Python for Data Science

[Participant @oluwole]

Data Science ranks third on the list of the top 10 best jobs in America for 2020  according to Glassdoor. The demand for data scientists has been on the rise since the last decade owing to the increase in popularity of data-driven decisions. Big companies like Google, Facebook and Amazon have entire data science divisions while small businesses have realized large gains from the use of data analytics.

A lot of tools are available for use by data scientists. These include Python, R, Tableau, SQL, Hadoop, Spark, AWS, and more. Python language, however, according to Jeff Hale is the most in-demand technology for data scientists in 2019, appearing in nearly 75% of listings. The nearest challenger for the top spot is R, with an appearance in about 55% of listings. Clearly, Python is significantly ahead. But what gives it the edge over other technologies?

What is Python?

Python is a high-level programming language which features object-orientation and integrated dynamic semantics. In less technical terms, it is a general-purpose language with an elegant syntax that shifts the focus of programmers from syntax errors to problem-solving.

It was created by Guido van Rossum and had its first release in 1991. Python operates with an open-source license, i.e. it is free to use and distribute, even for commercial purposes. The language is designed to be easy to use and read. Thus, it is no mere coincidence that it ranks as the second most loved language according to Stack Overflow’s 2019 Developer Survey.

Programming languages are how programmers express and communicate ideas – and the audience for those ideas is other programmers, not computers.
– Guido van Rossum

The use of Python is ubiquitous and that is a continuously expanding landscape. According to Stack Overflow’s 2019 Developer Survey, it is the fastest-growing programming language. Here is an inexhaustive list of Python’s applications;

1. DevOps
2. Web development
3. Data science
4. System scripting
5. Computer vision
6. Machine Learning
7. Artificial Intelligence

It is, therefore, a no-brainer that Python is regarded as the Swiss Army knife of the coding world. But what makes it such a great fit for Data Science? We will get to that, but first a little bit on Data science.

Data Science: An Overview

Data science involves obtaining useful information from Big Data. These data are often complex, unsorted and difficult to correlate with any reliable accuracy. It is a vast field that majorly cuts across finance, health care and e-commerce industries.

It is imperative to note that although data science is sometimes referred to as data analysis because of their similarities, they are not the same thing. The difference being that a data analyst focuses on getting insights from known data, while a data scientist is more involved with extrapolating hypotheticals and what-ifs. Both fields require similar skills, including knowledge of programming languages, such as Python, SQL, R, SAS, and Hadoop. Therefore, data scientists and data analysts potentially enjoy the same benefits that Python provides.

What makes Python a great fit for data science?

A lot of data scientists have a preference for Python for several reasons. From its ease of use to its popularity and strong online community, Python has become a reliable tool that caters to all the needs of a data scientist.

Ease of Use/Learning

Python is easy to learn, especially when compared to other languages. It is commonly recommended as the first language for beginner programmers because of its intuitive syntax and the fact that a lot of its complexities are handled internally. The interesting bit about this is that its ease of use does not compromise on its power.

Also, the time spent on implementing pythonic code and debugging is comparatively less. This, alongside the fast learning curve that python promotes, puts it in the good books of data scientists

Scalability

Python possesses good flexibility that affords data scientists to explore dynamic solutions to problems. It excels in scalability compared to languages like R and is also pretty fast.

Functional Libraries

A library is, essentially, a collection of modules. These modules allow the execution of many actions without writing code. In other words, a python library gives data scientists access to specific methods and functions.
Hundreds of Python libraries exist but not all are pertinent to data science. Some of the libraries commonly used by data scientists include NumPy, Pandas, SciPy, Matplotlib, Scikit-learn, Seaborn and TensorFlow.

Pandas is a contraction of Python Data Analysis Library. It is indispensable to the data scientist as it helps with the basic operation and maintenance of structured data. From importing data from Excel spreadsheets and CSV files to processing datasets for time-series analysis, Pandas pretty much takes care of data preparation and munging.

NumPy (Numerical Python) and SciPy (Scientific Python) are similar libraries that offer tools and methods for data analysis. They are the best resort for functionalities related to science and engineering such as linear algebra and transformations.

Matplotlib and Seaborn are powerful visualization libraries that can make plots of different types from the available data. Bar graphs, histogram, pie charts and heat plots are some of the visual representations that these libraries provide. Matplotlib is especially useful for 2D visualization.

Scikit-Learn is a useful machine learning library that makes provision for the implementation of functionalities such as regression, algorithms, and data mining.

TensorFlow is, perhaps, the most popular machine learning tool in Python. It can carry out deep learning tasks and its high processing capability gives it numerous applications.

Strong Community

Since Python is open-source, things can go wrong. However, Python’s reach extends far into the academic and industrial circles. This ensures an ecosystem where programmers are involved, library functionalities are consistently being extended, and solutions to coding problems are provided.

As a result, the data scientist is hardly ever stuck. Online platforms like Stack Overflow make good provisions for the scientist to have his queries answered. There are also local meet-up groups in most cities with experts that can assist data science enthusiasts.

Final Thoughts

Are you a new data science enthusiast struggling to choose what language to learn? Or are you an experienced one interested in adding a new skill? Whichever category you fall in, Python is the right place to start. The language is one of the easiest to learn, with the added advantage that it isn’t venomous! With very functional libraries and millions of users who make up diverse communities, it is the go-to tool for data scientists.

Python’s versatility also ensures that even if you don’t work on ML or data science, there are many other applications like web development and DevOps that would open a lot of career opportunities for you. Learning Python for data science is a win-win. Get started today!

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