In this blog, we will be discussing the similarities and differences in python and tableau as a tool to start your career in the data world. But first, we will be seeing the features of these tools independently and then we will compare these two. After your reading you will be able to make a decision about your career choice and which tool should you start your journey with. Now, let us check the functionalities of these two tools as an independent observer.
What exactly is Python?
To answer this question, first, we will ask what is Computer Programming? To unravel these questions, we write a couple of lines of commands into the pc and runs these commands one after the other. If these instructions run well then it would solve the matter.
Making the pc understand the commands isn’t simple for us humans. Because the language of the pc is different from ours. The computer can only understand the basic level of language. Now, this is often a difficult situation for humans because we don’t use the pc language in our daily communications. Hence, we’d like to find out it for the sake of communicating with the pc (that is, executing programs in it) and it’s a haul. You’ll need to spend time in understanding and interpreting the language instead of solving problems.
For the developers to understand and make it more user-friendly, they built languages over the essential computer-oriented language in order if any commands written in this language would be indirectly executed within the basic language. If we put it more easily, Python is a high-level programing language that’s widely popular within the software circles. The recognition mainly raises from three reasons:
The simplicity of Code:
Commands in Python are simple to write down because the syntax is sort of on the brink of the straightforward English commands we use on a day to day basis. This makes the execution of the code easier for the coders and thereby consider the effectiveness of the solutions
Large coding community:
Computer Programming may be a mammoth. But still, there are many situations in the industry with programmers trying to unravel in many computer languages. Being an open-source software and therefore the simple usage, there are thousands of coders who use the language. They’ve close group and share their problems and solutions thereby making the mundane problems simpler to affect instead wasting the valuable time on them.
Coding Packages and libraries:
Coders try to solve the dials problems using these languages. So, the structures within which they function remain largely similar – the info structures, the operations, mathematical applications, etc. Because the problem at hand becomes complex (which would be the case within the real world), it does not make sense to rewrite the code again and again. Hence, the community has close to create packages (essentially, libraries in python which may solve a couple of distinct set of problems – like all mathematical operations, etc.). Because the community is robust, so are the density of those packages that are prevalent.
Now, we will be looking at the feature of tableau and what it brings to the table.
What exactly is Tableau?
If you are making decisions it means you are solving a problem. When the proper decisions are taken, the matter is going to be solved (Eg: you would like to write down an essay, you opt to awaken at 5 am). Now, the accuracy of the choices is strongly supported by the experience available within the system. Experience, in other words, is actually data or information.
The experience here is the insights from data. Once you want to write down an essay, how does one know you’ll need to awaken at 5? Because you might have done it earlier as well, so one would have a thought of how long it might take to write a fancy essay and hence decides to awaken at 5. So, the previous experience (which is data), including the vivid idea of how long it might take to write the fancy essay (which is that the insight), would help him/her make a choice.
MS Excel may be a useful gizmo at managing and handling the data. But, Excel features a critical limitation. It cannot manage larger amounts of knowledge. You add 10 columns of info for ten thousand rows and you’ll see Excel struggling. The info generated within the industries lately isn’t only huge, but also complex. And therefore the dimensions of info are also multifold. For instance, a commerce clothing company would have product-level data, employee-level data, sales level data, and inventory level data. The list just goes on. Data consumers would wish something more robust and concrete as an answer to return up with insights.
The answer is Tableau. At the guts of it, Tableau may be a strong data visualization tool. And it’s growing fast. A couple of reasons for it:
It can manage multiple data sources:
Data lately is out there in various shapes and sizes. Also, in several formats and stored in several places. Tableau connects with any data source or format that’s prevalent in this day and age – csv, xls, xlsx, txt, etc. Data is often imported from the prominent servers – MySQL, Amazon Redshift, Tableau Server, etc.
Self-Reliant & Data Blending is easy:
Tableau Desktop is extremely easy to put in and comes with most of the features that are available, so in order that they can plough ahead and complete the data analysis. Tableau allows you to map different raw/semi-structured data together with no additional costs for the service. This makes performing on complex data easier to interpret.
After going through this blog, you must have decided about your own career plan. If not, then you can ask the experts at PST Analytics to get started with this career.