Data Analysis with Excel: A Complete Guide for Beginners
Microsoft Excel is a spreadsheet program that joined the Microsoft family in 1985. Microsoft engineered it based on earlier spreadsheet programs. Excel was first available for the Apple Mac and then available on Windows by 1987.
Excel is a powerful, accessible program that has changed how analysts and organizations approach data analysis across all industries. Since its inception, Excel has remained the most popular and widely used tool for data analysis.
The program can handle both basic and very complex operations for data analysis. Analysts frequently use it for many other tasks such as financial modeling, business planning, people management, and strategic analysis across many industries and use cases.
Having a solid working knowledge of Excel only adds to your skillset. There is such a wide range of things that users can accomplish with it. Its functions, visualizations, and arrays allow the user to analyze data quickly and accurately without repetitive work.
Why learn Excel for data analysis?
There are very few professions that will not benefit from using the tools found in Excel. Teachers can use it to compare student grades, businesses can use it to track sales and losses, and everyone else can use it to analyze data and track trends. In fact, 80% of employment opportunities that require digital skills include proficiency in Excel as a requirement, making it a crucial skill in today’s work environment.
Using Excel is more than just making tables and inputting basic data. Plugging in data to a table or cells is a start, but it can do so much more. Excel can organize data, complete mathematical calculations, turn complex data into easy-to-understand graphics and charts, build forecasts, and help provide answers to the type of complex questions.
Efficiency is part of what makes learning Excel important. Its many features and functions help users save time and ensure accuracy. It takes the old school methods of handwritten work and transforms it into automatic analysis through technology. There is no more tedious, repetitive work. Once we add the proper formula, we input data into the worksheet and the answer appears.
Learning Excel will benefit nearly everyone professionally. It is a transferrable, hard skill that will show a hiring manager you can do data analysis quickly and efficiently. Knowing Excel can offer expanded career opportunities and increased salary.
Why use Excel for data analysis?
Data analysis, also known as data analytics, is a process a person can use to clean, introspect, change, and model data. The goals of discovering and deciphering novel information, coming up with solid conclusions, editing, and formatting data properly, and validating a particular decision-making process, are all easily accomplished through data analysis.
There are many facets to conducting proper data analysis. And with Excel, analysts can accomplish all these with ease. Here are just some diverse features that Excel offers for data analysis:
Excel can sort data quickly, whether for one or more factors. Users can sort data in descending or ascending order based on the user’s needs. Users can view lists in alphabetical order, for example, or have numerical data rank from lowest to highest or vice versa.
Along with sorting, filtering is another type of data analysis that can be performed in Excel. Users can filter data in Excel into various subsets, viewing the data points that fit their chosen criteria. This is an outstanding feature to narrow down data, have it meet specific criteria, or view a limited set of cells. This is accomplished all while the original data remains intact. The visible data is adjusted depending on the filter the user applies. It is a practical way to explore a dataset.
This is another easy-to-use feature of Excel. Conditional formatting allows the user to highlight cells in a particular color based on the cell’s value and the user’s criteria. It is an excellent way to highlight data visually or to spot outliers and trends in data.
While Excel can easily manipulate data sets, it can also provide charts to simplify data analysis conclusions. Designing charts in Excel is user friendly and makes data easy to understand through color, simple presentation, and adaptability. Excel charting is a simple and easy way to help others understand insights drawn from your data analysis. Excel charts are ideal for presenting key insights from data analysis during meetings and presentations.
The table command lets the user convert data into a formatted table, which users can then sort and filter for easy organization and viewing.
This interactive Excel feature is a powerful tool for manipulating and analyzing large structured data sets. Pivot tables can take important content from large, detailed data sets and make the information more concise. It summarizes, sorts, reorganizes, groups, counts, totals, or averages the data in a spreadsheet. It gives the user the ability to analyze large data sets in Excel easily, even with hundreds of millions of data points.
Excel provides tools for users to run simulations of hypothetical scenarios to observe the impact an independent variable has on a dependent variable. For example, “What would sales look like if the company open two additional retail locations next year?” Excel has three types of What-If Analysis tools: Scenarios, Goal Seek, and Data Tables. Scenarios will allow you to consider different variables. Goal Seek will help you achieve your desired result, and Data Tables will show the effect of one or two variables on a specific formula. All allow the user to manipulate the data and run simulation analysis of hypothetical scenarios.
This is a Microsoft add-in program that you can also use for What-If analyses. It can find optimal (both minimum and maximum) value for an objective cell (one with a formula in it) based on your chosen constraints or limitations. Solver finds the best answers for decision problems, making the data more usable. For example, a business can use solver to find the optimal product mix to carry on its eCommerce store based on the different demand and profit margins for each product.
This is another add-in program for Excel you can add to help with complex statistical, financial, and engineering analyses. It includes statistical tests such as ANOVA, t-Test, z-Test, as well as tools for regression, sampling, and Fourier analysis designed to solve problems in linear systems.
Many features of Excel have made using it easy and logical. Descriptive statistics is one of the Toolpak add-ins found in Excel that allow the user to avoid manually running individual statistical analysis for commonly used statistics, such as mean, mode, and standard deviations. So with just one click of a button, the user will get statistical information for their data set immediately.
Additional Excel Add-ins
Although Excel has many built-in features for data analysis, it also provides many third-party add-on tools to enhance functionality and efficiency when doing data analysis. Aside from Solver and the Analysis Toolpak, other popular add-ons include Data Explorer, a tool to enhance data access and discovery; Power Pivot, that can turn Excel into a powerful business intelligence tool; Random Generator, a tool ideal for creating dummy data for mockups and testing; and Quandl, a tool ideal for running financial time-series from multiple data sources, just to name a few.
Excel from beginner to advanced
Whether you are just starting to learn or are a master at Excel, there are lots of courses to teach both the basic and more complex features of the software. Whatever type of Excel education you are looking for, do your due diligence and choose one that is thorough and will meet all your data analysis needs. Whether you are working with an older version of Excel, such as Excel 2010, or the newest 365 version released in 2019, they all work with the same basic premise and functions.
Although there are many options for learning Microsoft Excel, choosing the right instructor can make all the difference.
When choosing a course, make sure:
- The instructor or institution offering the course(s) is credible. Look at the instructor’s background and experience. Are they pure educators? Or are they also industry practitioners?
- The curriculum is comprehensive. Does the course cover enough of the important topics? Does the course offer a suitable amount of practice? Does the course offer real-world case studies so you’re learning not just about Excel, but also how to apply your lessons in real-world settings?
- The course is designed for your skill level. Find a progressive program. If you are just beginning to use Excel, then see if there are courses that can help you progress to the intermediate and advanced levels. Make sure it is well suited for both your current skill level and your intended goals.
Udemy’s marketplace offers the largest selection of high-quality Excel and data analysis courses that cover a wide range of approaches to the topic and are taught by instructors from unique backgrounds. You are certain to find one suited to your needs and goals.
Data analysis is an integral part of a business, and knowing how to use it flexibly when your job requires it is important. Data analysis using Excel can be the perfect combination to do this. Knowing your way around Excel and feeling at ease performing data analysis while using it will benefit your work and your career. And while getting started is easy, mastering Excel for data analysis will take time. But it can open doors at any age or stage of your career. Whether you are just starting out or have been in a particular field of work for years, it is never too late to learn or upgrade your Excel skills, especially for data analysis.
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