Artificial Intelligence is seemingly everywhere these days. Recent innovations have peppered the technology throughout our lives, with applications in just about every industry and field. But, if you’ve found yourself wondering what exactly this new technology is, you’ve come to the right place. In this post, we’ll cover what AI is, where it comes from, and how it’s used.
What is Artificial Intelligence?
Artificial Intelligence (AI) is the field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. It involves developing algorithms and systems that can analyze vast amounts of data, recognize patterns, learn from experience, and make informed decisions or predictions.
There are several different terms often used to refer to AI, as the technology encompasses a wide range of technologies and techniques. Commonly used terms include machine learning, natural language processing, computer vision, and robotics.
Machine learning, a subset of AI, enables machines to learn and improve from experience without being explicitly programmed.
Natural language processing allows machines to understand, interpret, and respond to human language. This is the foundation of applications like voice assistants and language translation.
Computer vision empowers machines to analyze and interpret visual data, facilitating tasks like object recognition and image classification.
Robotics combines AI with mechanical engineering, enabling the development of intelligent machines that can interact with the physical world.
Who invented Artificial Intelligence?
Alan Turing
Alan Turing was a pioneering figure in the field of computer science. He made several significant contributions that influenced the development of artificial intelligence (AI). Turing’s ideas and concepts laid the groundwork for AI research and continue to shape the field to this day. As a result, he is often considered its inventor.
One of Turing’s most influential contributions was his proposal of the “Turing test” in 1950. The Turing test is a measure of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. According to the test, if a machine can engage in natural language conversations and convince a human evaluator that it is human, then it can be considered artificially intelligent.
The Turing test became a benchmark for AI researchers, encouraging the development of conversational agents and natural language processing capabilities.
Dartmouth College
AI also has its roots in the Dartmouth Conference held in 1956. It was at the conference when researchers first coined the term “artificial intelligence” and laid the foundation for the field.
Since its inception, AI has undergone significant evolutions, driven by advancements in computing power, data availability, and algorithmic improvements.
Late 20th Century AI
Late 20th century AI experienced an “AI winter” from the late 1970s to early 1990s. It was a period characterized by decreased interest and progress in AI. High expectations, limited computing power, and challenges in solving complex problems contributed to the decline.
The AI winter ended due to advances in computing power, and practical applications demonstrating value, big data availability, improved algorithms, successful commercial products, and interdisciplinary collaborations. These factors renewed interest, leading to the resurgence of AI research and applications in the late 1990s and early 2000s.
Early 21st Century AI
The early 21st-century AI resurgence was driven by the availability of Big Data, which enabled improved algorithms, pattern recognition, real-world applications, and iterative improvement of AI models.
Data Availability The emergence of Big Data provided AI researchers with access to vast and diverse datasets for training and validation.
Enhanced Algorithms: AI algorithms, particularly in machine learning, are improved by leveraging large datasets, leading to better performance and accuracy.
Pattern Recognition: Big Data allowed for the identification of complex patterns and correlations that were previously difficult to uncover.
Real-World Applications: Industries leveraged Big Data and AI to gain insights, make better decisions, and improve operational efficiency.
Iterative Improvement: The feedback loop created by Big Data enabled iterative improvement of AI models through continuous learning from real-world data.
What Does AI Do?
The purpose of AI is to automate tasks, enhance decision-making, improve efficiency and productivity, enable personalization, augment human capabilities, and drive innovation and research.
Automate Tasks
AI automates routine and repetitive tasks, freeing up human resources and allowing them to focus on more complex and creative endeavors.
Enhance Decision-Making
AI helps in making informed and data-driven decisions by analyzing large volumes of information, identifying patterns, and providing valuable insights to support decision-making processes.
Improve Efficiency and Productivity
AI technologies optimize processes, streamline operations, and increase efficiency, leading to improved productivity across various industries and sectors.
Enable Personalization
AI enables personalized experiences by analyzing user preferences, behavior, and data, allowing businesses to tailor products, services, and recommendations to individual needs and preferences.
Augment Human Capabilities
AI complements human abilities by enhancing their cognitive and physical capabilities, enabling humans to perform tasks faster, with higher accuracy, and with reduced effort.
Advance Innovation and Research
AI fuels innovation by enabling breakthroughs in various fields, driving advancements in healthcare, science, engineering, and other disciplines, leading to new discoveries and solutions.
Different Types of Artificial Intelligence
AI systems are categorized based on how generalizable or specific they are (Narrow vs General) or by the way they make decisions (rule-based versus machine learning).
Narrow AI versus General AI
There are different types of AI, ranging from narrow or weak AI to general or strong AI.
Narrow/Weak AI refers to systems designed to perform specific tasks, such as facial recognition or voice assistants, and operates within predefined boundaries.
General/Strong AI aims to replicate human-level intelligence, possessing the ability to understand, learn, and apply knowledge across various domains.
While narrow AI is prevalent today, achieving general AI remains an ongoing challenge, and its development raises ethical and societal considerations.
Rule-Based versus Machine Learning
Rule-based AI, also known as expert systems, relies on predefined rules created by human experts to make decisions or solve problems. These rules are encoded into the AI system, and the system matches input data against these rules to determine the appropriate output or action.
Benefits:
Suited for well-defined domains with known and explicitly defined rules
Limitations:
It may struggle with handling ambiguity or learning from new data
Requires human expertise to create and maintain the rules
Machine Learning AI, in contrast, learns from data without explicit rules, using algorithms that analyze patterns and create mathematical models.
