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In the last 10 years, Android has made a name for itself, not only with its candy-themed platform updates, but also with its widespread, and unexpected, success. In its lifetime, the open-source Android operating system has grown to include 1.4 billion active users and 80% of smartphones today run Android software. Over 1 billion Android phones were sold in 2014 alone.
Mobile app development in the programming community is the minority – just over 9% of total developers in the world say they’re focusing on mobile devices, according to Stack Overflow’s 2015 developer survey. Of these mobile developers, however, Android developers make up the larger group, with 44.6% self-identifying as Android developers, compared to 33.4% who say they are building for iOS. Even so, many companies struggle to find enough developers with the technical skills to complete their Android projects. This trend is likely to continue as the overall number of smartphone users – and Android users, specifically – continues to grow.
The truth is, no one is born with the coding skills a potential employer is seeking for tech job openings, and your resume doesn’t need to include a computer science degree. The good news is every web developer acquires the technical skills along the way that leads to their first job, and the path is not the same for everyone. With this dream tech job becoming a desired career path for many, you’ll want to have a proper outline. Below are my 5 steps to getting your first job as a web developer.
1. Research and Visualize
If you came across this article because you were researching how to get a job as a web developer, guess what? You’re already immersed in the first step! Congratulations.
In one of my favorite business books, The 7 Habits of Highly Effective People, Stephen Covey outlines an easy-to-understand approach to attaining your goals — through conscious changes to your behavior and character. While all of his 7 habits are important, I want to point out habit 2 to you with regards to landing a web job: Begin with the end in mind.
In the book, Covey says that habit 2 is “based on imagination — the ability to envision in your mind what you cannot at present see with your eyes.” The idea is “based on the principle that all things are created twice,” he continues. “There is a mental (first) creation, and a physical (second) creation. The physical creation follows the mental, just as a building follows a blueprint.”
In essence, he says that everything that we bring into reality in the future has its start somewhere before, in the mind. It could be an idea, a goal, or even a Google search; suddenly “it” becomes an outline, a map or a schedule, and slowly the first mental birth gives a subsequent existence to something physical.
When it comes to your career as a web developer, this means researching the steps you can take today in order for you to be confident and capable to start a new developer job in the future. That’s how I’d like you to approach this goal.
Do your research: The better you know the blueprint for what you want to build for your web career, the more motivated you’ll remain along the way. You’ll be able to track your progress toward a goal. And then visualize: See yourself follow the plan as you grow your skills and confidence and ultimately land your dream job.
Key Takeaways:
Read 3–5 different takes on how to become a web developer from tech and career blogs, YouTube, Quora, LinkedIn, etc.
From the different perspectives you’ve gained, what is common among them? What do they say you should learn for a web developer job? How do you acquire the skills that are suggested?
Make a step by step plan on how you can achieve the goal. Base it on your personal skills profile, schedule, timeline, resources, etc.
Visualize yourself 1, 3, or 5 years in the future while focusing on what’s doable today.
2. Learn the Skills
The bottom line is that a web developer job comes down to skills and execution. Employers — your future hiring managers and colleagues — will expect you to have certain technical skills and to execute them as required by the role, especially if you want to keep the job. While soft skills are important in any organization, in this job you’ll ultimately find yourself being put to the test through your tech experience and knowledge, and your goal is to let this fuel your productivity and success.
When I started to learn web development, I realized I had a lot of new things to learn. Coming from an arts background in college but having a life-long fascination and familiarity with computers, I knew that my English writing would not directly translate to writing code. So what did I need to do? Thankfully I was in familiar territory; I had to learn to write and to learn new languages. That meant learning new writing syntax, structure, rules, etc. From the start, I learned HTML and CSS were the backbone of the web. HTML gives structure to every webpage’s content, and CSS provides the styling. Without those two, what would the web be? Would it be?
While I was working a full-time job that was not tech-related at the time, I made a schedule for myself: at least 3 times a week I would go to a learning space or coffee shop after work to learn HTML and CSS. Yes, I visited many spaces, libraries, and coffee shops. I drank a lot of needless coffee. I researched many topics. I stumbled and learned. One night, it could take me three hours to learn a simple concept which only became a rabbit hole to other related concepts. It never seemed to end and I only had so much time in one day.
