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What is Python: A Beginner’s Guide

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WHAT IS PYTHON?: AN INTRODUCTION

Python is one of the most popular and user-friendly programming languages out there. As a developer who’s learned a number of programming languages, Python is one of my favorites due to its simplicity and power. Whether I’m rapidly prototyping a new idea or developing a robust piece of software to run in production, Python is usually my language of choice.

The Python programming language is ideal for folks first learning to program. It abstracts away many of the more complicated elements of computer programming that can trip up beginners, and this simplicity gets you up and running much more quickly!

For instance, the classic “Hello world” program (it just prints out the words “Hello World!”) looks like this in C:

However, to understand everything that’s going on, you need to understand what #include means (am I excluding anyone?), how to declare a function, why there’s an “f” appended to the word “print,” etc., etc.

In Python, the same program looks like this:

Not only is this an easier starting point, but as the complexity of your Python programming grows, this simplicity will make sure you’re spending more time writing awesome code and less time tracking down bugs! 

Since Python is popular and open source, there’s a thriving community of Python developers online with extensive forums and documentation for whenever you need help. No matter what your issue is, the answer is usually only a quick Google search away.

If you’re new to programming or just looking to add another language to your arsenal, I would highly encourage you to join our community.

What is Python?

Named after the classic British comedy troupe Monty Python, Python is a general-purpose, interpreted, object-oriented, high-level programming language with dynamic semantics. That’s a bit of a mouthful, so let’s break it down.

General-Purpose

Python is a general-purpose language which means it can be used for a wide variety of development tasks. Unlike a domain-specific language that can only be used for specific types of applications (think JavaScript and HTML/CSS for web development), a general-purpose language like Python can be used for:

Web applications – Popular frameworks like Django and Flask are written in Python.

Desktop applications – The Dropbox client is written in Python.

Scientific and numeric computing – Python is the top choice for data science and machine learning.

Cybersecurity – Python is excellent for data analysis, writing system scripts that interact with an operating system, and communicating over network sockets.

Interpreted

Python is an interpreted language, meaning Python code must be run using the Python interpreter.

Traditional programming languages like C/C++ are compiled, meaning that before it can be run, the human-readable code is passed into a compiler (special program) to generate machine code — a series of bytes providing specific instructions to specific types of processors. However, Python is different. Since it’s an interpreted programming language, each line of human-readable code is passed to an interpreter that converts it to machine code at run time.

In other words, instead of having to go through the sometimes complicated and lengthy process of compiling your code before running it, you just point the Python interpreter at your code, and you’re off!

Part of what makes an interpreted language great is how portable it is. Compiled languages must be compiled for the specific type of computer they’re run on (i.e. think your phone vs. your laptop). For Python, as long as you’ve installed the interpreter for your computer, the exact same code will run almost anywhere!

Object-Oriented

Python is an Object-Oriented Programming (OOP) language which means that all of its elements are broken down into things called objects. Objects are very useful for software architecture and often make it simpler to write large, complicated applications. 

High-Level

Python is a high-level language which really just means that it’s simpler and more intuitive for a human to use. Low-level languages such as C/C++ require a much more detailed understanding of how a computer works. With a high-level language, many of these details are abstracted away to make your life easier.

For instance, say you have a list of three numbers — 1, 2, and 3 — and you want to append the number 4 to that list. In C, you have to worry about how the computer uses memory, understands different types of variables (i.e. an integer vs. a string), and keeps track of what you’re doing.

Implementing this in C code is rather complicated:

However, implementing this in Python code is much simpler:

Since a list in Python is an object, you don’t need to specifically define what the data structure looks like or explain to the computer what it means to append the number 4. You just say “list.append(4)”, and you’re good.

Under the hood, the computer is still doing all of those complicated things, but as a developer, you don’t have to worry about them! Not only does that make your code easier to read, understand, and debug, but it means you can develop more complicated programs much faster.

Dynamic Semantics

Python uses dynamic semantics, meaning that its variables are dynamic objects. Essentially, it’s just another aspect of Python being a high-level programming language.

In the list example above, a low-level language like C requires you to statically define the type of a variable. So if you definedd an integer x, set x = 3, and then set x = “pants”, the computer will get very confused. However, if you use Python to set x = 3, Python knows x is an integer. If you then set x = “pants”, Python knows that x is now a string.

In other words, Python lets you assign variables in a way that makes more sense to you than it does to the computer. It’s just another way that Python programming is intuitive.