Benefits:
Adapts internal parameters to optimize performance and makes predictions or decisions based on new, unseen data
Excels in complex domains with large amounts of data, discovering intricate patterns and generalizing from examples
Adapts and improves performance over time as new data becomes available
Relies on training data and algorithms to learn autonomously
Limitations:
Need for large amounts of data
Overfitting/an inability to generalize
Potential to duplicate biases present in data
Both approaches have their strengths and limitations, and the choice between them depends on the specific problem domain and the availability of labeled data and expert knowledge.
Oftentimes, both approaches are used at different stages in the life cycle of an AI project.
AI Uses In Industry
Self-Driving Cars (Tesla)
Tesla’s self-driving cars utilize a combination of AI techniques, including machine learning and expert systems.
Machine learning algorithms analyze vast amounts of data from cameras, radar, and other sensors to recognize and interpret the surrounding environment. Expert systems encode rules and decision-making processes, allowing the car to make real-time decisions based on input from sensors and the learned models.
Large Language Models (Chat GPT)
Large language models, like those Chat GPT, primarily rely on unsupervised machine learning techniques, particularly large language models. Engineers train these systems on large datasets of text, enabling them to learn patterns, language structures, and context.
By leveraging deep learning algorithms, the models generate coherent and contextually relevant prompts or responses based on the input they receive.
Editing and Proofreading (Grammarly)
Grammarly uses a combination of expert systems and machine learning approaches to provide editing and proofreading suggestions. Expert systems encode grammar rules, style guidelines, and best practices.
Machine learning algorithms analyze text patterns and linguistic features to detect errors, suggest corrections, and provide contextual recommendations.
Learn To Wield The Power Of AI
While Artificial Intelligence is currently exploding in popularity, it is still considered to be a new field. The rules are still being written, and the first to move often takes an advantage over those late to adapt. At Flatiron School, we’re teaching the skills to help you adapt to the AI revolution.
For enterprise clients, we’ve released entirely new AI training programs. If your organization wants to use AI to work smarter, move faster, and be prepared to innovate with the latest technology, Flatiron School’s suite of AI training programs is just what you’re looking for. Explore our AI training programs today.
For students, each of our programs has been enhanced with AI. We teach our students how to use the power of AI to accelerate their output and results in Software Engineering, Data Science, Cybersecurity, and Product Design and be ready to adapt to the next innovation coming down the pipe.
About Christine Egan
Christine is a Python Developer and Natural Language Processing Engineer, as well as a Senior Data Science Curriculum Developer at Flatiron School. She holds a Bachelor of Arts in Linguistics and Philosophy from Stony Brook University and is also an alum of the Flatiron School Data Science Bootcamp. Before joining Flatiron School’s curriculum team, Christine worked as a consultant for various federal agencies. When not working on Python code, you might find her writing data science articles for Medium, or playing Stardew Valley.
This article on interview prep with AI is part of the Content Collective series, featuring tips and expertise from Flatiron School Coaches. Every Flatiron School graduate receives up to 180 days of 1:1 career coaching with one of our professional coaches. This series is a glimpse of the expertise you can access during career coaching at Flatiron School.
My grandfather was known to say to his kids and grandkids when they received an invitation to interview for a job, “You get the interview. You get the job!” It was his enthusiastic way of communicating his confidence in our abilities. His belief, in part, meant that the hard work put into acquiring the interview (studying, relationship-building, practicing or honing our skills) would also serve us well performing in the interview itself.
There’s no question that the job market has evolved quite a bit since my grandfather’s day. With the introduction of AI assistant tools like ChatGPT, preparing for interviews has become more efficient than ever. If used creatively alongside thoughtful and responsible editing and practice, the following approach can turbo-charge your confidence.
How To Use AI Assistant Tools For Interview Prep
If you’ve been on an interview for a job or been on a first date, you may recall how much preparation can go into such an event. From selecting the right outfit to finding the words to express why you’re interested without coming across as desperate, it can be a lot to think about.
One of the most nerve-wracking parts is preparing to answer an interviewer’s questions that you can’t see ahead of time while weaving in your prior experience and how it will bring value to the organization.
When it comes to interviewing for a job, AI assistant tools like ChatGPT can alleviate this prep work with key entries or “prompts,” a term (and even a new job category of “prompt engineer”) being promulgated by this new technology.
Time to (role) Play!
Before diving into specific interview prompts, it’s helpful to view ChatGPT and other AI assistants like BingAI and Bard as just that – an assistant, or rather, a person. Give the assistant a specific role to play and give it as much information – or context – as it may need to play the role (that of an interviewer in this case) as accurately as possible.
Context is critical in interviewing, whether you’re using AI to help you prepare or a real person like your Career Coach. A company is hiring you not to do just any job and not to solve just any problem. A good company has a very specific problem, has very specific jobs or responsibilities to tackle the problem, and if hiring, is looking for a specific person who has the right experience, skills, and adaptability to help solve the problem.
Put It Into Action With AI
Enter the following prompt into the chatbot to give the AI assistant a role to play and as much context as possible to generate interview questions.
Play the role of a <job title> for <insert company of your choice> (e.g., Spotify) who is hiring for a <insert job title> to join their team. The job description is <paste in the text from the job description on the company’s website>. What questions will you ask the candidate during the 45-minute interview to determine whether or not you will offer them a job to join your team?