In the long run, every hurdle mattered. Once I learned something a couple of times, I started to retain knowledge, and through practice, I put it to work.
Bonus Tip: One thing I always tell my web development students is this: find a project. It can be anything — start a website about your dog, your kids, your most recent vacation, Auntie Betsy, the weather — whatever. It doesn’t matter. It doesn’t even have to be public. Just create something.
Having a pet project, even simple ones as those mentioned above, will give you a needed end-goal. You’ll start to come across hurdles as you envision what content is needed, how the website should look, how to host it, and what design you want, such as fonts, colors, assets, user experience, template layout, etc. Guess what? Each of these web hurdles and design choices may become your three hour session at a learning space, but each obstacle will reward you with practice and knowledge.
Key Takeaways:
Learn HTML and CSS. Interactive online platforms provide an excellent start, and they can be reinforced by coding bootcamps. If by chance you come from a graphic design background, you’ll enjoy CSS! You might soon see yourself as a web designer.
Learn how to write a simple HTML document from memory. The syntax will become engrained more thoroughly this way. Yes, type out the <html>, <head>, <title>, <body> and other common HTML content tags so that they become second nature. Learn how to integrate CSS into the document in-line, and through a linked stylesheet. Speaking confidently about the technical structure of an HTML + CSS page alone can help you score big in a job interview.
Explore JavaScript and front end web design, which will start molding you into a front end developer. What are the limits of HTML and CSS, and how does JavaScript help compliment them on the web? How can it improve your projects?
Explore dynamic websites to understand how a web application interacts with a database, and research different programming languages you can learn: JavaScript, Java, Python, PHP, Ruby, etc. What popular frameworks or content management systems can you explore to get started? Ultimately, this can lead to a full stack developer role.
Lastly, how do you get to be online given the framework or CMS? How does web hosting work? How do you deploy a web application?
3. Add to your Portfolio and Gain Experience
Once you’ve created one or two simple projects for yourself — as long as they provide a good dose of learning and experience — create something that you want to share publicly. Can’t think of anything? A personal blog or a professional resume website is a good start.
When you’ve created a website that you can share with others, get the word out. Add it to your social media profiles, including LinkedIn. Don’t be afraid to market it, and of course, improve it as new ideas and content start to flow.
My first public web project was a short story fiction website. Because I came from an arts background, it was actually my interest in creative writing that brought me to tech. I had owned a web domain for a couple of years (the “idea”), but it wasn’t until I was determined to create my own website that I took the steps to build it.
While I enjoyed writing and publishing short stories, through the project, I continued learning about web publishing, platforms and upkeep. I was public about my project and in time I had guest writers’ content on my site, and family and friends reading it. Before I knew it, I had family and friends asking me if I knew how to make a website. “Oh, you’re opening a business and want to know if I can help with the website? I’d love to”, I’d say.
At first, I built websites for family and friends, and then used that experience to get the attention of third-degree references. Ultimately, I started to freelance and gained contract work. I created professional business websites for small businesses and individuals. In the span of two years, I had decent freelancing experience, a nice group of samples from my creations, professional references, and the confidence to apply to full-time opportunities.
Key Takeaways:
Put to practice what you’ve learned. If you haven’t already, purchase your first domain. Choose a CMS or framework to get started.
Host and publish content, improve it, maintain it, and share it.
Add the site to your social media profiles, in the links or projects section.
Be open to freelance work with friends and family. Help someone improve their website. Provide web advice and guidance. Work on making someone else’s dreams come true through your new web skills.
Let one project breed the next. You’ll be amazed how many people and businesses need help online. After a handful of contract projects, you can start considering yourself a freelance junior web developer.
