For most analysts, you start with Excel then advance into Python and other languages. In the business world, Microsoft Excel is one of the most important programs, especially when it comes to collecting data. You can use it for data analysis, but there are challenges you might experience, which necessitates the move to Python programming for data analysis.
While Excel is a great tool, it has some unique challenges that you can overcome by learning Python. A bit of Python programming could really change your life and make data analysis easier for you in data science.
● Expert data handling
One of the first things you will enjoy in Python that sets it apart from Excel and other basic data analysis tools is the administrative privileges you enjoy when handling data. This is everything from importing data to manipulation.
You can upload any data file in Python, something that you cannot enjoy in Excel. There are some data formats that you generally cannot read or functionally work with in Excel, which impedes your ability to go about your work. This becomes a problem in many situations. You can also come across
data files that are unreadable, but can still work. Python generally allows you more control over data handling. Therefore, you can easily scrape data from different databases and proceed to analyze it and draw conclusions.
Granted that you can still perform a lot of tasks on your data in Excel, you might have some restrictions. These are not there in Python. You can carry out all manner of manipulation on the data you use. Think about recording, merging, and even cleaning data. Through Python libraries like Pandas, you can view and clean some data to ensure it is suitable for the purpose you intended the analysis.
To do this in Excel, you would have to spend more time than necessary, and probably never get it done properly. Therefore, other than the value in terms of utility, Python also offers you the benefit of time consciousness.
● Automated data management
Excel is an awesome program. Microsoft has spent years developing Excel into an amazing tool for data management. This we can see in the GUI. It is an easy tool for anyone to use, especially someone who lacks programming knowledge.
However, in data analysis, you need to go beyond the ordinary if you are to get the best results. More often Excel will be useful up until the moment you need to automate some processes. This is where your problems begin. Other than process automation, it is also not easy to perform an analytical process across different Excel sheets or repeat a process several times.
Programming in Python takes away these problems. Assuming you need to execute some code to analyze recurrent data, you only need to write a script that would import the new data whenever it is available, parse it, and deliver an analytical report on time. On the other hand, in Excel, you would have to manually create a new file, then key in the desired formulas and functions before proceeding with the analysis.
More importantly, in Excel you would save the data format only in the supported Excel formats. However, in Python you can save the output file in whichever database file format works for you. This means you do not have to spend more time on file conversion which in most cases interferes with the outcome.
● Economies of scale
Spare some time and study the organization of data in Excel. One feature that strikes out clearly is that data is organized in tabs and sheets. This is a prominent feature in Excel, and it works well for processes that are completely reliant on Excel. However, the problem comes in when you have a gigantic database to work with. You might be looking at Excel data sheets with lots of entries per
sheet, or a database that has too many Excel sheets.
Processing such database files will take a lot of time. This creates unnecessary lag in data analysis. Many are the times when your machine will crash, unable to process Excel sheets as fast as you need them to. In such a scenario, your only solution is to be patient and process the files one at a time. This is a challenge that you don’t have to worry about in programming. Languages like Python were specifically built to mitigate such issues. You can process large files in Python faster and more efficiently than you would in Excel.
Besides, it is highly unlikely that your device will give up on you as it would when processing datasets in Excel.
● Ability to regenerate data
In your role as a data analyst, you will need to explain your work to more people than you can imagine. Once you are done with the analysis, you might be asked to prepare a report on your findings, which another department will use to meet their objectives. Beyond that, you might also be required to present the outcome in person, and explain to a panel the decisions you make, and your
recommendations. To meet the objectives outlined above, your data must be reproducible. People who were not part of the analytical process should be able to access the data and understand it just as you do. Here’s where the problem arises when using Excel.
First of all, it is generally impossible for you to provide an elaborate illustration of the procedure and processes leading up to your recommendations. The only way you can walk anyone through your analysis is to get the original file and take them through each step.
Given the haste in which you might have done your work, this might be a challenge. Programming in Python, on the other hand, makes your work easier if you ever need to share it with someone. In some cases, all you need to do is press the OK or Enter button and the analysis will be executed as many times as you need it to. Besides, when analyzing data in Python, you can easily explain each step and have your audience follow through, executing code and seeing the results immediately.
● Debugging
If you are analyzing data in Excel, you will have a difficult time identifying errors. In fact, you have to manually look for the errors. Given a dataset with thousands of cells, this could prove to be a problem. Debugging in Excel is therefore a challenge any data analyst would not wish to deal with.
Programming languages like Python make debugging a lot easier. By design, if you enter the wrong syntax you get an error message instead of the expected output. Another good reason for analyzing data in Python is because you can trace the errors in each step. Whenever you key in the wrong functions or syntax, the program will return an error, prompting you to check and sort it out.
In Excel you would probably not know whether you have an error or not, and figuring out the genesis of the problem might force you to start from the beginning, which is more than you could have bargained for.
Since you can include comments in your code, it is easier to trace problems and sort them out. Even if you are not working with data you prepared, you can still read the comments and understand what another programmer did. At the same time, this should not be taken as an assertion that you will fix all the errors you encounter right away. Some errors might take you longer to identify and solve. However, the fact remains that analyzing data in Python gives you an easier and better chance at debugging errors than in Excel.