Watch as the AI assistant creates a list of questions that have remarkable relevancy to help you begin preparing for how best to respond.
Additional iterations:
In the same chat, enter the prompt “Now condense this list of questions to the top 5 questions.”
If you have a LinkedIn profile of the hiring manager, try entering the text from the profile and regenerating the first prompt to give the chatbot even more context about the role it’s playing as a hiring manager. See if the questions change. What do you notice?
Now, what to do with those AI-generated questions?
With these questions in mind, it’s time to start creating what you might say in response.
You can also use AI assistants like ChatGPT to generate some ideas. And, just like the example above, you’ll want to give ChatGPT as much context as possible to produce the most accurate “like you” types of responses.
Put into Action:
Enter the following prompt into the open chat you started above (i.e., do not start a new chat from scratch) to give the AI assistant a role to play and as much context as possible.
Now play the role of an interview candidate who has been selected to interview for the same open position of <insert job title>. Your resume is <paste into the chatbot the text of your resume> (tip: use your LinkedIn profile if it contains even more details and experience). How would you answer <insert one question at a time from the list above> in 60 seconds?
Watch as the AI assistant creates an answer using the inputs you gave it. What do you notice? Does it sound like your voice? Does it pull in the right experience? What’s missing from the answer it gives you? Should it be longer or shorter?
Additional iterations:
Ask the chatbot to regenerate responses using the STAR method.
Begin a new chat and ask the chatbot to create a 60-second introduction about yourself. Give it different scenarios in which you might introduce yourself (e.g., at the beginning of an interview, at an industry conference, or at a cocktail party).
Use elements of the prompts above to create a follow-up thank you note to the interview that takes place, adding new information you gather from the interview itself (e.g., the questions actually asked of you and any other important or fun details from the interview you want to use to personalize your follow-up).
Rules of Thumb For Interview Prep With AI
When employing an AI assistant, it’s beneficial to follow a couple of key guidelines to achieve the best outcomes. Carefully read the output, maybe 2-3 times, and reflect on several introspective questions.
Personalize & Contextualize
Does the AI-generated response sound like something I would say? Do the responses integrate the right context, accounting for nuances in my experience, and what I’ve learned about the company’s business, culture, and challenges? Not sure? Record yourself reading the response and listen back. What do you notice? What would you change?
Be Honest
Do I fully understand the response it generated? Do I understand the terms and concepts enough to answer follow-up questions reasonably well? Not to worry if the answer is no. Being honest about your responses to these questions will provide a helpful list of topics to prepare yourself confidently for the interview.
Practice and Seek Feedback
Now that you’ve got a solid starting point with your AI-generated questions, it’s time to put those responses into practice. Reading from a script is obvious. Not to mention, it defeats the point. Hiring managers are real people who are still hiring real people, even if AI tools are growing in acceptance in work environments.
First, schedule an interview prep session with your Career Coach. Send them the job description ahead of time along with the questions ChatGPT gave you. Ask them to alter the wording of each question slightly, putting them into new words or in a different order.
Next, put away the responses to the questions from ChatGPT and practice responding in your own words. How does it feel?
Keep practicing after your coaching session, recording your responses with tools like Loom or Riveter to listen and give yourself feedback. Send the recordings to your coach or a friend for additional feedback.
The more practice we have relaying our experience and skills – hard and soft – to a variety of interview questions, the more comfortable we become when under pressure. Once you’re comfortable with 70% of the questions, practice the tougher questions at least a couple more times for good measure, and then take a break. Let your brain do work while you sleep, and rest easy(ier) knowing you’ve put in a solid effort preparing to ace your interview!
No job interview yet? No problem!
Repurpose the above prompts to fit prep work for an upcoming informational interview or networking conversation. Or, ask the chatbot to help you create an outreach message requesting an informational chat.
In whichever scenario you choose to employ an AI assistant, remember that it’s merely a tool and those who use tools thoughtfully, responsibly, and creatively often create impressive results.
About Lindsey Williams
Lindsey Williams is the Senior Manager of Coaching at Flatiron School. She has more than 15 years of experience in the tech and edtech spaces and has held a variety of roles from Recruiter and HR to Campus Director and Training Director.
Are you considering a career change into the exciting and rapidly growing field of cybersecurity?
If so, you’re not alone.
The Bureau of Labor Statistics expects a 35% increase in cybersecurity employment opportunities from 2021 to 2031. This is far higher than the 5% average growth rate for all occupations. This surging demand for trained cyber talent has largely been driven by the acceleration of data breaches in both quantity and complexity over the years, the rapid expansion of data privacy laws, and society’s increasing reliance on unsecured systems and network infrastructure, among other factors.
According to Cyberseek, an initiative funded by the National Initiative for Cybersecurity Education (NICE), there are 755,743 unfilled cybersecurity job openings in the US as of April 2023. This represents a massive 140% increase from January 2019 when there were 314,000 unfilled openings. Of those 750K+ openings, 58% (441,972) request certifications, and 13.5% (101,725) specifically request the CompTIA Security+ certification.
Why are certifications important in the cybersecurity field?
Let’s take a look at the value propositions of security certifications (like CompTIA’s Security+) and why they’re in such high demand.
First, certifications validate baseline skills and increase a candidate’s visibility in the job market. Some certifications serve as an endorsement of your competency in technologies and tools provided by a particular vendor.