4. Be Part of a Community
Depending on the languages, frameworks, or CMS you choose to work with, be part of that community. Your presence can be digital and physical. Subscribe to pertinent tech blogs and tutorial websites on the subject to stay fresh; join online communities of tech professionals where you can learn and give help (e.g. Stack Overflow, WordPress.org); join an offline meet-up; attend a conference, coding bootcamp, and hackathon. This community experience will improve your coding skills, allow you to network, increase your chances of more freelance projects, and make you look more experienced to a potential employer.
Because I knew very little at first, I initially gravitated toward WordPress — an open source CMS. While I wasn’t programming at first, I learned the fundamentals of a front-end developer (HTML, CSS, JavaScript) and then back-end languages such as PHP and SQL. I subscribed to WordPress blogs, joined WordPress.org where I participated in forums, took tutorial after tutorial, listened to tech podcasts, and attended WordPress events. All of this helped me during the job search for not only freelance clients but also my first web development job. My first job ultimately included leading the development of event websites for a health industry corporation, and soon after, I also started to teach WordPress to others!
If you decide to start making simple websites with a CMS that is not open source, such as Shopify, Squarespace, or Wix, that’s fine too. As long as you can practice HTML and CSS at first, it will springboard you into other challenges. Eventually, I gravitated toward PHP, Python and related frameworks, and I became involved in those communities as well.
Key Takeaways:
Where are those who use your CMS or programming language hanging out? Developers tend to find each other online in forums, blogs and community websites. Join, say hello, and start to learn and contribute.
Be open to offline interaction. Look up events near you, or travel if you need to. While developers do enjoy their desks, events are very common. Programming does not have to be a solo experience.
5. Think Like a Recruiter
At this point, feel confident that you’re a junior or experienced web developer candidate for any potential employer, depending on the amount of freelancing you’ve done. Hiring managers will be interested because you have a solid working knowledge of how to make websites, you’ve built your own projects, you’ve helped create or improve others’ sites, and your community involvement shows you’re eager and motivated.
The next thing you’ll want to do is place yourself in the shoes of a recruiter. Whether it’s an in-house recruiter seeking talent for their tech company, or a staffing agency’s tech recruiter looking to find candidates for clients, your resume will not get to the hiring manager unless it first attracts a recruiter.
I landed my first web job through the help of a staffing agency’s tech recruiter who thought I’d be a good fit for a client. After a contract period, I was converted to a full-time employee. You’ll find that this is a common and realistic route.
In your resume, use keywords to be specific about the programming languages you’ve learned, including related frameworks, dependencies, tools, and software. When tech industry recruiters are looking at your resume, they want to make sure they won’t be wasting the hiring manager’s time with someone who doesn’t have specific experience in what’s required. Keywords are also how recruiters will find you.
Be open to templates. Recruiters are open to creative templates and designs as long as the content is strong. Use services such as Canva to find interesting layouts. Don’t forget to add the time you worked as a freelancer, your web links and projects, and any related education you’ve completed. Create different drafts of your resume, and get others’ opinions.
Maintain consistency across job boards and social profiles. Create a profile on 2-5 job boards. LinkedIn should go without saying — make sure the verbiage used on LinkedIn matches what you’ll send a recruiter or hiring manager on your resume. Keep thinking keywords. Consider joining other job boards such as Indeed, Glassdoor, or Monster, which are general job boards that recruiters frequent, or even tech-focused ones such as Hired, StackOverflow, or GitHub.
Be active online. Share others’ content and consider creating content of your own, such as videos, articles, and podcasts.
On the job boards, select that you’re open to being contacted by recruiters, but be subtle. Be open to a junior web developer job. Stay attentive and courteous even to opportunities that don’t align with your expectations: you never know which recruiter or employer may be the right match now or down the line.
The Opportunity is Yours
When you land a call or an in-person interview, be honest about what you know and don’t know, and be confident about what you can bring to the table. Always come prepared.
Once you have an interview, the opportunity is yours. There’s one thing left to do: ace the interview with the belief that you can walk through that door of what was once a visualization.
When you think about a new career in coding, what comes to mind? Do you imagine working behind the scenes at a movie studio or fashion house? How do you imagine the remote job lifestyle? The deep satisfaction of improving a life-saving medical device? The systems and engineering mindset needed to build the dashboard controls for a new smart car?