● Open-source programming
Everything about Excel is in the hands and control of Microsoft. If the program is buggy, you must depend on Microsoft to release patches for bugs. Feature support is also a challenge because unless Microsoft updates their releases, you will have to contend with what is available.
One of the perks of programming in Python is that you are free to enjoy the benefits of open-source programming. You have access to a large community of programmers who are always willing to assist you with any concerns. As you work with some Python code for data analysis, you can improve any of the functions by altering the code accordingly, and share it with the rest of the Python community. There are so many developers who have created or updated some of the packages they use, in the process improving the functionality of the programming language. This has also resulted in better visualizations.
● Advanced operation support
When using Excel, you will struggle when it comes to machine learning and the associated features. This is because Excel was not built for these functionalities. You need advanced programming languages to help you in this regard, hence the need for Python.
In Python, you should also be able to build unique machine learning models. These can be integrated into your code through some of the popular Python frameworks like TensorFlow and Scikit-Learn, thereby enhancing your capabilities when analyzing data.
● Data visualization
You need to see what you are working on. Visualization serves different purposes in data analysis. From the perspective of the analyst, the moment you come across some data, you should easily guess the kind of plot you will use for it. Someone might quip in at this juncture that Excel does offer visualization features. Well, that might be true, but visualization in Excel can be very limited. Python offers you so much more in visualization, especially when you need advanced visualizations. In a business environment, you are called upon to make presentations all the time. Your presentation should be attention-grabbing if it is to convince someone to come onboard.
Each time you are tasked with presenting your report before a panel, remember that most of the people you engage might have no knowledge of data analysis. Therefore, it is impossible for them to read statistical data with the same precision you would. The best way of assisting such individuals would be by plotting some amazing visualizations. A good plot should be one that the audience can make sense of without straining, even if they have no knowledge of statistical computations or data analytics.
It is important to mention that this should not mean you abandon Excel altogether. Excel as a Microsoft Suite has its unique features that will come in handy in data handling and management. However, when compared against Python and other programming languages, it still has a long way to go in terms of data analysis. Perhaps one of the perks of Excel is that you can manually enter data into your database. This comes down to the GUI. If you are working with a small set of data, you can still scan through it instantly through Excel. Generally, Excel is ideal for the basic data analyst. As you advance in the field, however, you should think outside the box. Advance into Python programming so you can learn to perform better, accurate, and complex data analysis without the encumbrances of Excel.
While Python offers these benefits, it is also important to be aware of some of the challenges and limitations you might experience when programming in Python. Our next post will focus on this topic.
While Excel is a great tool, it has some unique challenges that you can overcome by learning Python. A bit of Python programming could really change your life and make data analysis easier for you in data science.
● Expert data handling
One of the first things you will enjoy in Python that sets it apart from Excel and other basic data analysis tools is the administrative privileges you enjoy when handling data. This is everything from importing data to manipulation.
You can upload any data file in Python, something that you cannot enjoy in Excel. There are some data formats that you generally cannot read or functionally work with in Excel, which impedes your ability to go about your work. This becomes a problem in many situations. You can also come across
data files that are unreadable, but can still work. Python generally allows you more control over data handling. Therefore, you can easily scrape data from different databases and proceed to analyze it and draw conclusions.
Granted that you can still perform a lot of tasks on your data in Excel, you might have some restrictions. These are not there in Python. You can carry out all manner of manipulation on the data you use. Think about recording, merging, and even cleaning data. Through Python libraries like Pandas, you can view and clean some data to ensure it is suitable for the purpose you intended the analysis.
To do this in Excel, you would have to spend more time than necessary, and probably never get it done properly. Therefore, other than the value in terms of utility, Python also offers you the benefit of time consciousness.
● Automated data management
Excel is an awesome program. Microsoft has spent years developing Excel into an amazing tool for data management. This we can see in the GUI. It is an easy tool for anyone to use, especially someone who lacks programming knowledge.
However, in data analysis, you need to go beyond the ordinary if you are to get the best results. More often Excel will be useful up until the moment you need to automate some processes. This is where your problems begin. Other than process automation, it is also not easy to perform an analytical process across different Excel sheets or repeat a process several times.
Programming in Python takes away these problems. Assuming you need to execute some code to analyze recurrent data, you only need to write a script that would import the new data whenever it is available, parse it, and deliver an analytical report on time. On the other hand, in Excel, you would have to manually create a new file, then key in the desired formulas and functions before proceeding with the analysis.
More importantly, in Excel you would save the data format only in the supported Excel formats. However, in Python you can save the output file in whichever database file format works for you. This means you do not have to spend more time on file conversion which in most cases interferes with the outcome.
● Economies of scale
Spare some time and study the organization of data in Excel. One feature that strikes out clearly is that data is organized in tabs and sheets. This is a prominent feature in Excel, and it works well for processes that are completely reliant on Excel. However, the problem comes in when you have a gigantic database to work with. You might be looking at Excel data sheets with lots of entries per
sheet, or a database that has too many Excel sheets.
