The tech job market is among the most dynamic of markets. The only guarantee is constant change: yesterday's hot topic can become tomorrow's obsolete tool. But top programming languages like Python have so many applications that they simply aren't going anywhere. Python is here to stay.
Among the best roles right now, Full Stack Developers rank high on the list. Many of these skilled pros learn Python along with several other programming languages. Development and data science positions offer job stability in an ever-changing market, and Python paves the way.
This article will cover the main reasons Python should top your priority list. From data science to software development, task automation to data visualization, Python benefits those who need an expert's level of knowledge and those who need only the fundamentals.
Read on to learn more about this essential programming language.
Reason #1: Data Analytics Are CrucialÂ
Expanding career opportunities today often involve data analysis. From marketing to finance, education, and healthcare, more workers need skills that can help them sort, analyze, and use data to make recommendations to key stakeholders.
Data analytics as a career can take many forms. Business Analysts, Financial Analysts, and Database Administrators are a few of the roles that can require gathering, organizing, analyzing, and protecting data. Many of these positions include Python training, whether in-depth or on a fundamental basis.
Today a typical online job board search for Data Analystyields over 20,000 results in the United States alone. These positions include a wealth of varying titles, including:
- HR Business Intelligence Analyst
- Assistant Business Analyst
- Senior Data Management Analyst
- Product Data Analyst
- BI (Business Intelligence) Analyst
Data analysis is crucial to many organizations, whether public or in the private sector. Many of these roles have widely differing requirements. Not all require Python skills, but a high percentage do. Glance at the qualifications for these analysis positions and you'll see that workers typically need multiple programming language skills. Python may head the list, but tech professionals often need R, JavaScript, or C/C++ proficiency.
Variations on the Data Analyst role may include training in Python as part of a broader data analytics curriculum. Qualifications are usually Python-adjacent, like SQL, Power BI or Tableau, and even Microsoft Excel. If you plan to enter a career as a Data Analyst or Business Analyst, you will most likely need a range of skills like these.
Of course, not all data or financial analysis positions require all these skills. Consider the qualifications for entry-level data analysis positions, like an Assistant Business Analyst role. While some organizations demand relevant knowledge in their field, others may only require data analysis experience, accounting skills, and familiarity with spreadsheets. Bringing Python skills to the table in cases like these can make a job candidate stand out from other applicants.
To get the most comprehensive training, consider learning Python as part of a broader data analytics curriculum. For example, a certificate program may include Excel, Python, SQL, and data visualization tools like Power BI or Tableau. A combination like this will equip beginners with the tools to qualify for entry-level data positions in varying fields or sectors.
Another reason to pursue Python for data analysis is the chance for job security. Tech skills like Python and SQL provide countless opportunities in today's constantly changing employment landscape. Data Analysts make an average salary of around $75,000 annually in the U.S., while the average salary for all positions is only about $60,000. Top companies like Meta may pay substantially higher.
The skill set of a data analysis professional can often transfer to diverse roles in other industries or sectors. This flexibility is essential in today's dynamic job market. Cloud computing, data mining, and artificial intelligence are critical skills in nearly every organization today, from startups to multinational corporations. When learners get Python along with these skills in a training program, their chances of advancement multiply.
If you work toward a data analytics career, your ability to upskill in a current role or to a better position grows as you add expertise in Python and related tools. One of the best, most predictable career paths today includes a combination of data analytics and data science. In the next section, you will learn more about how data analysis roles can prepare you for a career as a Data Scientist, Senior Analyst, or Chief Data Officer (CDO).
Reason #2: Data Analysis Leads to Data Science
According to the U.S. Bureau of Labor Statistics, Data Scientists have one of the best job outlooks for the coming decade, with an anticipated growth rate of 36% between 2021 and 2031. Many Data Scientists begin their careers as Data Analysts but upskill to the Data Scientist role by developing more math and programming skills and mastering machine learning (ML).
Data Scientists use Python for a range of tasks. Overall, they prefer it to other programming languages for several reasons, including:
- Ease of learning—Python’s easy syntax (comparable to English) and massive support community make it a no-brainer for data science pros, who have many important tasks besides troubleshooting code
- Range of libraries and frameworks—Python users often prefer NumPy, Pandas, and scikit-learn to other options. Data science pros can learn all these in courses that include Python as part of a broader data-centered curriculum, like bootcamps and certificate programs
- Python ecosystem—The Python ecosystem makes it easy for data science professionals to perform tasks like data visualization, machine learning (ML), or natural language processing (NLP). When team members need to understand the same programming language, it helps if that language is Python
For Data Scientists, Python can assist with so many tasks that it makes no sense to use other alternatives. That's not to say that Data Scientists don't master multiple programming languages. On the contrary, an online search for top programming languages for Data Scientists yields many, including Java, JavaScript, Julia, Scala, R, and C++. But it's no accident that Python tops many of these lists.
While the path to data analytics leads to an average salary of around $75,000 in the U.S., the data science career track is even better. The average salary for Data Scientists is around $127,000 annually, significantly higher than average national salaries.
