now a part of our daily lives as businesses get a chunk of data for better decision making and documentation.

For instance, an organization’s server contains a lot of information, ranging from customer portfolio, products, partners and more. This data has to be organized and best still, used to take note of patterns for forecasting purposes. What happens here is, the company needs an excellent tool to keep this data clean without errors, duplicates and more as the worksheets get updated regularly.

Making use of Microsoft Excel can accelerate the way this data is organized.

The first step to Data Analytics with Excel is:

Data Importation: To help you work smoothly, the Data you desire to work on should be transferred to an Excel spreadsheet. Now this will let you note if the data is structured or not in such that the information gotten must have been from other storage sources which might not fit with Excel. This happens all the time where some details become unclear in the Excel spreadsheet.

This is where Data cleansing comes in.

There are four steps to take note of in the process of Data Analytics:

Descriptive analytics answers the “what happened” by summarizing past data usually in the form of dashboards. e.g Monthly revenue reports.

Diagnostic Analysis After asking the main question of “what happened” you may then want to dive deeper and ask why did it happen?

It creates more connections between data and identifies patterns of behaviour. Example - Identifying which marketing activity increased sales

The predictive analysis attempts to answer the question “what is likely to happen” This type of analytics utilizes previous data to make predictions about future outcomes. This analysis relies on statistical modelling and forecasting. e.g. Sales Forecasting.
 

Prescriptive analytics is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision. Eg. AI, Machine Learning

All these and a lot more form part of the valuable information to learn on your journey to becoming a Data Analyst anywhere in the world. Data Analysis is now part of our lives and to learn more about the future of work, you can join this free Masterclass.