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Many people, especially aspiring data scientists, wonder if they should learn to code if they have no interest in becoming software engineers. While there are many valid arguments on both sides of the issue, this article will focus on the benefits of knowing at least the basics of programming languages like Python and R, the two most popular languages in data science today. Coding is an invaluable skill that will serve you throughout your entire data science career, even if you don’t pursue development further down the line!
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Code Allows Us To Manipulate The Data
1: Which of the following algorithms is most suitable for classification tasks?
You have raw data in hand, but you don’t know what to do with it. You could query some databases and try to summarize data manually, but you risk missing things or getting stuck trying to combine datasets. You might use a visualization package to make a plot, but that limits your flexibility. Writing code is crucial because it allows us to be as flexible as we need; there are few limitations once we have our code set up. We can quickly and easily manipulate our data before turning it into something more readable. Coding also helps us extract specific pieces of information when needed; if we wanted to find all transactions above $250 today, how long would it take us if we were only querying databases?
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Scripting Makes It Easier To Replicate Results
A lot of machine learning algorithms need to be trained using many thousands of examples. If you want to try them out on different data sets, or if you want to run your program in parallel across multiple computers (to speed things up), then it can be very useful to write code that automates some of these processes. A few lines of Python, for example, will often make it easier to rerun a chunk of code many times over and extract whatever results are needed at each step along the way. And much like professional sports teams practice more than they play games so they can execute flawlessly when it matters most, good data scientists practice more than they perform analyses because writing code allows them to efficiently analyze their data every time they run their program.
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You Need Python
Python has been one of the most widely used programming languages in data science and machine learning (and most other areas of tech) over recent years. The language, created by Guido van Rossum in 1991, was initially conceived as a scripting language to make system administration more accessible. It has since become a general-purpose programming language that’s particularly well suited to data science, with built-in features such as an extensive standard library and powerful support for large libraries (called packages). Because it can be used both for quick analyses and sophisticated development projects, Python has become a top pick among data scientists. Why you should learn Python: You don’t have to be a wizard at coding… But if you want to break into data science or machine learning, it will definitely help.
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You Need Statistics Skills
One of the most important skills in data science is statistical analysis, and you won’t be able to do it unless you know how to code. Luckily, many programming languages are helpful when it comes to statistics, especially those designed with scientists in mind (such as R). Here are a few other ways coding can help you in your data science career: Help you communicate more effectively with data: Another key skill of data scientists is communicating their findings. In order to best communicate your findings, you must be able to visualize them clearly and simply—something that’s much easier done through an image than by writing out loads of numbers or words. Even just organizing raw data points into spreadsheets will make it easier for everyone else to analyze and understand what’s going on at a glance.
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Machine Learning Requires Programming Skills
A lot of people think that machine learning and data science are about throwing a bunch of data at software and letting it run—and there’s a little bit of truth to that. But, programming isn’t just about entering commands into some kind of magic software box. To really understand what’s going on in your algorithms, you have to know how they’re coded so you can troubleshoot them when they go wrong. Sure, we all wish someone else would do that hard work for us, but at some point, we’ll have to pay rent and learn how to code properly! After all, knowing is half the battle.
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Visualization Requires Coding Skills
Good data science isn’t possible without a way to communicate results and ideas. Tableau Public or simple interactive features within tools like Excel can help with that, but if you want to do real data science, you have to find a way to convey information visually. Many people in positions of a power struggle with interpreting information in table form; graphs are much easier for them (and everyone else) to understand. Fortunately, there are many open-source tools that allow users to code visualizations; however, you may have to learn how to code with them first. In some cases, it might make sense for you to pursue an advanced degree—something like computer science or statistics—if your goal is a career as a data scientist. If you are interested to learn new coding languages, the Entri app will help you to learn easily. Entri app is following a structural study plan so that the students can learn everything easily and quickly. You can download the Entri app from the play store and explore the benefits.
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