Next, pursuing certifications demonstrates a passion for continual learning and excellence in the field. Experienced cybersecurity professionals often seek certifications to add value to their teams, customers, and stakeholders.
Lastly, certifications play an important role in career advancement and upskilling. If you’re looking for a promotion or to land more advanced roles, the appropriate certification, when combined with hands-on training, can significantly increase your chances.
Certifications can also help meet basic requirements for cybersecurity and information assurance roles within the government, Department of Defense, and associated contractors. The CompTIA Security+ certification meets the DoD 8140/8570.01-M requirements for IAT-Level II, opening the door to a variety of exciting pathways.
So what makes the CompTIA Security+ certification so valuable?
According to CompTIA, the Security+ certification validates the core knowledge required of cybersecurity professionals and emphasizes vendor-neutral, hands-on practical skills. This ensures that security professionals are better prepared to problem-solve a wider variety of today’s complex issues.
Simply put, no. Certifications are not an instant ticket to success in cybersecurity. In a research report, The Life and Times of Cybersecurity Professionals 2021, ESG and ISSA surveyed 489 cybersecurity professionals and found that “only 1% of respondents believe security certifications are more important than hands-on experience. Alternatively, 52% believe that hands-on experience is more important […] while 46% place equal value on hands-on experience and certification achievement. […] Based upon this data, aspiring and advancing cybersecurity professionals should take a balanced approach to skills development.”
At Flatiron School, our industry-leading Cybersecurity Engineering curriculum is aligned with domains covered by the Security+ certification while providing over 500 hours of class time and hands-on experience through lab-based training. By combining Flatiron School’s rigorous curriculum with the pursuit of certifications like Security+, our graduates are well-equipped to enter the job market with confidence.
In conclusion…
Certifications are a valuable addition to your professional tech identity in cybersecurity. They demonstrate a passion for continual learning, increase visibility in the job market, validate skills, and aid in career advancement. The CompTIA Security+ certification is valuable due to its emphasis on vendor-neutral, hands-on practical skills. Flatiron School’s immersive and balanced curriculum, when supplemented with the Security+, is the foundation for success in the cybersecurity field.
About the Author
Scott Bowman, the Senior Manager of Career Coaching for Cybersecurity at Flatiron School, is a top cybersecurity coach and advocate. In 2023, he was recognized as one of the Top 15 Coaches in Denver by Influence Digest for his dedication to bridging the talent gap in the tech industry. With a comprehensive background in career services and a Master’s degree in Higher Education Leadership, Scott has over 5 years of experience launching successful cybersecurity careers with SecureSet and Flatiron School. Scott leads a robust Career Coaching team, empowered by an immersive technical curriculum, that equips new career changers and upskillers with the tools and confidence to thrive in the cybersecurity industry. His dedication, knowledge, and unique perspective make him an invaluable leader in the field.
Enhancements teach students how to effectively leverage artificial intelligence (AI) tools, giving them a competitive edge in the job market.
Flatiron School is excited to announce that we’ve launched curriculum enhancements designed to teach students how to leverage powerful, emerging artificial intelligence (AI) tools.
The curriculum enhancements affect each of our four disciplines – software engineering, data science, cybersecurity, and UX / UI product design – and come at a time where AI requirements are increasingly appearing in job descriptions. For the past 10 years, we have been committed to keeping our curriculum modern, aligning with the ever-evolving tech landscape. Now, as new AI tools emerge, we continue to lead tech education in providing students with the skills needed to thrive in today’s job market.
“Even as AI reshapes the industry, we expect demand for tech talent to remain strong,” said Kate Cassino, Flatiron School CEO. “The most competitive talent will have the skills needed to unlock the full potential of AI, and that’s why now is the time to invest in our students. Our latest curriculum enhancements empower students with the skills needed to not only excel in an evolving job market, but also to advance the future of tech through innovation.”
Curriculum Enhancements
The enhancements cover topics such as machine learning, natural language processing, and prompt engineering. Students will learn how to use generative AI as a research tool, effectively communicate with AI chatbots, build AI models, and leverage machine learning for testing.
Curriculum enhancements include:
Software Engineering. Discover how AI can help with code debugging, enable code completion, and integrate AI features into apps. Students will also learn prompt engineering and how to successfully correspond with chatbots.
Data Science. Learn the fundamentals of AI theory, including concepts such as data leakage, overfitting, and regularization. Students will also learn scikit-learn (a popular AI library for Python programming language) as well as how to build AI models for text data (also known as natural language processing, or NLP).
Cybersecurity. Gain experience leveraging AI and machine learning (ML) for threat intelligence feeds, vulnerability and penetration testing, and detecting anomalous and behavioral events. Students will also learn how bad actors could leverage AI to crack encryption.
UX / UI Product Design. Explore how to co-create with AI for inspiration, as well as how to analyze potential biases in AI-generated design solutions. Students will also learn about the implications of AI on intellectual property, how to develop user journeys with generative AI, and how to use NLP to automate identification of patterns in user data.
Taught by instructors with real-world experience, our courses are offered online or in-person on New York City or Denver campuses. Students can choose full-time or part-time programs, which are paced between 15 and 60 weeks. Upon graduation, we provide up to 180 days of 1-on-1 career coaching.
To learn more about our curriculum enhancements for students, please click here.
