Coding Category Archives - General Assembly Blog

Top 5 Coding Languages to Learn in 2023

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The tech industry is booming, and the demand for programmers is increasing every year. There are 26.9 million software developers globally, according to a recent Global Developer Population and Demographic Study. This number is projected to increase to 27.7 million in 2023 and 28.7 million in 2024. 

The world is moving fast, and there are many job openings for coders in 2023. There are many reasons why coding powers the world. The main use cases of coding include software development, data analytics, data science, web development, mobile app development and big data.

If you want to get involved in this exciting field, it’s essential that you learn how to code. But with so many different programming languages out there, where should you start? This blog will look at the top five coding languages that are most widely used today and why they’re so important.

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Looking for a new career? Here are the 3 most promising tech jobs of 2023

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The tech industry experienced mass layoffs and budget cuts in 2022 that have many tech workers who were once flying high worried about job security. Despite the gloomy news, however, the labor market for tech talent continues to be strong. While big tech firms are making headlines for drastic cuts, tech talent remains in high demand across other industries. 

For example, industries like finance, healthcare, government and automotive, all of which have yet to full digitally transform, are hiring tech talent. The City of San Francisco—a verifiable tech hub that should, in theory, have unlimited access to tech talent—recently shared that its vacancy rate for IT workers is 21%. 

For workers willing to look beyond big tech, well paid opportunities continue to abound. At General Assembly, we help people break into a career in tech so they can increase their wages and land a job with great benefits and working conditions—ultimately, improving their quality of life. 

Despite today’s economic landscape, we still believe this is a path to prosperity and that opportunities abound for tech workers. In fact, not having a technical skill set could leave you less secure in your career going forward as everything goes digital. 

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Why Entrepreneurs Should Learn to Code

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Entrepreneurship is on the rise. The pandemic led to a startup boom in the United States, with applications for new businesses increasing by 24%. Then, The Great Resignation accelerated the trend, with applications increasing 55% from 2019 levels in 2021. 

If you’re thinking about jumping on the entrepreneurship train, learning to code might not be the first thing that comes to mind. After all, computer science isn’t typically found in a business school curriculum. 

However, aspiring entrepreneurs should consider learning to code for a few reasons. Learning to code can help you launch your business faster, make better technology decisions, improve your creative problem solving skills, and better understand how your business operates. 

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6 Surprising Celebrities Who Know How To Code & Why You Should Too

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Are you thinking about a career in tech? If so, consider learning how to code. With the mass adoption of the digital era across all industries, businesses big and small are on the lookout for tech-savvy talent. 

According to Forrester’s recent report, the global tech market will experience robust 6% growth in 2023 (significantly faster than pre-pandemic levels). With the tech industry thriving and companies hiring, consider learning in-demand hard skills like coding. 

Coding is the building block of the digital world. It’s the set of instructions designed to make computers perform tasks. Although it may seem daunting initially, coding is much easier to learn than you might think. When learning to code, you have the option to learn many different languages like HTML, JavaScript, Ruby, Python, C++, SQL and many more. After all, celebrities like the ones we’ve listed below have all learned how to code without a dedicated tech career background. 

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Always Be Learning: Real Advice From Software Engineers at Anaconda, Inc.

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Software engineering is one of the fastest growing and highly paid careers, which means many people are vying for a position. Luckily, there’s more than one path to success.  

At its core, software engineering is problem solving. While technical skills are important, technology is always changing. Even seasoned professionals are constantly learning how to do something new. As a result, software engineers come from many backgrounds. Some opt for the traditional route, majoring in computer science in college, while others switch mid-career. 

We sat down with two engineers who work on the development team at our partner, Anaconda, Inc., to learn more about their individual journeys. Ken Odegard took the more traditional, academic route, while Bianca Henderson is self-taught and transitioned to software development later in her career. 

Here’s what they had to share. 

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Beginner’s Python Cheat Sheet

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Do you want to be a data scientist? Data Science and machine learning are rapidly becoming a vital discipline for all types of businesses. An ability to extract insight and meaning from a large pile of data is a skill set worth its weight in gold. Due to its versatility and ease of use, Python programming has become the programming language of choice for data scientists.

