Python is becoming the preferred programming language in the engineering field for calculations, simulations, and machine learning. Because of its versatility, Python has become the number one choice of coding language for unsupervised learning in engineering projects.
In this Python for engineers tutorial, we take a close look at the applications of this popular language in the field of engineering. We also introduce you to the best avenues to master Python for engineering projects and give you a step-by-step guide to streamline your learning experience.
What Is Python?
Python is an all-purpose and open-source programming language ideal for software engineering. The language’s high-level built-in data structure and large standard library make it the natural choice for developers looking to speed up the application process.
Python’s popularity continues to grow among the scientific community. Due to its versatility and ease of use, this programming language is perfect for simulations, calculations, and unsupervised learning. Many companies now require Python programming skills for engineering positions, so getting acquainted with this language can give you access to new job opportunities.
What Is Python Used for in Engineering?
As engineering projects grow in scope and complexity, coding skills are becoming a prerequisite for many professionals in this industry. Engineers use Python for automation, numerical analysis, data visualization, artificial intelligence, and machine learning. In fact, Python has been described as one of the most useful tools in artificial intelligence (AI) development.
In an industrial setting, technical professionals use Python to simultaneously control hardware and software. For example, they rely on this language to sync industrial projects using an open-source supervisory control and data acquisition (SCADA) system.
How Long Will It Take to Learn Python for Engineering?
Learning the basics of Python can take anywhere from a week to a year. Due to the fact that this is an object-oriented programming language with a syntax similar to that of the English language, Python has a shorter learning curve than other programming languages.
There are many options to learn the basics of Python for engineering or to bring your coding skills further if you are not a complete novice. An online coding bootcamp can help you master basic and advanced topics in a matter of weeks or months with a structured and supervised curriculum. Aside from bootcamps, you can also join online courses or even pick up a book on the subject.
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The average bootcamp grad spent less than six months in career transition, from starting a bootcamp to finding their first job.
Why Should You Learn Python for Engineering?
The engineering field is evolving rapidly. Almost every task can be automated, but this requires expert Python coders and engineers. Employers now favor candidates with coding skills, so learning programming can open many doors in the engineering industry. There are other reasons to consider learning Python as an engineer. We explore them below.
Easy to Learn
Python syntax parallels the English language, making it relatively easy to learn and understand. Compared to other popular programming languages, the learning curve of Python for beginners is relatively short.
Free Online Resources
There are a lot of free online resources to study Python. A growing community of Python users constantly produces free video tutorials, blog posts, and other learning materials. There are also online forums where Python experts will answer your questions if you get stuck during a project.
Required by Employers
Most engineering jobs now list coding skills as a requirement. Coding is now part of the job description, whether you are a mechanical engineer or a software engineer. Even if the particular field or job you are interested in doesn’t require coding, being able to write clean code in Python will always boost your employability.
How Can I Learn Python for Data Science?
You can explore several learning options to develop your Python skills. It all boils down to your needs and availability. Coding bootcamps are a great way to learn Python if you intend to apply for a job, whereas online tutorials and courses may be a better option if you simply aim to familiarize yourself with the basics.
Python coding bootcamps offer structured immersive programs that prepare you for a career as a programmer. If you are an engineer, these bootcamps are ideal to pick up or reinforce your coding skills and help you stand out among your peers in the competitive job market.
At a bootcamp, you’ll learn by doing, taking on real-world projects that put your skills to the test every step of the way. In addition, the best Python bootcamps offer extensive career support. Students have access to expert mentorship and a network of hiring partners, ensuring they can find a job quickly after their program.
There is a host of online platforms that offer Python courses, classes, and tutorials. Taking a Python course from edX, Coursera, or Udacity is a great alternative if you want to learn the basics or are interested in a particular aspect of this language. There are plenty of software development courses that focus on Python and that will teach you its application for projects in engineering.
Finally, there is no shortage of authored books on the subject of Python. This option is for more advanced learners that want to delve deeper into a specific aspect of the language or study its applications in a particular field, such as engineering, data science, data analytics, software development, machine learning, or artificial intelligence.