It also gives you the ability to do something like create a list where different elements have different types like the list [1, 2, “three”, “four”]. Defining that in a language like C would be a nightmare, but in Python, that’s all there is to it.

It’s Popular. Like, Super Popular.

Being so powerful, flexible, and user-friendly, the Python language has become incredibly popular. This popularity is important for a few reasons.

Python Programming is in Demand

If you’re looking for a new skill to help you land your next job, learning Python is a great move. Because of its versatility, Python is used by many top tech companies. Netflix, Uber, Pinterest, Instagram, and Spotify all build their applications using Python. It’s also a favorite programming language of folks in data science and machine learning, so if you’re interested in going into those fields, learning Python is a good first step. With all of the folks using Python, it’s a programming language that will still be just as relevant years from now.

Dedicated Community

Python developers have tons of support online. It’s open source with extensive documentation, and there are tons of articles and forum posts dedicated to it. As a professional developer, I rely on this community everyday to get my code up and running as quickly and easily as possible.

There are also numerous Python libraries readily available online! If you ever need more functionality, someone on the internet has likely already written a library to do just that. All you have to do is download it, write the line “import <library>”, and off you go. Part of Python’s popularity in data science and machine learning is the widespread use of its libraries such as NumPy, Pandas, SciPy, and TensorFlow.

Conclusion

Python is a great way to start programming and a great tool for experienced developers. It’s powerful, user-friendly, and enables you to spend more time writing badass code and less time debugging it. With all of the libraries available, it will do almost anything you want it to.

Final answer to the question “What is Python”? Awesome. Python is awesome.

8 Tips for Learning Python Fast

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It’s possible to learn Python fast. How fast depends on what you’d like to accomplish with it, and how much time you can allocate to study and practice Python on a regular basis. Before we dive in further, I’d like to establish some assumptions I’ve made about you and your reasons for reading this article:

  • You have little to no prior experience learning Python. 
  • You want to know how long it’s going to take to learn Python.
  • You’re interested in resources and strategies for learning Python.

First, I’ll address how quickly you should be able to learn Python. If you’re interested in learning the fundamentals of Python programming, it could take you as little as two weeks to learn, with routine practice. If you’re interested in mastering Python in order to complete complex tasks or projects, or to spur a career change, it’s going to take much longer. In this article, I’ll provide tips and resources geared toward helping you gain Python programming knowledge in a short timeframe.

If you’re wondering how much it’s going to cost to learn Python, the answer there is also, “it depends”. There is a large selection of free resources available online, not to mention the various books, courses, and platforms that have been published for beginners. More on that in a moment.

Another question you might have is, “how hard is it going to be to learn Python?” That also depends. If you have any experience programming in another language such as R, Java, or C++, it’ll probably be easier to learn Python fast than someone who hasn’t programmed before. But learning a programming language like Python is similar to learning a natural language, and everyone’s done that before. You’ll start by memorizing basic vocabulary and learning the rules of the language. Over time, you’ll add new words to your repertoire and test out new ways to use them. Learning Python is no different.

By now you’re thinking, “OK, this is great. I can learn Python fast, cheap, and easily. Just tell me what to read and point me on my way.” Not so fast. There’s a fourth thing you need to consider, and that’s how to learn Python. Research on learning has identified that not all people learn the same way. Some learn best by reading, while others learn best by seeing and hearing. Some people enjoy learning through games rather than courses or lectures. As you review the curated list of resources below, consider your own learning preferences as you evaluate options.

Now let’s dig in. Below are my eight tips to help you learn Python fast.

1. Cover the following Python fundamentals.

At a bare minimum, you (and your resource) must cover the fundamentals. Without understanding them, you’ll have a hard time working through complex problems, projects or use cases. Examples of Python fundamentals include:

  • Variables and Types
  • Lists, Dictionaries, and Sets
  • Basic Operators
  • String Formatting
  • Basic String Operations
  • Conditions
  • Loops
  • Functions
  • List Comprehensions
  • Classes and Objects

If you’re really pressed for time, all of these fundamentals can be quickly explored on a number of different websites: docs.python.org, RealPython.org, stavros.io, developers.google.com, pythonforbeginners.org. See the section below on “Websites” for more details.

