Aptude has served as expert data consultants to some of the most well-known companies in the world, including some we can’t name. Our clients span nearly every industry and ask us to help them with a variety of projects, including full-stack development, IT Service Management, data dashboarding, and UX/UI. And while we can do almost anything, some of our best work involves deep expertise in data science, especially when it comes to Python and Data Science.
We love Data Science and Python so much that we’ve developed a Python Center of Excellence in Mexico City, Mexico, to attract, train, and place experienced talent in local and remote projects.
In this article, we’ll dig deeper into why we love Python for data science so much, especially compared to other languages such as R or Scala.
The Languages for Data Science
First, it’s helpful to understand why programming languages are needed for Data Science at all.
The first thing to know: data has always used some kind of programming language to work. Relational databases, for example, use forms of SQL (including T-SQL) to tell the database what to do with the 0s and 1s that make up the data in the database. Because data is just that – static fields with (often) structured information. That’s it.
The second thing to know: data science involves the manipulation of extremely large data sets (“big data”) using complex mathematical algorithms. Where SQL involves simple commands of joining rows of data, adding or deleting data, and creating simple “views”, advanced data science programming languages manipulate data in ways that would be very expensive and frankly impossible to do manually or in a spreadsheet.
Just some of the available languages for data science are:
For our money, Python is where it’s at. And it’s not just because in survey after survey, Python is the most wanted, popular, and loved language.
Python is used for1:
- Desktop GUI
- Web development
- Game development
- Machine Learning
- Data Science
- Data analytics
- Artificial Intelligence
- Internet of Things (IoT)
- Computer Vision
- Web Scraping
- Natural Language Processing
- Scientific and Numeric computing
- Software Application Development
- Network Programming
Why We Love Python for Data Science Work
Python is a great programming language for data science work. Here’s why we love it…
Python is easy to learn. From a programming perspective, Python is one of the easiest languages to learn, which means it’s also one of the best for building larger teams of experienced developers and easier for our clients to maintain these teams after our main work is done. We’re also likely to find Python already in use in client organizations due to this ease of learning.
(Learning data science is another matter, of course.)
Python is Flexible. It runs on almost every platform, including Windows and MacOS. As a language it works well enough for a variety of uses, making it versatile and flexible.
Python programmers are more affordable. While you can do a lot with Java, R, and the Hadoop framework… that doesn’t mean that work comes at an affordable price.
Python is trusted by industry leaders. Google, Youtube, Instagram, NASA, IBM, Netflix, Spotify, Uber, Pinterest, Reddit, and more use Python.
Python is code efficient. For what you can accomplish in R, you’ll use far less code by writing it in Python.
Python has many data science libraries and tools:
- NumPy and pandas
- Scikit-learn for Machine Learning (ML)
- PyMySQL for MySQL databases
- iPython notebook for interactive programming
- Matplotlib for data visualization
The final reason, of course, is that we have a Python Center of Excellence in Mexico City, Mexico dedicated to solving complex data science problems for both local and global companies.
If you’d like some expert helping in figuring out where to start and what you need in terms of data, manpower, tools, and budget, we can help. Many of our projects involve data-related initiatives, especially since we now have a Python Center of Excellence in Mexico City, Mexico. Getting our help is as easy as contacting us via email, form, or phone.