Top Python for Engineering Libraries
Python libraries save you time by eliminating the need to write code from scratch. Some libraries are best suited for a particular task, such as manipulating data, carrying out simulations, or even building automation models. Below are some of the most common libraries for computational programming projects and numerical computation.
- SciPy. SciPy is an open-source library used for scientific, technical, and numerical computing. It can be used for optimization, linear algebra, integration, image and signal processing, and interpolation.
- Scikit-learn. This is one of the most important libraries for machine learning algorithms, particularly for unsupervised and supervised learning. You can also use it for data mining and data analysis, as well as scientific computation.
- Theano. Theano is a powerful library that can handle multidimensional arrays efficiently through GPU and CPU optimization. It is used for error testing, verification, and diagnosis.
- TensorFlow. This library is ideal for deep learning and neural networks. As the name suggests, it is helpful for running and defining tensor computations. TensorFlow is excellent for generating neural networks, which are essential for AI application development.
- Pandas. The Pandas library is used for data cleaning and manipulation and also for statistical analysis. It is one of the most popular libraries in the Python ecosystem.
- Matplotlib. Matplotlib is a Python data visualization library that is excellent for generating charts and graphs. Engineers can use it to create bar charts, scatterplots, box plots, and line graphs.
Professional engineers can use many other Python libraries for building machine learning models and performing data manipulation. There are more than a thousand of these libraries, many of which are open-source.
How to Learn Python for Engineers: A Step-by-Step Guide
Every person attempting to learn Python can use some guidance. We have prepared a step-by-step guide to help you in the process of learning this critical programming language so that you can acquire the skills you are after quickly and efficiently.
Step 1: Install Python
The first step in your learning language is to download Python on your computer. Downloading this popular language is totally free. Simply go to the official site and download the latest version, Python 3.
Step 2: Configure Your Programming Environment
Now, it is time to set up your programming environment, which will be composed of a text editor and a Python implementation. The text editor is where you write your code, while the implementation provides support for the execution of programs written in Python.
We recommend downloading an integrated development environment (IDE) with test runner, code highlighting, and syntax checking. There are many types of IDEs that you can install, but PyCharm is the most common one. PyCharm is open-source, free, and compatible with all major operating systems.
Step 3: Learn Python Basics
Before diving into more advanced topics, it is crucial to get yourself familiar with the basics of this language. There are the essential terms to know:
- Strings. A string is a sequence of characters. Strings are created by enclosing characters in quotes.
- Sets. Sets are mutable, unordered data types that have no duplicate elements. They are used to store multiple items in a single variable.
- List. Lists are created by adding elements or items separated by commas inside a square bracket. A list may contain different types of items.
- Tuples. Tuples are a data type in Python used to store data structures in an ordered value sequence. They also store multiple items inside a single variable.
- Dictionaries. Dictionaries are unordered, mutable containers that store keys. They are written in curly brackets. Each is paired to a value separated by a comma. A colon separates the key from its value.
Step 4: Study Machine Learning Libraries
Pandas and NumPy are important if you are interested in exploratory data analysis. You can start with NumPy and continue with Pandas, which is considered an extension. The Numpy library allows you to work on highly optimized multidimensional arrays. Pandas, meanwhile, is great for data wrangling.
Step 5: Acquire Data Visualization Skills
Learning data visualization is the first step toward performing any statistical analysis. Matplotlib is popular for basic data visualization. It allows you to quickly and easily plot charts and graphs to better understand the nature of your data.
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Step 6: Learn Databases
Learning how to retrieve information from databases is a useful skill in a wide range of fields, including engineering. SQLite is a popular database management system that, when combined with Python, simplifies data retrieval.
Start Learning Python for Engineering Today
To stay relevant in the engineering field, you need to acquire coding skills. Given its versatility and engineering applications, Python is the obvious choice for engineers looking to advance professionally. Whether you seek to become a machine learning engineer or a software engineer, Python should be on your radar.
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