Excel spreadsheets are quickly becoming obsolete thanks to the emergence of the latest data analytics tools and languages such as Python, Java, R, and Microsoft HDInsight. However, a large number of companies still use digital spreadsheets, creating a lot of problems for modern business data analysts. Analyzing data through excel is a poor choice because of reasons like errors in data validation, a poor shared workbook feature, no multi-user editing, inaccurate data, and safety concerns, making it necessary for you to switch to better and advanced alternatives. Even if your company prefers using spreadsheets, you can considerably improve your performance by learning a programming language for data analysis.

It is Difficult to Replace Excel

Tech-savvy businesses have started to realize the disadvantages of using spreadsheets. They are looking for more advanced and better alternatives for their modern data-analysts. However, there are many companies which are reluctant to embrace the change and continue to use Excel spreadsheets for data analysis. Some of these companies have massive infrastructures with ever-growing workloads, making it difficult to replace Office 365 or Excel. It is not impossible, but it will take many more years to come to pass.

It is also a fact that Excel continues to evolve. It is totally different from what it was just a decade ago. The latest version of Excel is more user-friendly and boasts some great functions and features for all kinds of organizations. For example, its computational model is extremely powerful yet simple. Above all, Excel offers some flexibility which other programs cannot, but Excel has its disadvantages too.

Some Common Problems with Microsoft Excel

More and more IT experts, consultants, and risk managers are pinpointing the problems in Microsoft Excel. To begin with, Excel spreadsheets are too fragile. Secondly, data sets are becoming bigger and bigger so that spreadsheets are becoming unwieldy. Some organizations actually have millions, if not billions, of spreadsheets, making data-analysis a true nightmare.

Furthermore, the amount of available data is growing at an enormous speed, making it necessary for organizations to make data processing more automated, more accessible, and error free. Some experts even believe organizations misuse Excel as a quick but temporary solution. They are also of the opinion that it was never a permanent, enterprise-grade solution in the first place.

It is difficult to maintain and manage millions of spreadsheets. Any information contained in these pages can easily fall into mischievous hands and be misused later. Therefore, data security is a big issue with Excel spreadsheets. It is especially dangerous to hold mission-critical data in Microsoft Excel.

Excel has significant limitations when it comes to data analysis. However, some companies are still stubbornly using Excel despite all of its disadvantages.

So, what can you do to survive in spreadsheet-driven organizations? If you are a tech-savvy data-analyst and have the will to master other more advanced programs, then you should try to learn Python.

Is There an Excel Alternative?

Python has garnered immense popularity among data-scientists in recent years. It is one of the best and most preferred tools in the world for data analysis. It is much more powerful than Excel, because it has different, better, and more effective functions.

People hesitate to use Python just because it is a programming language. However, it has quickly emerged as a viable replacement for Microsoft Excel. In fact, some organizations prefer candidates with Python proficiency. Now, you might ask why do we need Python when Excel can meet all of our requirements? In this regard, the following are some of the advantages of using Python over Excel.

1. Easy to Read

If you are a data scientist or even a common business user, you may find it very difficult to understand the spreadsheets prepared by others. It is an especially cumbersome task for someone who has never used Excel before. On the other hand, Python enables easy preparation, analysis, and visualization of data. It also involves universally understood code. Therefore, you can easily pick up from the point where the previous user left it. Python is also a brilliant resource for data visualization, calculations, and data cleansing. More importantly, you can also extract data from Excel using Python.

2. Easy to Automate Dull Tasks

When you are dealing with a huge amount of data, you ought to use Python. In fact, many data analysis teams require their members to have some knowledge of Python. It can handle dull tasks better than Excel. Similarly, it is much more effective in bringing automation to data analysis than any other tool.

3. Easy to Learn

Python is also very easy to learn. If you can write formulas in Excel, then you can write code in Python as well. As a matter of fact, more than 35% of data analysts in the world use Python because of the ease of learning. Python's syntax is very simple and easy to understand as compared to R and even Excel. The learning curve in Python is very small and you can start data-analysis within a week.

4. Versatile Data Visualization

Python comes with a variety of libraries that can be used for different types of data visualizations. For instance the Matplotlib library can be used for plotting static graphs, the plotly library can be used for drawing dynamic as well as geographical plots, and, finally, the pandas library can be used to plot time series plots.

5. Very Flexible

Python is a more flexible tool compared to Microsoft Excel. For example, there is a plethora of Python libraries available for data-analysts. Some of these libraries such as Matplotlib, Pandas, and Numby are really helpful for all kinds of data analysis. However, you should learn Python's basics before looking at these libraries.

6. Free Software License

In fact, you do not need to spend a penny on learning Python. It runs on both Linux and Windows and it is easily portable as well. Similarly, open source Python libraries for data visualization and data manipulation make it a must-have tool for organizations that still rely on spreadsheets for data analysis and expense tracking.

7. New Career Opportunities

The popularity of Python is growing at an exponential rate. You can considerably improve your chances to accelerate your career growth within your organization by mastering Python. If you possess a unique skill in the company, while everybody else is using decades-old Excel, you are guaranteed to get a reward or even promotion. Similarly, learning a growing skill means you will be able to get another job very quickly if you ever decide to resign from your current position.

Conclusion

Excel is one of the most popular tools for data analysis in the world. It may not be perfect, but it is not going anywhere any time soon. However, it is expected of you to get better with data-analysis in the modern business environment. You can only accomplish this goal by learning coding languages like Python. It is a more powerful, easier, and better data-analysis platform than Excel and it will take over the industry sooner than later. Therefore, the current data-analysis environment requires individuals to move toward Python because it is the only way to survive in the industry.

Are you a data analyst who wants to learn more about the use of Python for data analysis? Take a look at this course on Python Basics. It covers all the basic concepts that you need to learn to be an industry-ready and successful Python data analyst!