2. Establish a goal for your study.

Before you start learning Python, establish a goal for your study. The challenges you face as you start learning will be easier to overcome when you keep your goal in mind. Additionally, you’ll know what learning material to focus on or skim through as it pertains to your goals. For example, if you’re interested in learning Python for data analysis, you’re going to want to complete exercises, write functions, and learn Python libraries that facilitate data analysis. The following are typical examples of goals for Python that might pertain to you:

  • Data analysis
  • Data science and machine learning
  • Mobile apps
  • Website development
  • Work automation

3. Select a resource (or resources) for learning Python fast.

Python resources can be grouped into three main categories: interactive resources, non-interactive resources, and video resources. In-person courses are also an option, but won’t be covered in this post.

Interactive resources have become common in recent years through the popularization of interactive online courses that provide practical coding challenges and explanations. If it feels like you’re coding, that’s because you actually are. Interactive resources are typically available for free or a nominal fee, or you can sign up for a free trial before you buy. 

Non-interactive resources are your most traditional and time-tested; they’re books (digital and paperback) and websites (“online tutorials”). Many first-time Python learners prefer them due to the familiar and convenient nature of these mediums. As you’ll see, there are many non-interactive resources for you to choose from, and most are free.

Video resources were popularized over the past 10 years by MOOCs (massive online open courses) and resembled university lectures captured on video. In fact, they were often supported or promoted by leading universities. Now, there’s an abundance of video resources for various subjects, including programming in Python. Some of these video resources are pre-recorded courses hosted on learning platforms, and others are live-streamed courses provided by online education providers. General Assembly produces a live course in Python that covers Python fundamentals in one week

Below I’ve compiled a list of resources to help you get a jumpstart on learning Python fast. They fall into the categories laid out above, and at a bare minimum they cover Python basics. Throughout the list, I’ve indicated with an asterisk (*) which resources are free, to the best of my knowledge.

Interactive Resources: Tools and Lessons

  • CodeAcademy: One the more popular online interactive platforms for learning Python fast. I know many Python programmers, myself included, who have taken CodeAcademy’s Python fundamentals course. It’s great for beginners, and you can knock it out in a week. It will get you excited about programming in Python. 
  • DataCamp: Short expert videos with immediate hands-on-keyboard exercises. It’s on-par with the CodeAcademy courses. 
  • *PythonTutor.com: A tool that helps you write and visualize code step by step. I recommend pairing this tool with another learning resource. This tool makes learning Python fundamentals a lot easier because you can visualize what your code is doing. 

Non-Interactive Resources

Non-interactive resources fall into two sub-categories: books and websites.

Books

In researching books, I noticed a majority of them were actually catered to existing programmers interested in learning Python, or experienced Python programmers looking for reliable reference material (“cookbooks”) or specialized literature. Below, I’ve listed only the books I think are helpful for beginners.

  • Introducing Python, 2nd Edition: This book mixes tutorials with cookbook-style code recipes to explain fundamental concepts in Python 3.
  • Learn Python 3 The Hard Way: 52 well-developed exercises for beginners to learn Python. 
  • Python Basics: A Practical Introduction to Python 3: The website says it all — this book is designed to take you from “beginner to intermediate.” 
  • Python Crash Course, 2nd Edition: This book provides a foundation in general programming concepts, Python fundamentals, and problem solving through real-world projects.

Websites

At first, my list started off with over 20 examples of websites covering Python fundamentals. Instead of sharing them all, I decided to only include ones that had a clear advantage in terms of convenience or curriculum. All of these resources are free.

  • *Google’s Python Class: Tutorials, videos, and programming exercises in Python for beginners, from a Python-friendly company. 
  • *Hitchhiker’s Guide to Python: This guide helps you learn and improve your Python code and also teaches you how to set up your coding environment. The site search is incredibly effective at helping you find what you need. I can’t recommend this site enough. 
  • *Python for Everybody: An online book that provides Python learning instruction for those interested in solving data analysis problems. Available in PDF format in Spanish, Italian, Portuguese, and Chinese. 
  • *Python For You and Me: An online book that covers beginner and advanced topics in Python, in addition to introducing a popular Python framework for web applications.
  • *Python.org: The official Python documentation. The site also provides a beginner’s guide, a Python glossary, setup guides, and how-tos.
  • *Programiz in Python: Programiz has a lengthy tutorial on Python fundamentals that’s really well done. It shouldn’t be free, but it is.
  • *RealPython.com: A large collection of specialized Python tutorials, most come with video demonstrations. 
  • *Sololearn: 92 chapters and 275 related quizzes and several projects covering Python fundamentals that can also be accessed through a mobile app.
  • *Tutorialspoint.com: A no-frills tutorial covering Python basics. 
  • *W3Schools for Python: Another no-nonsense tutorial from a respected web-developer resource. 