Maybe you’re thinking of a coding job in a workplace where projects are different every day, like an ad agency building websites for global brands. You may even have an app idea that could change the landscape if only you knew how to make it. Whatever you think a coding career might be, you’re probably right — unless you think it’s boring.
Of course, every job has its boring moments, as well as stressful ones. The good news is, once you become a proficient coding specialist coder, you can begin to explore and decide on the types of coding projects that will help you thrive for years in your new career. The idea is to choose a career path that’s the right fit for your particular working style; it doesn’t necessarily have to be a programming job.
The variety of industries that hire programmers and developers is endless, from the energy industry to retail operations to manufacturing to social causes. The software your dentist uses to view your X-rays; the app that you use to order takeout; the computer in your car that lets you know your coolant is low; the playlist that syncs your phone to your home audio system — all of these cool innovations were made possible by teams of professional coders.
Remember, understanding a computer language, and writing code are not the only programming skills out there. The individuals who built those solutions to everyday challenges have lots of different titles, from web developer to mobile developer to software engineer, or even data scientist. They all work with code in their ways and have their career paths, with their obstacles and rewards. With so many career options that stem from a shared set of programming skills, the last thing the coding field could be called is boring. The real question: is coding the right fit for you?
Do you like learning new things?
Neuroscience reveals that our brains have something in common with technology: neither our brains nor tech are fixed but are instead constantly changing and evolving.
Experienced senior developers are constantly studying to learn new coding skills, as new programming languages like Python become widely used, and new applications are found for existing fields like machine learning.
Fortunately, job seekers don’t need a computer science degree to start a new programming career or learn a programming language. Many coders and developers are self-taught, using free or low-cost resources available at their local libraries or online such as Stack Overflow. Some seek out learning opportunities and coding experience at their current jobs, like volunteering to help maintain a business website or install a new database. Still, others invest in themselves by signing up for a coding bootcamp with live instruction, real-time code critiques, and built-in networking opportunities.
In the end, the programmers who are most successful in this field are the ones who continuously upskill and stay current with new developments in tech. What does this mean for you? It means that a demonstrated commitment to lifelong learning and a growth mindset as a computer programmer can be the key characteristic that sets you apart from other candidates for that first junior developer job!
Are you good at solving puzzles?
Can’t get enough of jigsaw puzzles, riddles, and crosswords? Your ability to quickly see patterns and solutions where others do not is a quality that could serve you very well in a software development or computer programming career. Successful engineers and developers have an excellent eye for detail, an essential skill in a field where a single misplaced bit of punctuation can stop an elite billion-user app dead in its tracks.
Is it stressful? Not for you, because you thrive on pursuing solutions when others have given up and find it deeply rewarding to help a team resolve wicked issues that no one could fix alone. Every bug is an interesting coding challenge, and every update a chance to make something good into something even better.
Are you a musician?
If you think composing and arranging music is fun, you’re likely to find programming to be fun as well, and a good fit for your skills. Studies show that playing music can help people learn more quickly and create more elegant and creative solutions to complex problems. Trained musicians and successful coders tend to share specific core competencies: a good memory for details, the ability to sort and prioritize an incredible amount of information, and the skill to recognize and tweak patterns. A musician with programming skills can be a great team asset, proficient in both creativity and code. There are even coding courses and workshops designed especially for musicians. Who knew?
Every musician understands the importance of practicing scales before you play your first concerto. In a line of work like programming, a great way to learn is to practice writing bits of code over and over, then begin to string those bits together in sequence until you’ve composed something wonderful and new.
Consider what makes you thrive in a workplace. There will be stressful days and boring days in whatever field you choose, and to stand out in any field requires hard work. But if finding patterns, solving puzzles, or taking small perfect bits and then using them to craft something larger and much more complex sounds enjoyable to you, buckle up — a new programming career may be exactly the path for you.
If you’re applying for a software engineering position, chances are you’ll encounter some technical interview or coding challenge. For newer engineers applying for software programming roles, the coding interview is often the most terrifying part.
