You never hear about data science without hearing about Python as well, and for good reason as it’s the most common language for data scientists. In fact, 69% of data scientists report using Python, compared to 24% using R. This doesn’t mean Python is superior in general; it just means that Python for data science seems to be the more common pair, whereas other fields may flock to R. If you haven’t started Python for data science yet, then here are a few reasons why it may be time.

Reason 1: Python for Data Science is in-demand for job searches

When hiring managers are looking for data scientists, data engineers, and others in the AI field, Python often ranks as one of the most included skills – with many listings not even mentioning R. Especially for the emerging title of data engineer, Python is clearly a dominating required skill.

Data Engineer Skills


Reason 2: You already know R or Julia and want to start Python for data science

Well, you already know a coding language and that’s great. But, maybe you’re in a different field such as statistics or analytics where R is better, and you want to shift over to data science. By adding Python to your skillset, not only will you learn a skill better suited for data science, but you then supplement your existing skillset making you a truly stand-out data scientist.

Reason 3: There’s a great community and open-source ecosystem to learn from

The joy of open data science is that there’s a vibrant community of data science enthusiasts who share their work constantly, framework developers post how-to guides on how to get started, and even larger companies with in-house AI teams will post their findings. With all of this information available, including the Python section of our own site, there are plenty of ways to get the information you need without having to look too far.

Reason 4: It’s easy and convenient to get the training you need in Python for Data Science

If you’re not finding the open-source information you need, or if you just want a little more hands-on training, then there are plenty of options out there. Maybe you do need that certificate, or maybe a few courses are all you need. With Ai+ Training, there are tons of options available to help teach you what you need to succeed. One example is with the upcoming session on October 12th, “Introduction to Python Programming.”

Reason 5: It’s a timeless skill that will be valuable for years to come

Python dates back to the early 1990s – meaning it’s been actively in use for 30 years and counting. While it’s had some ups and downs with different trends (Thanks, Y2K), it’s been a staple of the programming community since its inception. Data scientists are actively developing for it, hiring managers are actively seeking experts in it, and it’s not slowing down.

Learn more about Python for Data Science with Ai+ and ODSC Events

In the aforementioned Ai+ Training session on October 12th, “Introduction to Python Programming,” you’ll be able to understand the object model of Python. You will familiarize yourselves with basic data structures, build functions, and classes to aid in data science work. Upon the completion of the course, you will be able to write and read Python code as well as being aware of some of the idioms that are unique to Python.

If you’re interested in a conference setting across multiple days, then these sessions coming to ODSC West 2021 this November that feature Python may be right for you:

  • Applications of Modern Survival Modeling with Python: Brian Kent, PhD | Data Scientist/Founder | The Crosstab Kite
  • Build a Question Answering System using DistilBERT in Python: Jayeeta Putatunda | Data Scientist | MediaMath
  • Identifying Deepfake Images and Videos Using Python with Keras: Noah Giansiracusa, PhD | Assistant Professor of Mathematics/Data Science | Bentley University
  • Introduction to Scikit-learn: Machine learning in Python: Thomas Fan | Senior Software Engineer | Quansight Lab
  • Introduction to DL-based Natural Language Processing using TensorFlow and PyTorch: Magnus Ekman, PhD | Director | NVIDIA