Processing such database files will take a lot of time. This creates unnecessary lag in data analysis. Many are the times when your machine will crash, unable to process Excel sheets as fast as you need them to. In such a scenario, your only solution is to be patient and process the files one at a time. This is a challenge that you don’t have to worry about in programming. Languages like Python were specifically built to mitigate such issues. You can process large files in Python faster and more efficiently than you would in Excel.
Besides, it is highly unlikely that your device will give up on you as it would when processing datasets in Excel.
● Ability to regenerate data
In your role as a data analyst, you will need to explain your work to more people than you can imagine. Once you are done with the analysis, you might be asked to prepare a report on your findings, which another department will use to meet their objectives. Beyond that, you might also be required to present the outcome in person, and explain to a panel the decisions you make, and your
recommendations. To meet the objectives outlined above, your data must be reproducible. People who were not part of the analytical process should be able to access the data and understand it just as you do. Here’s where the problem arises when using Excel.
First of all, it is generally impossible for you to provide an elaborate illustration of the procedure and processes leading up to your recommendations. The only way you can walk anyone through your analysis is to get the original file and take them through each step.
Given the haste in which you might have done your work, this might be a challenge. Programming in Python, on the other hand, makes your work easier if you ever need to share it with someone. In some cases, all you need to do is press the OK or Enter button and the analysis will be executed as many times as you need it to. Besides, when analyzing data in Python, you can easily explain each step and have your audience follow through, executing code and seeing the results immediately.
● Debugging
If you are analyzing data in Excel, you will have a difficult time identifying errors. In fact, you have to manually look for the errors. Given a dataset with thousands of cells, this could prove to be a problem. Debugging in Excel is therefore a challenge any data analyst would not wish to deal with.
Programming languages like Python make debugging a lot easier. By design, if you enter the wrong syntax you get an error message instead of the expected output. Another good reason for analyzing data in Python is because you can trace the errors in each step. Whenever you key in the wrong functions or syntax, the program will return an error, prompting you to check and sort it out.
In Excel you would probably not know whether you have an error or not, and figuring out the genesis of the problem might force you to start from the beginning, which is more than you could have bargained for.
Since you can include comments in your code, it is easier to trace problems and sort them out. Even if you are not working with data you prepared, you can still read the comments and understand what another programmer did. At the same time, this should not be taken as an assertion that you will fix all the errors you encounter right away. Some errors might take you longer to identify and solve. However, the fact remains that analyzing data in Python gives you an easier and better chance at debugging errors than in Excel.
● Open-source programming
Everything about Excel is in the hands and control of Microsoft. If the program is buggy, you must depend on Microsoft to release patches for bugs. Feature support is also a challenge because unless Microsoft updates their releases, you will have to contend with what is available.
One of the perks of programming in Python is that you are free to enjoy the benefits of open-source programming. You have access to a large community of programmers who are always willing to assist you with any concerns. As you work with some Python code for data analysis, you can improve any of the functions by altering the code accordingly, and share it with the rest of the Python community. There are so many developers who have created or updated some of the packages they use, in the process improving the functionality of the programming language. This has also resulted in better visualizations.
● Advanced operation support
When using Excel, you will struggle when it comes to machine learning and the associated features. This is because Excel was not built for these functionalities. You need advanced programming languages to help you in this regard, hence the need for Python.
In Python, you should also be able to build unique machine learning models. These can be integrated into your code through some of the popular Python frameworks like TensorFlow and Scikit-Learn, thereby enhancing your capabilities when analyzing data.
● Data visualization
You need to see what you are working on. Visualization serves different purposes in data analysis. From the perspective of the analyst, the moment you come across some data, you should easily guess the kind of plot you will use for it. Someone might quip in at this juncture that Excel does offer visualization features. Well, that might be true, but visualization in Excel can be very limited. Python offers you so much more in visualization, especially when you need advanced visualizations. In a business environment, you are called upon to make presentations all the time. Your presentation should be attention-grabbing if it is to convince someone to come onboard.
Each time you are tasked with presenting your report before a panel, remember that most of the people you engage might have no knowledge of data analysis. Therefore, it is impossible for them to read statistical data with the same precision you would. The best way of assisting such individuals would be by plotting some amazing visualizations. A good plot should be one that the audience can make sense of without straining, even if they have no knowledge of statistical computations or data analytics.
It is important to mention that this should not mean you abandon Excel altogether. Excel as a Microsoft Suite has its unique features that will come in handy in data handling and management. However, when compared against Python and other programming languages, it still has a long way to go in terms of data analysis. Perhaps one of the perks of Excel is that you can manually enter data into your database. This comes down to the GUI. If you are working with a small set of data, you can still scan through it instantly through Excel. Generally, Excel is ideal for the basic data analyst. As you advance in the field, however, you should think outside the box. Advance into Python programming so you can learn to perform better, accurate, and complex data analysis without the encumbrances of Excel.
While Python offers these benefits, it is also important to be aware of some of the challenges and limitations you might experience when programming in Python. Our next post will focus on this topic.
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