This article on tech verticals is part of the Content Collective series, featuring tips and expertise from Flatiron School Coaches. Every Flatiron School graduate receives up to 180 days of 1:1 career coaching with one of our professional coaches. This series is a glimpse of the expertise you can access during career coaching at Flatiron School.
I hear it from early career job-seekers all the time: “I just want a job. Any job is fine with me, I’ll take anything.”
New grads are eager to add professional experience to their resume and increase their skills and their worth in the marketplace so they can advance to their so-called dream job. While this is an understandable mindset when trying to land that first job without relevant professional experience, this can lead job seekers to take jobs they’re bored by in an industry they could care less about.
So how can students find opportunities that interest them?
How To Find Tech Verticals That Interest You
Oftentimes, those who are new to tech are simply not familiar with many companies outside of the big names – Google, Apple, Amazon, etc. Unless you are a regular consumer of tech journalism and publications that focus specifically on technology innovation and the companies and start-ups in those spaces, it’s easy to be unaware of the rich opportunities that exist outside of these big name-brand firms.
One of the best things you can do when you begin a job search is to carefully consider the myriad of industry verticals that exist in the marketplace and target two or three. Then, investigate and select a few on which to focus on building relationships with professionals in those industries. Many grads are surprised to learn that the market is segmented in surprising and interesting ways.
For example, have you ever heard of the PetTech vertical? How about the Sweat Industrial Complex? I meet lots of Flatiron School grads who are into fitness, outdoor activities, pets, and sports. No matter what you’re passionate about, there are likely several tech companies in a vertical that focus on it.
Sweat Industrial Complex
Are you a self-professed gym rat? Do you spend lots of time getting your fitness on, know all the latest health trends, or routinely counsel your family on the importance of healthy habits? There are lots of tech companies in the vertical known as the “Sweat Industrial Complex” that focus on fitness, health, and overall wellness that you could channel your passion into!
Tonal – a digital weight training system
Hydrow – an indoor rowing machine with live and on-demand workouts
Mirror – a digital fitness screen that streams live and on-demand workouts
Tempo – an all-in-one home gym with AI-powered guidance
Myx Fitness – a digital fitness platform that includes a bike and other equipment for on-demand workouts
FightCamp – a connected boxing workout system
Echelon – a digital fitness platform with a range of equipment options for on-demand workouts
The Sweat Industrial Complex market is expected to grow at a compound annual growth rate (CAGR) of 15.9% from 2021 to 2028, according to a report by Grand View Research. The market was valued at USD 32.63 billion in 2020 and is projected to reach USD 104.39 billion by 2028.
Pet Tech
As of 2023, 66% of U.S. households (86.9 million homes) own a pet. That’s a lot of pet lovers. If you’re among them, why not join a company with the goal of making life better for our little (and sometimes not so little) lifetime companions?
Here are some examples of companies in the PetTech space:
Rover – an online platform connecting pet owners with dog walkers, pet sitters, and doggy daycares.
Whistle – produces GPS pet trackers that allow pet owners to track their pets’ whereabouts and activity levels.
Embark – offers DNA testing for dogs to provide insight into their breed, health, and ancestry.
Petcube – produces interactive pet cameras that allow pet owners to monitor and interact with their pets remotely.
Wisdom Panel – offers DNA testing for dogs to identify their breed, ancestry, and potential health risks.
FitBark – produces fitness trackers for dogs, allowing pet owners to monitor their pets’ activity levels and health.
Scratchpay – offers pet financing options to help pet owners pay for veterinary care.
Petnostics – produces at-home urine testing kits for pets to help pet owners monitor their pets’ health.
Tractive – produces GPS pet trackers and activity monitors for dogs and cats.
Vetcove – provides a centralized platform for veterinarians to purchase medical supplies and equipment.
According to a report by Grand View Research, the global pet tech market size was valued at USD 4.5 billion in 2020, and it is expected to grow at a compound annual growth rate (CAGR) of 21.2% from 2021 to 2028, the pet tech market could potentially be worth over USD 20 billion by 2028.
Clean Tech / Green Tech
In recent years, the world has come to realize the full extent of climate change and humankind’s impact on the environment. As a result, dozens of green tech companies have popped up in recent years to minimize and reverse the effects.
Here are just a few companies in the Clean / Green Tech industry:
CarbonCure Technologies – develops technology to reduce the carbon footprint of concrete production.
Cool Planet – creates sustainable biochar and other products from agricultural waste.
Ecovative Design – creates eco-friendly packaging materials using mycelium (the root structure of mushrooms) as a sustainable alternative to Styrofoam.
Pivot Bio – creates nitrogen fertilizer for crops using microbes, reducing the need for traditional, energy-intensive fertilizer.
Sistine Solar – creates solar panels that blend into their surroundings.
Xpansiv – develops blockchain-based technology to track the carbon footprint of commodities such as oil and gas.
Solugen – uses biotechnology to create sustainable chemicals and materials.
Brightmark Energy – develops and operates waste-to-energy facilities, converting organic waste into renewable natural gas and other products.
Wexus Technologies – smart metering and water management solutions to help agricultural operations reduce water usage.
EnergySage – an online marketplace for solar panels and energy-efficient home upgrades, making it easier for homeowners to go green.
According to a report by Allied Market Research, the global cleantech market size was valued at $1.32 trillion in 2020 and is expected to reach $4.61 trillion by 2028.
Education Tech (EdTech)
Education is going digital. With all of mankind’s collective knowledge hosted on the internet, access to the tools needed for a quality education are no longer gated behind physical institutions charging a hefty fee for admittance.