Video Resources

Video resources have become increasingly popular, and with good reason: they’re convenient. Why read a textbook or tutorial when you can cover the same material in video format on your computer or mobile device? They fall into two sub-categories, pre-recorded video-courses, and live video courses.

Pre-Recorded Courses

  • Coursera: A large catalog of popular courses in Python for all levels. Most courses can be taken free, and paid courses come with certifications. You can also view courses on their mobile app.
  • EdX: Hosts university courses that focus on specific use cases for Python (data science, game development, AI) but also cover programming basics. EdX also has a mobile app.
  • Pluralsight: A catalog of videos covering Python fundamentals, as well as specialized topics like machine learning in Python.
  • RealyPython.com: A collection of pre-recorded videos on Python fundamentals for beginners.
  • *TreeHouse: A library of videos of Python basics and intermediate material.
  • EvantoTutsPlus: 7.6 hours of pre-recorded videos on Python fundamentals, plus some intermediate content.  
  • *Udacity: Provides a 5-week course on Python basics. Also covers popular modules in the Python Standard Library and other third-party libraries. 
  • Udemy: A library of popular Python courses for learners of all levels. It’s hard to single out a specific course. I recommend previewing multiple beginner Python courses until you find the one you like most. You can also view courses on their mobile app.

Live Courses

  • General Assembly: This live online course from General Assembly takes all of the guesswork out of learning Python. With General Assembly, you have a curated and comprehensive Python curriculum, a live instructor and TA, and a network of peers and alumni you can connect with during and after the course.
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4. Consider learning a Python library.

In addition to learning Python, it’s beneficial to learn one or two Python libraries. Libraries are collections of specialized functions that serve as “accelerators.” Without them, you’d have to write your own code to complete specialized tasks. For example, Pandas is a very popular library for manipulating tabular data. Numpy helps in performing mathematical and logical operations on arrays. Covering libraries would require another post — for now, review this Python.org page on standard Python libraries, and this GitHub page on additional libraries.

5. Speed up the Python installation process with Anaconda.

You can go through the trouble of downloading the Python installer from the Python Software Foundation website, and then sourcing and downloading additional libraries, or you can download the Anaconda installer, which already comes with many of the packages you’ll routinely use, especially if you plan on using Python for data analysis or data science

6. Select and install an IDE.

You’ll want to install an integrated development environment (IDE), which is an application that lets you script, test, and run code in Python. 

When it comes to IDEs, the right one is the one that you enjoy using the most. According to various sources, the most popular Python IDEs/text editors are PyCharm, Spyder, Jupyter Notebook, Visual Studio, Atom, and Sublime. First, the good news: they’re all free, so try out a couple before you settle on one. Next, the “bad” news: each IDE/text editor has a slightly different user interface and set of features, so it will take a bit of time to learn how to use each one.

For Python first-timers, I recommend coding in Jupyter Notebook. It has a simple design and a streamlined set of capabilities that won’t distract and will make it easy to practice and prototype in Python. It also comes with a dedicated display for dataframes and plots. If you download Anaconda, Jupyter Notebook comes pre-installed. Over time, I encourage you to try other IDEs that are better suited for development (Pycharm) or data science (Rodeo) and allow integrations (Sublime). 

Additionally, consider installing an error-handler or autocompleter to complement your IDE, especially if you end up working on lengthy projects. It will point out mistakes and help you write code quicker. Kite is a good option, plus it’s free and integrates with most IDEs.

7. When in doubt, use Google to troubleshoot code.

As you work on Python exercises, examples, and projects, one of the simplest ways to troubleshoot errors will be to learn from other Python developers. Just run a quick internet search and include keywords about your error. For example, “how to combine two lists in Python” or “Python how to convert to datetime” are perfectly acceptable searches to run, and will lead you to a few popular community-based forums such as StackOverFlow, Stack Exchange, Quora, Programiz, and GeeksforGeeks.

8. Schedule your Python learning and stick to it.

This is the part that most people skip, which results in setbacks or delays. Now, all you have left is to set up a schedule. I recommend that you establish a two-week schedule at a minimum to space out your studying and ensure you give yourself enough time to adequately review the Python fundamentals, practice coding in your IDE, and troubleshooting code. Part of the challenge (and fun) of learning Python or any programming language is troubleshooting errors. After your first two weeks, you’ll be amazed at how far you’ve come, and you’ll have enough practice under your belt to continue learning the more advanced material provided by your chosen resource. 

