No one really knows how to do a job before they do it. Most people land a coveted position through a strange alchemy of related experience, networking, and hard work. The real experience is the job itself. That’s when you get the opportunity to apply what you know to real-world problems and see it pay off.
The following four programmers earned their first Python jobs in different ways. Some had prior Python experience, some didn’t. Some knew what they were getting into, others found out later. Understanding how they landed their first Python job might help you land yours. Here’s how they did it.
First Python job: Data Scientist
How Nathan Got the Job
While completing my Physics degree, I applied for a data science job with a small tech startup that primarily used Python (and SQL). The thing is, I didn’t have experience with Python at the time. When the interview came around, I answered the programming questions by using pseudocode to demonstrate I understood the concepts.
Pseudocode uses coding logic without using coding syntax. So by using the same logic that Python does, I could show an understanding of the concepts without being specific to any language.
For example, any computer scientist can understand the simple pseudocode below, but they may not understand the Python function unless they’ve worked with it before.
loop_index = 0 while loop_index < 5: print(loop_index) loop_index += 1
Set loop index to 0 Loop while loop index is less than 5 print loop index Increase loop index by 1
Pseudocode is more readable to humans, too. It’s not actually much different from code, it just avoids using language-specific syntax. And using it it worked! They gave me the job. But of course, before I arrived I had to actually learn the language.
My advice for those wanting to enter the field is to tackle real-world problems as soon as you can. At Project Hatch, a company I cofounded that analyzes startups and provides them with analytics to grow their businesses, we do hire people who are self taught, but there’s a huge skill gap between those who only do Codecademy-style courses and those who actually apply their knowledge. I would say keep working through Codewars challenges until you’re at a point where you don’t have to repeatedly look up what arguments you should be using and what order they should be used in.
If you’re looking for real-world problems to solve, go on Kaggle, which has a huge number of data sets to play with, and practice pulling useful information out of them. For example, if you’re looking at a data set for food recipes, align the data set with local food prices to find all of the recipes that create meals for under $5. When you’re ready for a real challenge, try Kaggle competitions. You’ll find problems to solve and companies willing to pay. These challenges will be incredibly difficult to begin with, but you’ll learn a lot discussing solutions with other computer scientists on the forum.
First Python job: Cyber Security Architect
How Bill Got the Job
I had supported Python developers for a number of years as a NASA network administrator and security engineer, so I was aware of the power and flexibility of the language before a new opportunity presented itself.
In 2017, I was approached by a major financial institution to join a team charged with developing a new assessment program to identify monitoring gaps in a particular business process and its supporting applications. I believe they came to me because of my:
- network and security experience
- lack of experience in the financial sector, as they wanted a fresh set of technical eyes on their problem
- ability to tease out what actual requirements are
- ability to approach a new project with an open mind and no preconceived notions.
Funnily enough, and unbeknownst to me, this turned out to be my first Python job.
Our team was expected to triage the gaps, identify possible mitigations, and report our findings to leadership. We began by mapping applications to each business process, but quickly realized that the size of the different data sets we needed to review (application and hardware inventories, Qualys vulnerability scans, daily BladeLogic reports, Splunk logs, etc.) were too large for import into Excel spreadsheets. Furthermore, we didn’t have access to traditional UNIX text processing resources or administrative access to our workstation, where we might have installed any new data management tools. And we didn’t have the budget to purchase new tools.
We did, however, have access to Python, a full set of Python libraries, and the ability to install Python using existing enterprise support software.
I didn’t know Python going in. I had to learn on the job, and good thing I did. Python was critical in our being able to parse hardware inventories based on applications used by the business process, isolate vulnerabilities associated with the appropriate hardware, and identify unauthorized services running on any device that supported one (or more) applications.
My advice to aspiring Python developers is threefold.
First, familiarize yourself with the different libraries available in Python that might assist you in a potential job. Our team used
csv extensively. If you’re looking at a machine-learning project, pay attention to libraries like TensorFlow, Numpy, and Keras.
Next, be on the lookout for processes that need to be automated, or where existing automation can be improved. There’s likely an opportunity for applying Python.
Lastly, have a good Python reference book to supplement all of the online resources that are available. I recommend T.J. O’Connor’s Violent Python.
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