However, with a few interview preparation tips and things to consider, the technical interview will seem a lot less scary and will hopefully be a valuable learning opportunity during your job search. Let’s break down a few helpful tips:
1. Build the hard skills
Get in the habit of regularly doing code challenges. It’s a much more effective way to prepare for coding interview questions than trying to cram a bunch of studying in before the big day.
It’s important to schedule time each day to attempt at least one code challenge. You’ll get better at solving them, and you’ll also get better at outlining your process and speaking to it. A few great websites to help you practice code challenges in varying degrees of difficulty include LeetCode, Codewars, and AlgoExpert.
These code challenges help build the essential hard skills you need to perform well in a coding interview technically. If you’re applying for a mid-level position as a software engineer, you’ll want to feel pretty solid with these types of practice problems in your interview preparation. If you’re gearing up for your first technical interview as a junior engineer, you’ll want at least some exposure and practice with these.
2. Don’t forget the soft skills
Mastery of coding challenges is only half the battle in coding interview preparation, so don’t forget the soft skills. Throughout the entire interview process, including the technical coding interview, there are a lot of things that interviewers are looking for besides your ability to code.
These other skills have to do with how well you communicate your thought process, collaborate, talk about the problem at hand, your leadership skills, your drive to learn, and generally speaking, how nice you are. Soft skills are often overlooked by candidates and can be deal breakers for a lot of coding interviews.
A company that’s worth applying to will want candidates that have strong soft skills, sometimes more so than hard skills, because they show how well a person can grow within the company and develop those hard skills over time. This is especially the case for junior software engineers.
When you practice your code challenges, see if you can buddy up with someone and take turns doing mock interview. Practice talking through the coding problem as you work, asking questions, giving each other hints here and there, and revealing your ability to lead, collaborate, and persevere through the coding test.
3. Acknowledge multiple solutions
This is the “cherry on top” for an interviewer: a candidate that’s not only skilled enough to work through the problem and has a personality that fits the company culture but can also defend their solution and mention alternative approaches. This shows that you’re not just going with what you were taught or what you read online, but that you also acknowledge that there are multiple solutions to the same problem and have considered which is most appropriate for a given context.
As an interviewer administering a coding problem, I would prefer to see the simpler solution over the best solution, as it will give me more time to talk with the candidate. Now, if that candidate can also suggest alternative approaches and defend why they selected theirs, that’s an instant win. Bravo!
An example of this might be a challenge where you’re asked to system design a search function for a video streaming app. You might use an inefficient algorithm for the sake of quick implementation during the job interview, but then mention a more appropriate algorithm that would otherwise be used in real life. Speaking of algorithms…
4. Study your algorithms and data structures
This goes hand-in-hand with the hard skills but deserves its own section. You don’t need to be a master of computer science to ace a coding interview, but there are some standard algorithms and data structures that you should feel good about referencing, or at least mentioning and talking about. For instance:
How does a bubble sort work vs. a merge sort?
What’s the difference between a stack and a queue?
What’s a linked list? What about a hash table?
It’s likely that you’ll be asked one algorithm question in a job interview, so becoming familiar with and being able to speak about them to a degree is a good thing. Cracking The Code Interview by Gayle Laakmann McDowell is a great book covering all of the essential algorithms, data structures, and how to implement and use them in sample code challenges.
The coding interview is an opportunity for you to not only show off your skills as an engineer, but also to demonstrate how well you work with others as a data scientist. It’s designed to simulate what it’s like to work with you on a team. So be yourself, study, know the programming language(s) and practice, take a deep breath, and crush that coding interview!
As a blooming Python developer who has just written some Python code, you’re immediately faced with the important question, “how do I run it?” Before answering that question, let’s back up a little to cover one of the fundamental elements of Python.
An Interpreted Language
Python is an interpreted programming language, meaning Python code must be run using the Pythoninterpreter.
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.
So to run Python code, all you have to do is point the interpreter at your code.
