Why Python, is One of the Most Preferred Languages for Data Science Why not R and SQL?
Why do most data scientists love Python? Learn more about how so many well-developed Python packages can help you accomplish your crucial data science tasks.
According to job sites such as Indeed, Glassdoor, Naukri, LinkedIn, and Dice demands data scientists and continues to grow, year over year, as businesses across the industries increasingly depend on data-driven insights.
There are, in fact, many different learning paths to this hottest profession, and choosing the right one depends on where you are in your career. Besides mathematical and statistical skills, programming expertise is also one of the must-have skills an aspiring data scientist needs to acquire.
Let’s dig deeper to unearth the most popular programming languages in the data science community!
Top 3 programming languages most used by data scientists
As revealed by the findings of a survey conducted by Kaggle, Python is the most used programming language followed by SQL and R. On the whole, both Python and R help programmers to perform common data analysis tasks efficiently. But Python is a general-purpose highly flexible programming language, whereas R is designed specifically for statistical computing and data analysis. The Image shows the overall use of Python by data Science across the globe.
The survey was carried out on nearly 24,000 data professionals, wherein 3 out of 4 respondents recommended aspiring data scientists to begin their learning journey with Python. In this article, let’s find out what makes Python the most sought-after programming language among data professionals and why to choose Python for data analysis.
Why data scientists love Python?
Data scientists need to deal with complex problems, and the problem-solving process involves four major steps — data collection & cleaning, data exploration, data modeling, and data visualization.
Python provides them with all the necessary tools to effectively carry out this process with dedicated libraries for each step that we will discuss later in this article. It comes with powerful statistical and numerical libraries such as Pandas, Numpy, Matplotlib, SciPy, sci-kit learn, etc. and advanced deep learning libraries such as Tensorflow, PyBrain, etc.
Moreover, Python has emerged as the default language for AI and ML, and data science has an intersection with Artificial Intelligence.
Therefore, it is not at all surprising that this versatile language is the most used programming language among data scientists.
This interpreter-based high-level programming language is not only easy to use, but it also equips data scientists to implement solutions and, at the same time, follow the standards of required algorithms.
Now, let’s take a look at the steps involved in the data science problem-solving process and Python packages for data mining that should be an indispensable part of your toolbox as a data scientist:
- Data collection & cleansing
- Data exploration
- Data modeling
- Data visualization & interpretation
Data collection & cleansing
With Python, you can play with almost all sorts of data that are available in different formats such as CSV (comma-separated value), TSV (tab-separated value) or JSON sourced from the web.
Whether you want to import SQL tables directly into your code or need to scrape any website, Python helps you achieve these tasks easily with its dedicated libraries such as PyMySQL and BeautifulSoup, respectively. The former enables you to easily connect with a MySQL database to execute queries and extract data while the latter helps you to read XML and HTML type data. After extracting and replacing values, you would also need to take care of missing data sets during the data cleansing phase and replace non-values accordingly.
Furthermore, if you get stuck with any particular dataset, then you can get a solution by doing a Google search about that dataset and Python, thanks to the strong and vibrant Python community!
Data exploration
Now that your data is collected and tidied up make sure it is standardized across all the data collected. Now that you have clean data, figure out the business question that needs to be answered, and then convert that question into a data science question.
For that, explore the data to identify their properties and segregate them into different types such as numerical, ordinal, nominal, categorical, etc., to provide them required treatments.
Once data is categorized as per their type, NumPy and Pandas, the data analysis Python libraries, will help you to unleash insights from the data by allowing you to manipulate it easily and efficiently.
Now that your data is ready to be used, it’s time to jump onto AI and machine learning for data modeling.
Data modeling
This is a very crucial phase in the data science process wherein you would strive to minimize the dimensionality of your data set.
Python has many advanced libraries to help you tap the power of machine learning in performing the tasks involved in data modeling.
Would you like to perform a numerical modeling analysis of your data? Just reach out for Numpy in your toolkit! With SciPy you can easily perform scientific computing and calculations. Scikit-learn code library offers you an intuitive interface and helps you apply machine learning algorithms to your data without any complexities.
After data modeling is over, you would need to visualize and interpret data for actionable insights.
Data visualization & interpretation
Python has many data visualization packages. Matplotlib is the most used library among them for generating basic graphs and charts. In case you need beautifully designed advanced graphs, you could also try another Python library, Plotly.
Another Python library, IPython, helps you with interactive data visualization and supports the use of a GUI toolkit. If you want to embed your findings into interactive web pages, nbconvert function can help you convert your IPython or Jupyter notebooks into rich HTML snippets.
After data visualization, the presentation of your data is of utmost importance, and it must be done in such a manner that the findings are driven by the business questions that you have asked at the beginning of your project.
Now that you deliver the answer to the business questions along with actionable insights, try to keep in mind that your interpretations appear useful to the stakeholders of your organization.
Ready to embrace Python for your data science goals?
With so many reasons to consider Python programming when you are embarking on your data science journey, here’s another solid one to consider. Top tech giants are also using Python for various reasons. Here’s why Amazon is using Python:
So, If you like the blog, please give a clap and for any doubt, just write a comment, I will surely help you.
Thank You!
BIO: Arpit Bhushan Sharma is an Electrical and Electronics Engineer and pursuing his Bachelor of technology from the KIET Group of Institutions, Ghaziabad. He has a little bit of experience in python and machine learning and wants to make his career in Machine Learning. He loves to write technical articles on various aspects of data science on the Medium platform and Blogger Platform.
Medium: www. medium.com/@bhushansharmaarpit
Blogger: arpit-ml.blogspot.com