Spurred along by evolutions in remote learning during the pandemic, here are some EdTech companies changing the way students and adults learn:
Quizlet – an online learning platform with flashcards, quizzes, and games to help students study.
Edmentum – personalized online learning solutions for K-12 students.
AltSchool – a network of private schools that use technology to personalize learning and provide real-world experiences.
Lingoda – online language courses with native-speaking teachers and personalized curriculums.
Classcraft – an online gamification platform for K-12 classrooms to promote student engagement and behavior management.
EdSurge – curates news, insights, and research on educational technology and its impact on teaching and learning.
Varsity Tutors – online tutoring and test preparation services for K-12 and college students.
Yellowdig – an online platform for collaborative learning, discussion, and community building in higher education.
Codesters – online coding courses and curricula for K-12 students and teachers.
Articulate – software tools for creating interactive e-learning content and courses.
According to a report by ResearchAndMarkets, the global EdTech market size was valued at $76.4 billion in 2020 and is expected to reach $404 billion by 2028.
EMentalHealth or DigitalMentalHealth
Millions of Americans struggle with mental health at some point in their lives. The amount of people seeking mental health care has trended upward in recent years as cultural taboos around “talking about it” have lessened, resulting in a steep increase in demand that local therapists may not be able to meet.
Here are mental health tech companies bridging the gap and helping people who need help, get help:
Talkspace – online therapy and counseling services through a secure messaging platform.
Ginger – on-demand mental healthcare services, including therapy, coaching, and psychiatry, through a mobile app.
BetterHelp – online therapy and counseling services with licensed therapists and counselors.
Woebot Health – a chatbot-based mental health platform that provides cognitive-behavioral therapy and other evidence-based interventions.
NeuroFlow – a platform for remote monitoring and managing mental health conditions, including depression, anxiety, and PTSD.
Meru Health – provides virtual mental health clinics with a team of licensed therapists, psychiatrists, and coaches.
Hims & Hers – provides telehealth services for a range of healthcare needs, including mental health, sexual health, and primary care.
Spring Health – a mental health platform that uses AI and machine learning to match employees with personalized care options.
Tava Health – virtual mental health and well-being services for employers and their employees.
Koa Health – a suite of digital mental health solutions, including self-help tools, therapy, and coaching.
According to a report by Grand View Research, the global digital mental health market size valued at $1.5 billion in 2020 and is expected to reach $8.1 billion by 2028.
How To Pursue A Specific Tech Vertical
When you are familiar with the specifics regarding the vertical you are targeting, you can show up to interviews and networking situations sounding like an insider. Without relevant industry experience, this is one of the best ways to differentiate yourself from other early-career job seekers.
Here’s how to dive deeper into your target tech vertical:
Identify external factors supporting the vertical’s growth
Align your learning with tech stacks and tools used in the tech vertical
Build applications that support some aspect of the industry or solve one of its problems
Identify leaders and influencers in those spaces and engage with them on social media and LinkedIn
Listen to podcasts and read industry articles that are relevant to those businesses
Final Thoughts
When you apply for every job opening that comes your way, you’re playing a numbers game. Playing the lottery is fun, but what are the chances you’ll win?
It’s better to take a targeted approach, focusing on specific tech verticals and aligning your skills and experience with the companies that operate within those verticals. That way you’re more likely to land a job that’s a good fit and exposes you to additional career opportunities in your target tech vertical.
…and it’s still OK to buy a few lottery tickets per week to hedge your bets.
Good luck!
About Dyana King
Dyana King is a career coach with Flatiron School. She previously worked as a technical recruiter and co-founded a technical recruiting agency, Thinknicity. She became a certified professional coach (CPC) in 2012 and specialized in transition and career engagement coaching.
Tech is shaping the world around us and our future. Whether it’s climate change or waste management, tech is solving some of the world’s most pressing problems. To celebrate Earth Day, here are a few ways technology is helping to make the world a better place — both for today and tomorrow.
Track Temperature Trends
Climate change models need millions of data points spanning centuries to identify Earth’s temperature trends. NASA reported that 2022 was tied with 2015 as the fifth warmest on record, and the past nine years have been the warmest years since modern recordkeeping began in 1880. To generate these climate findings, the space agency collects data from 6,300 weather stations, Antarctic research stations, and sea surface temperature observations to determine the surface temperature for a given year.
To make sense of it all, NASA uses data science. “These raw measurements are analyzed using an algorithm that considers the varied spacing of temperature stations around the globe and urban heat island effects that could skew the conclusions,” according to NASA.
Improve Climate Models
Data science can also further improve current climate models. For example, Georgia Tech researchers are creating data mining methodologies that can help scientists identify warming trends. Georgia Tech researchers are already using the methodology to measure sea surface temperatures.
Predict Extreme Weather
Columbia University has a great list of ways artificial intelligence (AI) is being used to predict tropical cyclones and areas of potential poaching and map the damage caused by hurricanes.
Adapt Farming Practices
If you want something more practical for Earth Day, there are apps that can help you live a more sustainable life. Farmers in India, for example, have used AI for fertilizer application and to identify ideal sowing dates leading to a 30 percent increase in groundnut production per hectare.
Make Sustainability Accessible
Oroeco gamifies sustainability. The app helps you track and reduce your carbon footprint while helping you save money. There’s also a leaderboard where you can share your stats across social media.