In this Python crash course, we will walk you through a couple of examples using two of the most-used data types: the list and the Pandas DataFrame. The list is self-explanatory; it’s a collection of values set in a one-dimensional array. A Pandas DataFrame is just like a tabular spreadsheet, it has data laid out in columns and rows.

Let’s take a look at a few neat things we can do with lists and DataFrames in Python!
Get the PDF here.

BEGINNER’S Python Cheat Sheet

Lists

Creating Lists

Let’s start this Python tutorial by creating lists. Create an empty list and use a for loop to append new values. What you need to do is:

#add two to each value
my_list = []
for x in range(1,11):
my_list.append(x+2)

We can also do this in one step using list comprehension:

my_list = [x + 2 for x in range(1,11)]

Creating Lists with Conditionals

As above, we will create a list, but now we will only add 2 to the value if it is even.

#add two, but only if x is even
my_list = []
for x in range(1,11):
if x % 2 == 0:
my_list.append(x+2)
else:
my_list.append(x)

Using a list comp:

my_list = [x+2 if x % 2 == 0 else x \
for x in range(1,11)]

Selecting Elements and Basic Stats

Select elements by index.

#get the first/last element
first_ele = my_list[0]
last_ele = my_list[-1]

Some basic stats on lists:

#get max/min/mean value
biggest_val = max(my_list)
smallest_val = min(my_list)avg_val = sum(my_list) / len(my_list)

DataFrames

Reading in Data to a DataFrame

We first need to import the pandas module.

import pandas as pd

Then we can read in data from csv or xlsx files:

df_from_csv = pd.read_csv(‘path/to/my_file.csv’,
sep=’,’,
nrows=10)
xlsx = pd.ExcelFile(‘path/to/excel_file.xlsx’)
df_from_xlsx = pd.read_excel(xlsx, ‘Sheet1’)

Slicing DataFrames

We can slice our DataFrame using conditionals.

df_filter = df[df[‘population’] > 1000000]
df_france = df[df[‘country’] == ‘France’]

Sorting values by a column:

df.sort_values(by=’population’,
ascending=False)

Filling Missing Values

Let’s fill in any missing values with that column’s average value.

df[‘population’] = df[‘population’].fillna(
value=df[‘population’].mean()
)

Applying Functions to Columns

Apply a custom function to every value in one of the DataFrame’s columns.

def fix_zipcode(x):
”’
make sure that zipcodes all have leading zeros
”’
return str(x).zfill(5)
df[‘clean_zip’] = df[‘zip code’].apply(fix_zipcode)

Ready to take on the world of machine learning and data science? Now that you know what you can do with lists and DataFrames using Python language, check out our other Python beginner tutorials and learn about other important concepts of the Python programming language.

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:

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 spur a career change, then 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.

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, “Okay, 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 of 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 an absolute beginner, 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 a master Python programmer looking for reliable reference material (“cookbooks”) or specialized literature. Below, I’ve listed only the books I think are helpful for beginners.

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 concepts, 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, 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, a TA, and a network of peers and alumni you can connect with during and after the course.

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 Python 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 learning goal for your study, you have a list of learning resources and learning method 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!

What Is coding?

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Coding is a language, simply put. But that doesn’t stop the mysteries and the global misconceptions that swirl around it. Too often, coding is presented as difficult to understand and needlessly complicated. Why does coding have such a mystique?

Shahzad Khan, one of our lead instructors, breaks it down:

“People think that coding is about sitting in a dark room writing thousands of lines of incomprehensible code. It’s not.” Khan has built a career on breaking down the complicated concepts of coding into easily understood concepts in our Introduction to Coding course, which allows students to dive right into learning programming language. “With the new high-level languages like Javascript and Python, coding is more intuitive and closer to the English language than it has ever been.”

Just like with other languages, once you learn a coding language and how to use the tools of computer science to communicate, a whole new world opens up.

