Ten Strong Arguments for Learning Python for Data Science
Despite the high average wage and high levels of job satisfaction associated with data science, there are still more companies publishing job advertising for data scientists than there are actual data scientists.
For a variety of reasons, including the fact that many FAANG-adjacent organisations use it, its versatility, and the ease with which both experienced and novice programmers can learn it, I dabbled in data science using Python.
Despite the fact that Python is a general-purpose language, this article lists 10 compelling justifications for learning it for data science along with an explanation of what Python is.
It’s so easy to use that Next Academy actually suggests it as a fantastic way for kids to learn how to code.
Python is a wonderful coding language to start with if you want to work in data science since you can pick it up quickly and painlessly. Beginners may find it easy to learn data science with Python.
Extensive Data Science Libraries
python’s extensive collection of data science libraries is one of its biggest strengths for data scientists. Here are some of the most prominent libraries that make Python a popular choice for data science:
1.NumPy:
NumPy is a fundamental library for scientific computing in Python. It provides powerful tools for working with multi-dimensional arrays and matrices, along with a wide range of mathematical functions. NumPy forms the foundation for many other data science libraries in Python.
2. Pandas:
Pandas are a versatile library for data manipulation and analysis. It offers data structures such as DataFrames that make it easy to clean, transform, and explore tabular data. Pandas provides a wide range of functions for data alignment, filtering, aggregation, and handling missing data.
3. Matplotlib:
Matplotlib is a plotting library that enables the creation of various types of static, animated, and interactive visualizations. It offers a high degree of customization, allowing data scientists to create publication-quality plots and charts.
4. Seaborn:
Seaborn is a data visualization library built on top of Matplotlib. It provides a higher-level interface for creating visually appealing statistical graphics. Seaborn simplifies the creation of complex plots like heatmaps, violin plots, and pair plots with minimal code.
5. Scikit-learn:
Scikit-learn is a comprehensive machine-learning library in Python. It offers a wide range of algorithms and tools for tasks such as classification, regression, clustering, dimensionality reduction, and model evaluation. Scikit-learn provides a consistent API and is widely used for building and deploying machine learning models
It is simple to read.
Everything you develop with Python will make sense to you and many other people, even if they aren’t Pythonistas themselves since it has a clear, straightforward syntax that is similar to English.
Python was initially quite simple for me to learn in part because I could read Python code examples and understand what they were attempting to do. You should surely consider readability as a fundamental element of whatever language you select if you want to go into data science.
You’ll read a lot of code and converse with your coworkers about it (or strangers on the internet as you try to debug something on StackOverflow). That is simple to implement with Python.
If you learn Python, you’ll be one of many. It’s one of the most widely used languages in data science (and elsewhere). It’s the third-most widely used language in the world according to TIOBE’s 2020 index. And in data science specifically, it’s emerged as the leader, outstripping my old favorite language R.
As I alluded to above, many companies are using Python to build frameworks and projects. Google, for example, created Tensorflow, which is based on Python; Facebook and Netflix are also relying on it more and more in their data science projects.
If you want to get into data science, you won’t get far without knowing at least some Python. Luckily it’s a joy to learn!
Large Pythonista Community
There is a huge and passionate community of Pythonistas out there who are more than happy to share their tips, answer your questions, correct your code, and discuss new ideas because it has been around for three decades, is simple to learn and easy to build with, and has remained relevant to so many people and companies for such a long time. You can find them anywhere; Reddit has one of the most active communities, but you can also discover Discord groups that meet to discuss Python.
This makes learning Python for data science such an excellent option since learning any language is challenging, especially if you’re under professional pressure. That is made simpler by communities like the ones that have developed around Python.
A wider variety of data science libraries
On its own, Python excels as a language for data research. But in addition to the straightforward syntax, concise vocabulary, readability, community, and all the other advantages I’ve already mentioned, there are also libraries. In the data science communities, libraries like Pandas, statsmodels, NumPy, SciPy, and Scikit-Learn are particularly well-liked.
Data science activities are greatly simplified by ecosystems like SciPy. (SciPy is not pronounced skippy as I first thought; it is pronounced sigh-pie.) Numerous typical data science needs are addressed by SciPy, including managing data structures, analysing complicated networks, algorithms, and machine learning toolkits. Popular and dynamic Python data science libraries are available.
The truly interesting part is that as more Pythonistas join the community and contribute on their own, new Python packages for data science are constantly being released. Popular and dynamic Python data science libraries are available. For instance, the 2015 release of the simple deep learning package Keras. Since that time, it has grown to be an essential part of the Python library ecosystem.
Communication
“By providing it with a visual context through maps or graphs, data visualisation offers us a clear notion of what the information means. According to an anonymous blogger on the Analytiks blog, “This makes the data more natural for the human mind to interpret and, as a result, makes it simpler to discover trends, patterns, and outliers within enormous data sets.
Many people believe that data science ends with the analysis, but like with anything else in the professional world, what happens next is what counts.
The really simple matplotlib and its two offspring Pandas and seaborn are only a couple of the fantastic visualisation tools that Python has to offer (both built on matplotlib). The fight is half won if you can quickly create a solid viz to explain or demonstrate the facts. Python facilitates it.
Data cleanup is simple.
When I hear the term “data science,” I picture Neo from The Matrix performing awesome things while sporting a stylish coat. Many people are unaware that data science involves a LOT of less glamourous data cleansing. According to conservative estimates, 80% of a data scientist’s normal effort consists of data cleansing. The good news is that Python excels at that.
If you want to work in data science, you must accept that it will take you a lot of data scrubbing, cleaning, massaging, wrangling, etc. before you create even one cool visualisation. Python is built to clean, which is why studying it for data science is an excellent option.
NumPy and Pandas, two of the packages I previously mentioned, are excellent at cleaning data.
Teaches fundamentals
Even though Python has virtually endless applications, studying Python and data science really have a lot in common. Python’s introductory tutorials make it simple to pick up the fundamentals of data science. If you wish to utilise Python to understand data science, you may start by learning how data scientists retrieve, clean, visualise, and develop models.
By default, as you progress through the traditional path of learning Python coding, you’ll pick up the fundamentals of data science. For instance, you will first learn how to set up your environment, import data, clean it up, conduct statistical analysis on it, produce some attractive visualisations, and share your findings. And look at what you’ve accomplished.
You can easily locate a tonne of materials that teach Python and especially teach Python for data science if you keep common data science activities in mind while you search for Python lessons. A natural learning route for data science is to understand the fundamentals of Python.
Speedy prototypes
Projects involving data scientists are pricey, which is a little-known truth. In fact, “87% of data projects will fail,” according to Chris Chapo, SVP of data and analytics at Gap. Building anything that succeeds requires a lot of patience, time, effort, and resources.
To circumvent this, the majority of data scientists employ prototypes to stress-test and dry-run their ideas before actually developing them. It shouldn’t come as a surprise to you if you’ve been following the subject of this essay that Python is excellent for creating high-quality prototypes to test out concepts, ideas, and products.