Concluding thoughts

By this point, we’ve established a minimum learning timeline, you know to select a goal for your study, you have a list of learning resources to choose from, and you know what other coding considerations you’ll need to make. We hope you make the most of these tips to accelerate your Python learning!

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3 Reasons Python Programming is So Popular

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Since its introduction in the ’90s, Python has rapidly become one of the world’s most popular programming languages. Most recently, we have seen Python even surpass other languages like Java. How has a humble language like Python managed to gain so much attention? Why is Python so popular?

Some estimates claim there were over 8 million active users of Python by the end of 2018. What has created the demand for this programming language compared to Java with 7.6 million, C# with 6.7 million, and JavaScript with 11.7 million active users at the end of 2018? One way to think about using a programming language is to think about its primary use case. In the case of JavaScript, the primary function is building software for the web or the cloud. Cloud infrastructure and web development are still very common business needs. For C# and Java, these use cases are more driven to desktop application development, which has started to fall off with the rise of the mobile-first mentality of end-users.

1. The rise of analytics and Python.

With Python, the use cases are shifting to data analysis and machine learning. As Clive Humby stated back in 2006, “Data is the new oil.” The bottom line is that data science has a high value. Companies have made data analytics and data science a priority due to their abilities to maximize profits and gain better insights on business. Because of well-developed resources like the data science workhorses of Pandas and Scikit-learn, Python easily does the heavy-lifting of machine learning algorithms.

Along with ready-made tools to do the work, Python is also an incredibly readable programming language. Its syntax was explicitly designed to remove a lot of unnecessary code and emphasize making it human-readable. Python makes the development of complex programs easier to write and easier to manage, which translates directly to the bottom line of the company.

2. Why is Python so popular? One word of many: Free.

The facts that drive Python’s booming popularity: it is an open source and free to use. Developers all over the world are writing and distributing software packages in Python that small companies or individual developers can use in their projects for free. Who wouldn’t want to be able to plug into a sophisticated image segmentation library developed by Google? At no cost! Just a few years ago, similar image analysis software cost thousands of dollars and was not nearly as user-friendly.

3. It takes a village.

Python programming is easy to learn, easy to write, cheap to build with, and massive followings of programmers worldwide. It’s no wonder Python is rapidly gaining in popularity. One of the worst feelings for new developers is not understanding why their program isn’t working, but with Python, the programming and data science communities are very active. Blog posts, answer sites like StackOverflow, and groups on LinkedIn have made getting feedback and solutions to your issues easier than ever. Getting hands-on help with issues quickly, learning, and picking up better development practices are no longer a daunting task.

The best way to learn any new language is to immerse yourself. Popular programming languages like Python are no different. The more time you interact with solving real-world problems with a new language, the faster you can become fluent. There are tons of resources like YouTube videos and blog posts, but I find that there really isn’t a better-suited way to learn than hands-on teaching. You need to raise your hand and ask an instructor attuned to the Python language, programming languages, Python code, data science, python developers, artificial intelligence, programming, and machine learning, and more.

General Assembly: the bridge to machine learning.

The immense rise of use cases and companies hiring developers, allows an increase in places to learn these new skills. General Assembly has a multitude of ways to get you started on the path to learning Python and becoming a Python developer. Informal and free introduction sessions at General Assembly aim to get you running code in just a couple of hours. Part-time classes take things up a notch by giving you focused hands-on lessons twice a week, over 10 weeks — artificial intelligence will have nothing on you. For those future Python developers that are ready to take the plunge, and want a deep-dive into all things machine learning, General Assembly also offers full-time Data Science Immersive programs every quarter to learn Python code, programming, nuances of artificial intelligence — and more.

Why is Python so popular? These reasons are a very good place to start!

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Three Big Reasons Why You Should Learn Python

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As a data scientist, my work is contingent on knowing and using Python. What I like about Python, and why I rely on it so much, is that it’s simple to read and understand, and it’s versatile. From cleaning, querying, and analyzing data, to developing models and visualizing results, I conduct all these activities using Python. 

I also teach data science in Python. My students learn Python to build machine learning models but I’m always excited to hear of the other ways they’ve leveraged the programming language. One of my students told me they used it to web-scrape online basketball statistics just so they could analyze the data to win an argument with friends. Another student decided to expand on her knowledge of Python by learning Django, a popular framework, which she uses to build web apps for small businesses. 