The general public's understanding of data science is far from adequate. Most people think any role that includes the word scientist in the title must require wearing a lab coat and working with biological materials. On the contrary, Data Scientists are professionals with a deep understanding of computer programming, analytics, and statistics. While their positions may be in biomedical science or healthcare, they are more likely to be found in computer services, management, banking/financial services/insurance (BFSI).
The U.S. Bureau of Labor Statistics lists the following broad categories as the largest employers of Data Scientists:
- Computer systems design and related services
- Management of companies and enterprises
- Insurance carriers and related activities
- Management, scientific, and technical consulting services
- Scientific research and development services
Do such categories mean you should confine your job search to industries like these? Not necessarily. Consider sectors like healthcare, cybersecurity, or marketing, which may not be the first industries that come to mind when scanning the list above.
Data Analysts don't always become Data Scientists, although many do. However, there's no doubt that many Data Scientists go on to upper management roles in organizations. That might seem counterintuitive, as data science training doesn't necessarily include management training. But the reason is more simple than it might appear.
Nearly 80% of Data Scientists hold a graduate degree, most in computer science or business administration and management. Whereas some individuals have received degrees before employment, many attend college or university part-time. When organizations look around for a newly-created Chief Data Officer position, their best Data Scientist may be at the top of their list. So the career track of a motivated data professional may go from Data Analyst to Data Scientist, then to Data Science Manager or CDO.
Reason #3: Software, Web & Game Development with Python
Python is well-known as a pillar of data science and analysis. However, the need for Python proficiency has continued to grow in software, web, and game development.
The Bureau of Labor Statistics (the BLS) reports an anticipated growth rate of 25% over the next ten years among Software Developers, Quality Assurance Analysts, and Testers—a category that includes Python Developers. Significantly, they recorded a median annual salary of $122,000 for Software Developers in 2021.
Python for development can relate to innumerable careers across every industry and sector, public and private. Some positions have the word Developer in the title, but many others do not. Among the most common examples are:
- Software Developer
- Software Engineer
- Web Developer
- Python Developer
- Python Engineer
- Back-end Developer
- Application Architect
Consider first the general category of software development. The BLS lists software publishers as the top industry for Software Developers, closely followed by manufacturing and BFSI (Banking, Financial Services & Insurance).
Among the highest-paid Python pros are Software Engineers. These skilled tech professionals may be Systems or Applications Engineers, but both require in-depth training. Software Engineer qualifications may include multiple programming languages, like Python, Java, JavaScript, and C/C++. They may spend time creating SQL queries and developing SQL scripts as part of the software development life cycle. Many need to be involved in the process from inception to ongoing monitoring.
Another high-paying role for Python professionals is that of Data Engineer. Like Software Engineers, Python is among the many skills these tech pros need to master. Cloud services, automation, and industry-related experience may be essential for the best roles in this growing field.
Web Developers, especially Back-end Developers, often need Python proficiency as well. They typically need to be versed in security protocols and web server frameworks, Python, PHP, Java, and C/C#.Â
Python for web development is a broad field in itself. Popular frameworks like Django, CherryPy, and Flask give Web Developers a great deal of latitude and the ability to be creative when developing websites. Household name brands like Spotify, Yelp, and Reddit used Python frameworks like these to power their websites. Other well-known companies using Python include Dropbox, which hired Python creator Guido van Rossum to work with them; Instagram, which uses Python's Django framework to handle its back-end; and Bank of America, whose Quartz system depends on Python codes and scripts.
Game development is an area where Python isn't always the top language required. JavaScript, C++, and C# are popular for game development but don't count Python out. Game Developers who want to refine designs quickly may prefer Python for these tasks, and Pygame is one of the most popular sets of Python modules for game creation. Top games built with Pygame include Frets on Fire, Metin2, and Flappy Bird.
With all these considerations in mind, you might find it difficult to decide whether to pursue game, software, or web development, and even more challenging to land a job in the field. Nothing could be further from the truth. A targeted bootcamp or certificate program that features Python, or includes it as part of a broader development curriculum, can help novices narrow their many choices to a few.
One final word about development roles: as noted above, Python development positions do not always mention Python or development in the title. When searching for online listings in the field, consider alternative verbiage like the following:
- Engineer
- Architect
- Computer Science
- Artificial Intelligence (AI)
- Machine Learning (ML)
Whether you study Python for back-end development or learn it as part of a software engineering curriculum, your expertise can take you far in this exciting, ever-changing field.Â
Reason #4: Data Visualization: a Tool for Every Business
Data visualization is no longer a mere tool in the arsenal of a Business Analyst or Data Scientist. In many cases, it's become a career in itself. Titles like Senior Data Visualization Analyst and Data Visualization Specialist now regularly appear on popular job boards like Indeed, LinkedIn, and ZipRecruiter. Data pros today may need intermediate or advanced data visualization expertise for multiple reasons.
When it comes to data visualization training, Python often plays an essential role. Search online for top programming languages for data visualization, and you'll find Python at the top of many lists. While some tech pros prefer JavaScript or R, Python's popularity and ease of use make it the number one choice for many organizations.