Promote Conscious Consumerism
TheGoodGuide reviews products by analyzing their ingredients, to help you make informed purchasing decisions.
Reduce Water Usage
If you want to reduce your water usage, there’s the Dropcountr app.
Deputize Citizen Scientists
You can even help NASA track how carbon moves through ecosystems by taking photos of trees.
Climate Change Challengers Are Powered By Tech Skills
Behind these reports, interactive websites, and apps are data scientists, software engineers, and UX/UI designers. Front-end and back-end engineers are working to make websites functional. Data scientists and analysts are making troves of collected information actionable. UX/UI designers are ensuring users can have the best experience possible.
Flatiron School has already helped alumni plant seeds of change in the tech industry. We encourage you to explore our immersive Software Engineering, Data Science, and UX/UI Design programs for Earth Day.
Python is one of the most popular programming languages in software development. A general-purpose programming language, Python is an interpreted, object-oriented, and high-level programming language with dynamic semantics.
Since its creation in the early 1990s, it’s been used to build websites and software, automate tasks, and conduct data analysis by an estimated 8.2 million users.
Despite its current global popularity, Python came from humble beginnings and grew over time into the must-have language in developers’ toolkits.
Python’s Origin Story
Python was created by Guido Van Rossum, a dutch programmer, and released on February 20, 1991. Van Rossum created Python to be a general-purpose tool that could be applied to anything, and to reduce the complexity of coding syntax.
Did You Know: Python was named after an old BBC television comedy sketch series called Monty Python’s Flying Circus.
After releasing the program, Van Rossum further outlined his vision for Python:
To be an easy and intuitive language that is just as powerful as those of the major competitors
The ability for anyone to contribute to its development by being open source
For the language to be easily understandable as plain English
That the language be suitable for everyday tasks and allow for short development times
Python’s rise in popularity ever since is a testament to the fact that Van Rossum succeeded in creating an intuitive, accessible, and versatile language.
Python Popularity Over The Years
When Python was released in 1991, the premier languages at the time were FORTRAN, COBOL, C, and C++. Since the mid-90s, it has steadily been increasing in popularity and overtaking its old competitors.
Stack Overflow’s developer survey results from 2021 had Python as the 4th most popular language according to professional developers. According to TIOBE Index, as of October 2022, Python overtook Java and C as the most popular language.
Learner rates have also skyrocketed, with Python tutorials being the most popular language searched for on Google, according to the PYPL index (Popularity of Programming Language Index), overtaking Java in 2018.
Why Is Python So Popular?
So, why is Python so popular? What does it have that has made it such a runaway success when there are other programming languages available?
Beginner-Friendly
Python was designed to be easy to understand and intuitive. It was created with the intention of people reading code as they do plain English, so it is excellent for beginners and there is a smaller learning curve.
For example, Python learners encounter one of the first exercises: “Hello World.”
Learners simply input the plain English code of print(‘Hello World’), telling the computer to write out (a.k.a. print) ‘Hello World’ on the screen.
Using plain English as the code’s vocabulary helps users better understand what direction they are giving to the computer, and from there increases complexity.
Versatility of Use
Python was designed as a general-purpose language and can be used to create a variety of different programs. It is frequently used to build websites and software, automate tasks, and conduct data analysis.
Active and Supportive Community
Python is an open-source language, meaning that anyone can contribute to the code. Over time this lack of barriers to entry has resulted in an active and supportive community that wants to see Python thrive as a programming language.
There are several frameworks and libraries for different specializations that make using Python a lot easier.
The two main frameworks for web development are:
Flask
Flask is a web framework used to develop webpages. It is considered more Pythonic than the Django web framework – another commonly used option – because in common situations the equivalent Flask web application is more explicit.
This gives control to the developer to create more explicit code to build applications.
Django
Django is a web framework that is easy to use, with a plug-and-play approach to developing applications.
Automation Made Easy
Python can automate tedious tasks such as updating and formatting data in Excel spreadsheets of any size, delivering reminder emails, and sending text notifications.
It is also the backbone of chatbots, which are found on just about every website these days. Chatbots use Natural Language Processing libraries like NLTK and spaCy based on Python that take in hundreds of thousands of sentences and then create new sentences as a response to questions.
Python Works with the IoT
IoT, or the Internet of Things, is a network of physical things embedded with software, sensors, and technologies that connect to other devices – think smart appliances and wearable devices.
Developing IoT devices is often data-intensive, making Python the natural programming language of choice.
Python can be used for programming IoT devices and developing the corresponding backend. What’s more, IoT paired with Python is an effective tool for prototyping, development, and the operation of various IoT devices and systems.
Efficient and Reliable
Because Python is so user-friendly and versatile, developers of almost any level can create powerful applications with minimal effort. With a little bit of skill and creativity, Python can be used to solve a myriad of problems.
Big Companies Use Python and Flask
There are several large and well-known companies that use Python and Flask as their web development language and framework.
These include Samsung worth over $343.35 billion as of January 2023, Netflix, and Uber, just to name the top three. For young developers dreaming of working at tech giants like these, Python is a great place to start.
Which Companies Use Python and Flask
According to Stack Share, there are 1,144 reported companies that use Flask in their tech stacks. This includes big-name brands like Netflix, Lyft, Patreon, and Reddit.
So Why Does Python Popularity Matter?