Different Versions of the Python Interpreter
It’s critical to point out that there are different versions of the Python interpreter. The major Python version you’ll likely see is Python 2 or Python 3, but there are sub-versions (i.e. Python 2.7, Python 3.5, Python 3.7, etc.). Sometimes these differences are subtle. Sometimes they’re dramatically different. It’s important to always know which Python version is compatible with your Python code.
Run a script using the Python interpreter
To run a script, we have to point the Python interpreter at our Python code…but how do we do that? There are a few different ways, and there are some differences between how Windows and Linux/Mac operating systems do things. For these examples, we’re assuming that both Python 2.7 and Python 3.5 are installed.
Our Test Script
For our examples, we’re going to start by using this simple script called test.py.
test.py print(“Aw yeah!”)'
How to Run a Python Script on Windows
The py Command
The default Python interpreter is referenced on Windows using the command py. Using the Command Prompt, you can use the -V option to print out the version.
Command Prompt > py -V Python 3.5
You can also specify the version of Python you’d like to run. For Windows, you can just provide an option like -2.7 to run version 2.7.
Command Prompt > py -2.7 -V Python 2.7
On Windows, the .py extension is registered to run a script file with that extension using the Python interpreter. However, the version of the default Python interpreter isn’t always consistent, so it’s best to always run your scripts as explicitly as possible.
To run a script, use the py command to specify the Python interpreter followed by the name of the script you want to run with the interpreter. To avoid using the full file path to your script (i.e. X:\General Assembly\test.py), make sure your Command Prompt is in the same directory as your Python script file. For example, to run our script test.py, run the following command:
Command Prompt > py -3.5 test.py Aw yeah!
Using a Batch File
If you don’t want to have to remember which version to use every time you run your Python program, you can also create a batch file to specify the command. For instance, create a batch file called test.bat with the contents:
test.bat @echo off py -3.5 test.py
This file simply runs your py command with the desired options. It includes an optional line “@echo off” that prevents the py command from being echoed to the screen when it’s run. If you find the echo helpful, just remove that line.
Now, if you want to run your Python program test.py, all you have to do is run this batch file.
Command Prompt > test.bat Aw yeah!
How to Run a Python Script on Linux/Mac
The py Command
Linux/Mac references the Python interpreter using the command python. Similar to the Windows py command, you can print out the version using the -V option.
Terminal $ python -V Python 2.7
For Linux/Mac, specifying the version of Python is a bit more complicated than Windows because the python commands are typically a bunch of symbolic links (symlinks) or shortcuts to other commands. Typically, python is a symlink to the command python2, python2 is a symlink to a command like python2.7, and python3 is a symlink to a command like python3.5. One way to view the different python commands available to you is using the following command:
This special shebang line tells the computer how to interpret the contents of the file. If you executed the file test.py without that line, it would look for special instruction bytes and be confused when all it finds is a text file. With that line, the computer knows that it should run the contents of the file as Python code using the Python interpreter.
You could also replace that line with the full file path to the interpreter:
#!/usr/bin/python3.5
However, different versions of Linux might install the Python interpreter in different locations, so this method can cause problems. For maximum portability, I always use the line with /usr/bin/env that looks for the python3.5 command by searching the PATH environment variable, but the choice is up to you.
Next, we’re going to set the permissions of this file to be Python executable with this command:
Terminal $ chmod +x test.py
Now we can run the program using the command ./test.py!
Terminal $ ./test.py Aw yeah!
Pretty sweet, eh?
Run the Python Interpreter Interactively
One of the awesome things about Python is that you can run the interpreter in an interactive mode. Instead of using your py or python command pointing to a file, run it by itself, and you’ll get something that looks like this:
Command Prompt > py Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>
Now you get an interactive command prompt where you can type in individual lines of Python!
Command Prompt (Python Interpreter) >>> print(“Aw yeah!”) Aw yeah!
What’s great about using the interpreter in interactive mode is that you can test out individual lines of Python code without writing an entire program. It also remembers what you’ve done, just like in a script, so things like functions and variables work the exact same way.
Command Prompt (Python Interpreter) >>> x = "Still got it." >>> print(x) Still got it.