Before taking the plunge into data science, we all had fundamental questions (and concerns) about learning Python. If this sounds like you, don’t worry. Before I started learning Python, I spent several months convincing myself to start. Now that I’ve learned, my only regret was not starting sooner.

If you’re interested in learning Python, I want to share my biggest reasons for why you should. Two of these reasons are inherent to Python; one of them is a benefit of Python that I experienced first-hand, and some of the examples I discuss come from things I have researched. My goal is to give you enough information to help make an educated decision about learning Python, and I really hope that you choose to learn.

1. Python is easy to learn. 

Long before I learned Python, I struggled to learn another object-oriented programming language in high school: Java. From that experience, I realized that there’s a difference between learning to program, and learning a programming language. I felt like I was learning to program, but what made Java difficult to learn was how verbose it was: the syntax was difficult for me to memorize, and it requires a lot of code to be able to do anything.

Comparatively, Python was much easier to learn and is much simpler to code. Python is known as a readable programming language; its syntax was designed to be interpretable and concise, and has inspired many other coding languages. This bodes well for first-timers and those who are new to programming. And, since it typically requires fewer lines of code to perform the same operation in Python than in other languages, it’s much faster to write and complete scripts. In the long run, this saves developers time, which can then be used to further improve their Python. 

One observation I’ve made of Python is that it’s always improving. There have been noticeably more updates to the language in the last 5-10 years than in prior decades, and the updates have often been significant. For example, later versions of Python 3 typically benchmark faster completion times on common tasks than when carried out in Python 2. Every release in Python 3 has come with more built-in functions, meaning “base” Python is becoming more and more capable and versatile.

Learning is not an individual process; often you will end up learning a lot from “peers.” According to various sources, Python has one of the largest and most active online communities of learners and practitioners. It’s the most popular programming language to learn; it’s one of the most popular programming languages for current developers; and among data scientists, it’s the second most common language known and used. All of this translates into thousands of online posts, articles (like this one!), and resources to help you learn.

Speaking of online learning, Python is also tremendously convenient to learn. To learn the fundamentals of Python, there are a lot of learning tools out there — books, online tutorials, videos, bootcamps — I’ve tried them all. They each have their merits but ultimately having options makes it easier to learn. Once you start learning, the resources don’t stop. There are dozens of really good tutorials, code visualizers, infographics, podcasts, and even apps. With all of these resources at your disposal, there’s really no reason why you can’t learn!

2. Python is versatile.

Python’s popularity is also tied to its usability and versatility. According to O’Reilly, the technology and business training company, the most common use cases for python are data science, data analysis, and software engineering. Other use cases for Python include statistical computing, data visualization, web development, machine learning, deep learning, artificial intelligence, web scraping, data engineering, game and mobile app development, process automation, and IoT. 

To get into any of these use cases would require another post. Regardless, you might be wondering what allows Python to be such a versatile programming language? A lot of it has to do with the various frameworks and libraries that have been built for Python. 

Libraries are collections of functions and methods (reusable and executable code) with specific intents; and frameworks more or less are collections of libraries. If you ask any Python developer, they can name at least a half-dozen libraries they use. For example, I often use NumPy, Pandas, and Scikit-learn — the holy trinity for data scientists — to perform math and scientific operations, manipulate and analyze data, and build and train models, respectively. Many Python-based web developers will name Django as one of their preferred frameworks for building web applications.  

While it’s true that libraries are written for most programming languages and not just Python, Python’s usability, readability, and popularity encourage the development of more libraries, which in turn makes Python even more popular and user-friendly for existing developers and newcomers. When you learn Python, you won’t just be learning base Python, you’ll be learning to use at least a library or two.

3. Python developers are in demand.

Many people learn to program to enhance their current capability; others to change their careers. I started off as one of the former but became the latter. Before data science, I built digital ad campaigns and a lot of my work was automatable. My only problem was that I didn’t know how to code. Although I eventually learned how, in the process of learning Python for my work, I was presented with different job opportunities where I could use Python, and learned about different companies who were looking for people experienced in Python. And so I made a switch.

There are a lot of Python-related roles in prominent industries. According to ActiveState, the industries with the most need for Python are insurance, retail banking, aerospace, finance, business services, hardware, healthcare, consulting services, info-tech (think: Google), and software development. From my own experience, I would add media, marketing, and advertising to that list.

Why so many? As these industries modernized, companies within them have been collecting and using data at an increasing rate. Their data needs have become more varied and sophisticated, and in turn, their need for people capable of managing, analyzing, and operationalizing data has increased. In the future, there will be very few roles that won’t be engaged in data, which is why learning Python now is more important than ever — it’s one way to bullet-proof your career and your job prospects.