Coders have been known to perpetuate the mythology, though. When they talk about coding, practitioners can sound like proselytizers. They tell passionate stories of how coding has changed their lives — and the world. Famous lines of code have become legendary. Look no further than the Facebook “like” button, an example of how the most consequential code changes people’s behaviors. That’s a lot of power, and it can be intoxicating.

Steve Jobs famously claimed that everyone should learn how to write code because learning how to code teaches you how to think. That may be true, but this definition of coding is still our favorite: Coding is solving real-world problems with existing technology.

And the barriers to entry are relatively low. “Coding is awesome because it allows you to build some amazing things as long as you have a working computer and the internet. No need to go invest in expensive equipment,” says Khan. 

“Software is eating the world, so coding is already extremely important and will be even more so as we progress into the future. The right people who know how to code will save the world.”

The fact is that software is only getting more ubiquitous, finding its way into government and public policy. One look at the United States’ patchwork response to COVID-19, and it’s not hard to imagine how the right software at the right time could have lifesaving implications.

For others, coding is a calling and a way to express creativity — not something you usually associate with computer science. “Creating something is so satisfying, and coding is the ultimate tool to do that,” says Arwa Lokhandwala, one of our lead instructors:  “I love getting my hands dirty trying to learn how to use a particular technology to solve a problem or just creating something for fun.”

Coding isn’t a solitary, head-down endeavor, contrary to those popular misconceptions. We can dispel the image of the glassy-eyed, hoodie-wearing loner right here. “There is a common myth that coders work alone,” Lokhandwala continues. “That’s not true! Coding is a very collaborative role. You have to interact with your team members, designers, product owners, and stakeholders, to name a few.”

“We are entering the Fourth Industrial Revolution where technology will dominate every domain. Currently, people are using coding for everything from detecting diseases to exploring outer space. This is just the beginning. Coding is completely going to revolutionize every industry and give birth to new ones.”

Ready to learn? Enrolling in a coding bootcamp is a great way to learn coding without investing years or thousands of dollars. At GA, a coding bootcamp can be 12 or 15 weeks long and is designed to be a fast-paced learning experience. Students learn and implement quicker than in more traditional courses, and the most successful learn to trust the process. Our Software Engineering Immersive course gives students all the coding skills they need to start job hunting and is Khan’s favorite course to teach. “I love that I get to make an immediate impact in the lives of people who come to learn and want to change their lives for the better.”

Want to learn more about Arwa?

https://www.linkedin.com/in/arwalokhandwala-b831b/

https://www.instagram.com/code.with.arwa/

Want to learn more about Shahzad?

https://www.linkedin.com/in/shahzadkhanaustin/

https://flawgical.medium.com

How long does it take to learn coding?

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How long does it actually take to learn coding? To create a diverse portfolio that wows clients, you’ll want to showcase your talents on varying platforms. But first, you’ll need to assemble your coding toolkit. The most efficient approach for beginners is to pick one programming language and try to master it. So, what can you expect next?

Since everyone’s learning style is different, the time commitment required to learn coding can vary. Some people will pick up a new coding language in days, while for others, it could take months. Taking a course specific to Python or JavaScript will teach you the core concepts of that language and how to write programs in those languages. Expect a bit of a learning curve as you train your mind to think like a programmer. But it’s all part of the process. In our coding courses, you’ll gain broad benefits that set you up for workplace success. You’ll learn best practices, get feedback from peers and experts, build a network, and receive career coaching.

Shahzad Khan, lead instructor and owner of software development and consulting firm Frame of Mind considers coding to be a life-long learning process. “Coding is a way of thinking rather than a thing you learn and implement. Once you understand that, it’s just a matter of practice. Some students will arrive at that “a-ha” moment faster than others.“

For those who can invest more time upfront, Khan recommends the intense learning environment of a bootcamp like our Software Engineering Immersive (SEI), which gives  all the coding skills for full-stack web development. 