How to Run a Python Script from a Text Editor
Depending on your workflow, you may prefer to run your Python program or Python script file directly from your text editor. Different text editors provide fancy ways of doing the same thing we’ve already done — pointing the Python interpreter at your Python code. To help you along, I’ve provided instructions on how to do this in four popular text editors.
Notepad++
VSCode
Sublime Text
Vim
1. Notepad++
Notepad++ is my favorite general purpose text editor to use on Windows. It’s also super easy to run a Python program from it.
Step 1: Press F5 to open up the Run… dialogue
Step 2: Enter the py command like you would on the command line, but instead of entering the name of your script, use the variable FULL_CURRENT_PATH like so:
py -3.5 -i "$(FULL_CURRENT_PATH)"
You’ll notice that I’ve also included a -i option to our py command to “inspect interactively after running the script”. All that means is it leaves the command prompt open after it’s finished, so instead of printing “Aw yeah!” and then immediately quitting, you get to see the Python program’s output.
Step 3: Click Run
2. VSCode
VSCode is a Windows text editor designed specifically to work with code, and I’ve recently become a big fan of it. Running a Python program from VSCode is a bit complicated to set it up, but once you’ve done that, it works quite nicely.
Step 1: Go to the Extensions section by clicking this symbol or pressing CTRL+SHIFT+X.
Step 2: Search and install the extensions named Python and Code Runner, then restart VSCode.
Step 3: Right click in the text area and click the Run Code option or press CTRL+ALT+N to run the code.
Note: Depending on how you installed Python, you might run into an error here that says ‘python’ is not recognized as an internal or external command. By default, Python only installs the py command, but VSCode is quite intent on using the python command which is not currently in your PATH. Don’t worry, we can easily fix that.
Step 3.1: Locate your Python installation binary or download another copy from www.python.org/downloads. Run it, then select Modify.
Step 3.2: Click next without modifying anything until you get to the Advanced Options, then check the box next to Add Python to environment variables. Then click Install, and let it do its thing.
Step 3.3: Go back to VSCode and try again. Hopefully, it should now look a bit more like this:
3. Sublime Text
Sublime Text is a popular text editor to use on Mac, and setting it up to run a Python program is super simple.
Step 1: In the menu, go to Tools → Build System and select Python.
Step 2: Press command ⌘ +b or in the menu, go to Tools → Build.
4. Vim
Vim is my text editor of choice when it comes to developing on Linux/Mac operating systems, and it can also be used to easily run a Python program.
Step 1: Enter the command :w !python3 and hit enter.
Step 2: Profit.
Now that you can successfully run your Python code, you’re well on your way to speaking parseltongue!
Python is a popular and versatile programming language. But what is Python used for? If you’re interested in learning Python or are in the process of learning how to code in Python, your efforts will be greatly rewarded as there’s so much you can do with it. In this article, we’ll explore the top three major uses for Python.
Before we dive into the uses, let’s briefly discuss why Python has so many uses in the first place. What characteristics does Python have that allow it to be so useful? Python is:
General-purpose: The language was designed to be “general purpose”, meaning it doesn’t have language constructs to force it into a specific application domain. Other programming languages that are general-purpose include (but are not limited to): C++, Go, Java, JavaScript, and Ruby.
Readable: Python is a high-level programming language, meaning it has a higher level of abstraction from machine language and has a simple syntax and semantics (e.g., indentation instead of curly brackets to indicate blocks), which lends to its readability.
Versatile: Python has a large standard library, meaning it comes equipped with a lot of specialized code to handle different tasks. For example, instead of writing your own Python code to read and write CSV files, you can use the csv module’s reader and writer objects. In addition, there are many open-source libraries and frameworks that provide additional value for Python programmers — especially those in machine learning, deep learning, application development, and game development — and scientific computing will find an ample supply of libraries and modules.
What is Python used for? There are so many different tasks that Python can accomplish. You can use it to build recommender systems, create cool charts and graphs, build restful APIs, program robots, conduct scientific computing, manipulate text data or extract text from images; the list goes on and on.