A lot of top tech companies value Python programmers. For instance, to say that Google “uses” Python is an understatement. Among Google engineers, It’s a commonly used language for development and research, and Google’s even released their own Python style guide. Google engineers have developed several libraries for the benefit of the Python community including Tensorflow, a popular open-source machine learning library. YouTube uses Python to administer video, access data, and in various other ways. Python’s creator Guido van Rossum, a Dutch programmer, was hired by Google to improve their QA protocols. And most importantly, the organization continues to recruit and hire more people skilled in Python. Other notable tech companies who frequently hire for Python talent include Dropbox, Quora, Mozilla, Hewlett-Packard, Qualcomm, IBM, and Cisco. 

Lastly, with demand often comes reward. Companies looking to hire people skilled in Python often pay top dollar or the promise of increased salary potential. 

Conclusion

In summary, there are lots of reasons to learn Python. It’s easy to learn, there are many ways to learn it, and once you do, there’s a lot you can do with it. From my experience, Python programming is a rewarding skill that can benefit you in your current role, and will certainly benefit you in future ones. Even if Python doesn’t end up being the last programming language you learn, it should certainly be your first.

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How Long It Takes to Learn Python

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Python, an essential programming language, has taken the programming world by storm. Much of this attention has followed from the interest in machine learning and AI. Python has become the default programming language of Data Scientists and Machine Learning Engineers all over the world. Python’s versatility has also gained a loyal following not just in computer science but also amongst diverse fields like Bioinformatics, Astronomy, Gaming, and of course, Data Science.

But utility alone doesn’t explain why so many developers love using Python. From its humble beginnings in 1991, Python was designed by Guido van Rossum to be a programming language that emphasized code readability. Or in Guido’s words, “Computer Programming for Everybody.” This ease of human interpretability pairs with an open source ethos that makes it available to developers everywhere for free! So with a few short lines of code you can import packages and libraries that professional developers from companies like Facebook, Google, or AirBnB have spent thousands of hours building _(for free)_.

It’s low-entry cost and ease of reading programs has rightfully garnered Python an immense and passionate following.

1. Where there is talent, there is an opportunity, especially, in Python programming.

Python has rapidly become a deep learning skill that is in high demand within the job market. Jobs sites like Dice and Glassdoor have seen near-exponential growth in postings looking for candidates with Python skills over the last few years because making pivot tables and wrangling data in spreadsheets is no longer enough to get you noticed for data analyst positions. As the variety, velocity, and volume of data has exploded, developers have had to scale their analysis pipelines to match — this means that the people pouring over those numbers must develop a deeper skill set to deal with the enormous amounts of data piling up in their databases.

2. Speed and flexibility are the names of the game!

Python is ideal for handling the heavy-lifting required for today’s computationally intense data analyses used by most businesses today.

OK, so now that you’re sold on its value, how long does it take to learn Python? Like any language, practice and muscle memory are the name of the programming language game. The more time you can immerse yourself, the quicker you will see gains.

It also depends on how much you intend to learn during this process. You can figure out elementary Python and have a simple “Hello World” program running in a matter of minutes, i.e., _Seriously; it is only one line of code!_, etc. To get an understanding of deep learning, a subset of machine learning, or data scientist techniques may take months of focused study, pushing past basic concepts. But, to get your foot in the door as a Data Analyst, it takes about 40–50 hours of studying and practicing — in my computer programming experience.

Some of the rudimentary skills from loading required packages, the underlying data structures, and some simple data manipulation take some effort to put into practice. Remember that learning anything takes motivation and attention, especially when learning a new programming language. With our focus being pulled in many directions at once, sometimes having some guided learning  as a programmer can be a huge help — especially with data analysis and data analysts.

How often have you had a problem you spent hours trying to solve by Googling every corner of the internet, only to have the solution explained to you in three seconds by an expert? You can have industry professionals help guide you through this exciting learning adventure to help make sure you are spending your effort in the right places rather than sift through all the YouTube videos, blogs, or StackOverflow posts.

3. General Assembly Python programming FTW!

Often you get back what you put in. So if you are thinking about getting started on your programming language journey of learning Python, General Assembly has several great ways to get you started.

Free Introduction to Python workshops are held regularly. The aim here is to get you set up to start learning and developing in a couple of hours.