“SEI will teach you everything from how to ideate and think about the user to how to implement design patterns and deploy the application to the cloud,” he says. “All that, in a nutshell, is full-stack development. You will learn at least two languages and their respective frameworks. There is also time dedicated to computer science fundamentals, so graduates have a robust exposure to concepts as they interview for their first role as software developers.” 

When Python instructor, Diego Rodriguez, was working as a data analyst, he used coding to get his job done faster. “I was doing many repetitive data analysis tasks, and I knew that if I could code, I could not only get through them quicker, but I could teach others to do the same. I read “The 4-Hour Workweek” by Tim Ferriss, and that shaped my perspective on how to work. I realized that coding would allow me to do more in less time.”

He encourages beginners to start with the fundamentals and apply learning code to a personal project for the most successful — and efficient — approach.

“In as little as two weeks, you can learn enough to take on small projects like creating data visualizations using structured data. If you’re learning with a specific goal in mind, you can focus on accomplishing each step of the workflow using code.”

Rodriguez breaks down just how long it takes to learn the programming language Python here. 

Want to learn more about Shahzad?

https://www.linkedin.com/in/shahzadkhanaustin/
https://flawgical.medium.com

Want to learn more about Diego?

https://www.linkedin.com/in/rodriguezadiego/

How To Learn Coding

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Do you know how to use a computer? Do you have a curious mind? If you answered yes to both, you have everything you need to learn a programming language and become a coder. Coding is very accessible — it’s really that simple.

There are many ways to learn to code, from going it alone on a DIY coding website to scoring a coveted spot in a computer science doctoral program. Learning along with others and from an instructor who is passionate, knowledgeable, and has real-world experience creates our dynamic General Assembly environment. From a bootcamp Immersive to a classic Introduction to Coding, our coding courses are taught by professionals who are industry leaders. Essential is their own love of learning, and they thrive on sharing this with students, often in a collaborative discussion that covers a wide range of coding topics.

Lead Instructor at General Assembly Singapore, Arwa Lokhandwala, is a full-stack web developer and advocate for women in technology through groups like the Women Techmakers Community and Mumbai Women Coders. She describes herself as a coder at heart with a passion for sharing. We trust her guidance on all things coding.

“Anyone with a passion for learning new things can learn how to code, “ says Lokhandwala. “You don’t need a 4-year degree. Familiarity with computer science is good to have, but it’s not necessary; you can learn that as you go along. A lot of companies hire people directly from a coding bootcamp.”

“Bootcamps are inherently intense because there is a limited time period to train, which has its own advantages. The initial days are challenging, but as you progress with the projects you build, the people you interact with, and the things you learn, you will become confident with interviewing and getting the job. If you are just starting out with coding, I would highly recommend a GA Immersive because it gives you a community. Talking to other people who are in the same situation as you can help you get motivated.”

There is no one-size-fits-all, ideal coding student. Students at GA have come from all walks of life, from service industries to liberal arts backgrounds to working on an oil rig. Lokhandwala describes what makes a student successful. “Never giving up. Coding is hard, and nobody gets it on their first attempt. So don’t let your imposter syndrome get the better of you. Keep practicing, and you will get it. Your intrinsic motivation to code has to be stronger than the external motivation in order to create a fulfilling career.” 

Shahzad Khan, one of our lead instructors and owner of software development and consulting firm Frame of Mind, appreciates the experience that students from non-traditional backgrounds bring to his Introduction to Coding course at our Austin, Texas campus. Khan got a degree in philosophy and began studying programming languages as a way to gain acumen after graduate school. “I saw coding as something I needed to learn in a world where we are surrounded by software.” 

His passion for teaching makes his courses popular among returning students.

“I love teaching programming because it forces me to learn every single day and to think about different ways to explain complex concepts. Plus, I get to make some genuine connections with students and inspire them to awesome things.”

Want to learn more about Arwa?

https://www.linkedin.com/in/arwalokhandwala-b831b/
https://www.instagram.com/code.with.arwa/

Want to learn more about Shahzad?

https://www.linkedin.com/in/shahzadkhanaustin/
https://flawgical.medium.com