The best way to think about uses for Python is through the most active and popular disciplines that rely on Python programming:
Artificial intelligence and machine learning
Data analysis and data visualization
Web development
1. Artificial Intelligence and Machine Learning
What it is: Artificial Intelligence is a concept that’s more or less the idea of machines or computers that mimic human cognitive functions such as “learning” and “problem-solving.” Activities like driving a car, playing chess, and answering a question are all structured, logic-based things that humans can do that are being implemented by computers today. At the heart of this activity is machine learning, which is the process that a computer takes to learn the relationships between variables in data so well that it can predict future outcomes (usually on unseen data). If data is the input (“knowledge”), the machines understand the relationships between variables (“learning”) and it can predict what the next step is (“outcome”) — then you have machine learning.
How Python is used: Artificial intelligence requires a lot of data, which in turn requires appropriate storage, pre-processing, and data modeling techniques to be implemented. Deep learning is the intermediary component; it’s the use of specialized models (neural networks) that can handle “big data” at scale. Python is a programming language of choice for the machine learning, deep learning, and artificial intelligence community due to it being a minimalistic and intuitive language with a significant number of libraries dedicated to machine learning activities, which reduces the time required to implement and get results. R is another popular language used by machine learning enthusiasts and practitioners, but Python tends to be more popular because of the number of machine learning and AI-related efforts coming from the tech community, which uses Python. For example, TensorFlow is Google’s AI platform and open-source software library used for machine learning and the creation of neural networks for AI purposes.
What it is: Data analysis is the specialized practice of analyzing data, both big and small, for information and insights. Results of data analyses are often visualized, for the benefit of the recipient, and the tools and techniques used to communicate results visually requires the specialization that is known as data visualization. Data analysis and data visualization are not unique to any industry. It’s better to think of them as process-focused roles than industry-specific roles. After all, every company and industry has its own data to work with. What data analysis is not is the management of data from servers and storage, although some data analysts specialize in data management.
How Python is used: Data analysis and data visualization are specialized roles that can implement Python in ways that are integral to the mission of each role. A data analyst will use Python for data wrangling and data transformation, which is converting data from its raw format to a usable, analyzable format. Then, using open-sourced libraries like Pandas, NumPy, and SciPy, data analysts can manipulate and analyze both numerical and categorical data. In order to visualize data locally, additional libraries such as Seaborn, matplotlib, ggplot, and bokeh, can be used. Some data visualization professionals prefer using Python over business intelligence platforms like PowerBI and Tableau because it’s free, easy to learn, and reduces the need to have to use additional software to create visualizations.
What it is: Web development is a catch-all term for creating web applications and application programming interfaces (APIs) for the web. Web development is a highly specialized role that can be explained by the design pattern known as model, view, and controller (MVC). These terms represent the specialized layers of code of a web application or API. The model involves the code for an application’s dynamic data structure, the view involves the code that directly interacts with the user, and the controller is the code that handles user interactions and works to facilitate input going from the view to the model.
How Python is used: Python has several MVC frameworks that can be used for web application development straight out of the box, and this includes Django, turbogears, and web2py. While a web framework is not required for web development, it’s beneficial to use them as they greatly speed up the development progress. For beginners, learning Python’s syntax and the libraries needed for building a web application or API is a high level of effort, but the alternative would involve a much greater effort, as it would require the knowledge and correct use of multiple programming languages instead of Python.
We’ve explored the major uses for Python, which include machine learning and artificial intelligence, data analysis and data visualization, and web development. If you’re currently learning Python programming, then you’re off to a good start, especially if you’re considering pursuing work in any of the aforementioned areas. For those unsure how to start learning Python, I encourage you to read some of our other posts, which provide more details and tips on how to get started.
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 application 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 the Django web application 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 program code must be run using the Pythoninterpreter.
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. A Python object is very useful for software architecture and often makes 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 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 defined 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 creating 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. Python’s 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 Python 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.
The final answer to the question “What is Python”? Awesome. Python is awesome.
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!
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!
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.
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)_.
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.
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.
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?