There is a 10-week part-time Python course that give you all the programming language skills you need to start a new career as a Data Analyst or Python Developer for those that are ready for more structured and in-depth learning. These classes are held for two hours, twice a week, over 10 weeks.

For those who like to jump in and learn as much as possible in concentrated, full-time sessions every day, General Assembly offers a 13-week Data Science Immersive as well, which covers all the essentials of putting Python programming into good use for Machine Learning and Data Science.

4. Dive into Python programming + a Python course.

If you are on the fence about learning the programming language Python, I strongly suggest you dive in and don’t look back! I have found the transition from being a Data Analyst in a cancer research lab to becoming a Data Scientist at an InsureTech company, one of the best experiences of my life. All the nerdy things I loved, i.e.,  _(computers, stats, data visualization)_, all banded together in an amazing career path

How long does it take to learn the Python programming language? The answer is your learning path up to YOU. 

Are you ready to start your next chapter and boost your coding skills as a python programmer?

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Getting Started with Sublime Text 3: 25 Tips, Tricks, and Shortcuts

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Sublime Text 3 (ST3) is the latest version of one of the most commonly used plain text editors by web developers, coders, and programmers. It’s available for Mac, Windows, and Linux, and free to download and use.

Make the most of ST3 with the 25 tips and tricks in this ultimate guide for web developers. Learn not only how to use Sublime Text 3, but also about must-have packages, useful keyboard shortcuts, and more.

1. User Preference Settings

By default, ST3 uses hard-tabs that are 4 characters long. This can result in hard-to-read code, as large tabular indents push your work to the right. I recommend all developers add this to their user settings (Sublime Text 3 => Preferences => Settings – User):

  {
    "draw_white_space": "all",
    "rulers": [80],
    "tab_size": 2,
    "translate_tabs_to_spaces": true
  }

This setting converts hard-tabs to spaces, makes indents only two characters long, puts a ruler at the 80 character mark (to remind you to keep your code concise), and adds white space markers. Here is a complete list of preference options if you wish to continue customizing your ST3 environment.
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7 Essential Skills You Need to be an Android Developer

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Building Android applications requires a deep understanding of programming and design. When approaching a new technology for the first time, it often helps to break it down into pieces. If you’re an experienced web developer, many of the concepts and technologies involved in Android app development will be analogous to things you already know – although building apps for mobile devices often requires mastery of a number of more nuanced concepts. Mobile devices have smaller screens, simpler processors, and – in the case of Android – many different manufacturers, meaning that developers need to keep code flexible and account for a variety of user interface scenarios.

So what does it take to become an Android developer? We asked some of the brightest developers in our community – here’s what you need to know.

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5 Reasons You Should Learn to Code

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learning to code

There’s no denying that full-stack web development is one of today’s most sought-after careers. With a median salary of more than $75,000 and demand expected to grow 27% from 2014-2024, according to the U.S. Bureau of Labor Statistics, full-stack web development is a smart career path for many individuals.

But even if you’re not planning on becoming a full-time programmer, learning how to code and having that kind of knowledge and experience can have substantial benefits for your career and further job opportunities. In today’s competitive job market, the smartest workers are those who are able to leverage technology to their advantage — no matter their job title.

Not sure if you want to tackle the challenge? Here are five reasons and benefits of learning to code that will add serious value to your career.

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What Is Front-End Web Development?

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Name: Nick Schaden (@nschaden)
Occupation: Web Designer/Developer

1. In 140 characters or less, what is front-end web development, from your experience?

A mix of programming and layout that powers the visuals, interactions, and usability of the web.

2. If a website were a house, front-end web development would be ______?

Front end development would be the pretty exterior that gives the house character, or the host that invites guests in and makes them feel at home.

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Preparing for an Immersive Coding Program? Don’t Stop at the Pre-Work.

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Over the years, we’ve spent a lot of time thinking about the onboarding experience for students entering our Software Engineering Immersive (SEI) program. SEI is a 12-week, full-time program that gives people the foundation and skills needed to become full-stack web developer.

From 9 am to 9 pm on weekdays, and all day Saturdays and Sundays, students are immersed in code. Because the program is so intense and the learning curve so steep, we, along with other coding immersives (also known as “bootcamps”), advise students to start preparing before they arrive on day one.

Pretty standard is the concept of “pre-work”: 50-100 hours of readings, tutorials, and exercises designed to give everyone a foundation in basic web development concepts, as well as level set the class. At GA, students cover Git, HTML, CSS, and Ruby before starting SEI.

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