To understand why, it's crucial to know how Data Scientists and other professionals use data visualization. It might sound mysterious, but at its core, data visualization is just a visual representation of information or data. Data visualizations may be charts or graphs and typically use colors and shapes to emphasize trends, exceptions or outliers, and conclusions. In short, data pros use visualizations to present their findings to non-technical people, whether key internal stakeholders or external clients.
Python’s many libraries include quite a few that apply to data visualization. Among the most popular are:
- Matplotlib—Often listed as the number one Python data visualization library, Matplotlib also has numerous other applications, including scripts, shells, and Jupyter Notebook
- Seaborn—Seaborn works well with the popular Pandas library and is listed in the top three data visualization libraries on multiple sites. It’s excellent for pie or bar charts and other types of visualizations
- Plotly—Plotly vies with Seaborn and Matplotlib as the top choice for Python data visualization. It boasts features like multiple viewing options and high-level visualizations
- Ggplot—Based on the ggplot2 R visualization library, this Python library also works well for high-level data visualizations. Plotting in ggplot typically requires Pandas and Plotnine packages
- Geoplotlib—Geoplotlib is essential for visualizations that require maps and other geographical data. It requires NumPy and Pyglet installation to run. Many data visualization pros prefer the simplicity and performance of this somewhat lesser-known library
Other popular visualization libraries include Pygal, Altair, Missingno, and Folium.
As the library discussion indicates, data visualization with Python can be a complex, multi-faceted subject. However, two popular competing tools often integrate with Python (or without it) for data visualization: Tableau and Power BI.
The various advantages and drawbacks of Power BI and Tableau are beyond the scope of this article, but the essential thing to know is that many data pros use one or the other in their daily tasks. That's especially true for roles like Data Analyst, Business Analyst, or Financial Analyst, which may require Python and data visualization skills.
Developed by Microsoft, Power BI integrates automatically with other Microsoft products like MS Excel and SQL Server. Tableau is more generalized, as you can use it with Windows or MacOS. Whichever you choose—and organizations may dictate one over the other—you can learn it as part of a data science or analysis bootcamp or certificate program.
Reason #5: Python for Artificial Intelligence, Machine Learning, & More
Python is known mainly for web development, data science, and data analysis. But a growing number of organizations use artificial intelligence (AI), and in particular, machine learning (ML), for predictive analysis.
Machine learning is a subcategory of the broader AI category. ML algorithms allow computer systems to identify behavior patterns, which helps data pros turn datasets into predictions. These, in turn, can lead to actionable decisions based on data.
In the U.S., Machine Learning Engineers have become increasingly in-demand as data influence organizational decisions in nearly every industry. Today theaverage base salary for a Machine Learning Engineeris $155,000, more than double the average U.S. wage of around $75,000. And well-known companies like eBay, Meta, and Glassdoor may pay significantly higher salaries for experienced Machine Learning Engineers.
However, it would be a mistake to focus only on the phrase "machine learning" when searching online job boards. A glance at Indeed listings for Machine Learning Engineers yields a range of different titles, including:
- Software Engineer
- Lead Research Engineer
- Director, Product Management-Machine Learning
- Signal Processing Engineer
- Machine Learning Algorithm Developer
- Senior Infrastructure Engineer
Naturally, you must review any job posting thoroughly to determine what level of ML expertise it requires, if any. But the examples above show that not every machine learning position features or even includes the phrase in the title. Furthermore, because ML is a subcategory of artificial intelligence, other titles may mention it, but not ML.
Besides ML, subsets of artificial intelligence that may require Python skills include:
- Artificial Neural Networks
- Deep Learning
- Natural Language Processing (NLP)
- Robotics
- Speech & Facial Recognition
Machine learning pros who use Python must typically familiarize themselves with multiple libraries. Consider the following top Python libraries, as some may be essential for your ML role:
- scikit-learn—This open-source Python library offers functions for classification, clustering, and regression algorithms. It is also essential for pre-processing, dimensionality reduction, and model selection
- PyTorch—PyTorch is especially popular for ML and deep learning (DL). Predictive modeling with a library like PyTorch uses statistical algorithms to make predictions
- TensorFlow—While not strictly a Python library, TensorFlow is popular with Python machine learning pros. It is essentially an open-source library for ML and DL, and uses Python or JavaScript
- NumPy—NumPy is short for Numerical Python. The open-source NumPy Python library is essential in ML roles for data pre-processing and implementing machine learning algorithms
- SciPy—SciPy extends NumPy, and it's appropriate for scientific or technical tasks that include math computations. It's maintained publicly on the GitHub platform
Many tech professionals get machine learning training as part of a broader Python data science or analytics curriculum. Course providers offer bootcamps and certificate programs that include topics like regression analysis, data classification, and decision trees.
Learning Python can be an enjoyable adventure. This programming language is among the easiest to master and has applications across every industry and sector. With its straightforward syntax, scalability, and the growing demand for Python skills in a rapidly-changing tech scene, Python promises to deliver value for the foreseeable future.Â
Whether you need comprehensive training through a bootcamp or certificate or the kind of Python fundamentals you can gain in a video tutorial, there's no denying Python's significance in the tech marketplace.