The rise of Python proves that popularity matters in terms of the amount of documentation, communities, and support for the language. With such a strong community with almost endless tutorials and “how-to’s” made readily available to anyone with an interest and an internet connection, getting started with Python and couldn’t be easier.
Learning Python will set you on a journey into programming, with active communities to support you and employment opportunities at the biggest tech companies.
If you dream of joining the ranks of other software developers working at places like Samsung, Reddit, and Lyft, why not get started learning Python today?
We need to clarify something before answering the question, “What programming language should I learn?” You shouldn’t learn a language, but languages. Sure, you can learn a single language, but you’ll be missing out on an incredible amount of functionality. JavaScript is one piece of the puzzle that really comes to life when you introduce HTML and CSS to web development. It’s also about what you want to do with that language.
You can eat a cake without layers or frosting, but it’s not quite as special. The same goes for programming languages. Different careers also have their own languages you should learn.
Fortunately, learning programming languages is extremely accessible, and if you’re thinking that you need a 4-year degree to get started, well, that’s no longer the case.
Getting started is easier now than ever. Programming is aptitude-based, and all that a company wants to see is your talent level, skills, attitude, and potential.
There are plenty of different languages with very different purposes, though, and plenty of career paths to pursue. That can make starting a tad confusing, but we won’t let that stop us.
So, really, what programming language should you learn?
Below, we compare the best programming languages to learn in 2021 based on average salaries, popularity, job opportunities, demand, difficulty, and your interests. We also will show you how and where to learn them.
So let’s start off.
What are programming languages?
Programming languages, to put it simply, are the languages used to write lines of code that make up a software program. These lines of code are digital instructions, commands, and other syntaxes that are translated into digital output. There are 5 main types of programming languages:
Procedural Programming Languages
Functional Programming Languages
Object-Oriented Programming Languages
Scripting Programming Languages
Logic Programming
Each of these programming language types serves different functions and has specific advantages and disadvantages.
Procedural programming languages
Procedural languages are written as a sequence of instructions. The user declares what to do and also how to do it, and then these instructions are completed in sequential order. Procedural languages are excellent for general purpose programming. Example: Basic, C, Java, Pascal
Functional programming languages
Functional programming is based on mathematical equations and is designed to handle symbolic computation and list processing applications. These languages are especially beneficial when working with big data. Example: Haskell, Scala, SQL, PHP, Go, Rust, Raku
Object-oriented programming languages
These are the most popular forms of programming languages and are based on the concept of “objects,” which can contain data and code in the form of procedures. Many of the programs you use daily are built with these languages because of those languages’ extreme flexibility. They can also be less static and might leave room for more errors. Example: Java, Javascript, Python, C++, R, PHP, Ruby
An example of Python.
Scripting programming languages
Scripted languages are used to create a special run-time environment that automates the execution of tasks. Scripting is great for cutting out time-consuming tasks and automating workflow, but it can take a lot of time to build and test upfront. Example: PHP, Bash, R, Perl, Ruby
Logic programming
Logic programming is a programming paradigm largely based on logic. Logical programs are written as sets of sentences in logical form, expressing facts and rules about some problem domain. Logical programming provides a lot of flexibility but also contains no method of representing computational concepts. Example: Prolog, XSB, ALF
What are some of the most popular programming languages? And what are they used for?
As you probably know, there are many different programming languages, and what you learn should be based on the type of web development you’re interested in pursuing. You should start with a programming language for beginners and then move on to high-level languages as you hone your skills and expand your toolkit.
We are going to cover the most popular languages, what they’re used for, their pros, cons, job demand, and salary range.
As a side note: The chart above shows the relative popularity based on how many GitHub pulls are made per year for that language. This chart and all the charts below are based on data from GitHut 2.0, created by littleark.
JavaScript
JavaScript is the most popular language among developers and is the best beginner-friendly language to learn. Many developers today start by learning JavaScript because of its versatility. It’s often used to build websites, web servers, iOs apps, and other mobile apps. JavaScript is a full-stack language, meaning you can use it server-side as well as in front-end web development. This is a great way to get started programming simply for the fact that you can work on any part of a project using this language.
JavaScript is also very popular because of its simplicity to learn. It’s used everywhere on the web because of its speed as it can be run immediately on a client-side browser. JavaScript meshes very well with other languages, meaning you can use it in a wide variety of applications.
There are very few disadvantages of JavaScript, but no language is perfect. The biggest hurdle with JavaScript is that it’s read differently by each browser, making it somewhat difficult to write code that works perfectly across all browser software.
There is no shortage of jobs for a JavaScript developer. According to Career Foundry, 72% of companies are looking to hire JavaScript developers.
Typical JavaScript roles: Software engineer, front-end developer, full-stack developer // JavaScript developer demand:124k jobs jobs on ZipRecruiter // JavaScript developer salary: $107k per year on ZipRecruiter. The average salary for entry-level JavaScript devs is closer to $71k.
Python
Python is another popular general-purpose programming language that’s very beginner-friendly to learn. While easy to pick up, Python is powerful and versatile, making it great for beginners and experts alike. Python is used by major companies like Google and Facebook, which bodes well for the language’s future.
Python’s immense popularity is due to its wide range of uses. You can use Python for data science, scientific computing, machine learning, analytics, data visualization, animation, interfacing with databases, and web applications. Python’s extreme versatility accounts for its high job demand. Web developers use it, data scientists use it, and so do data analysts and software engineers.
Typical Python roles: Back-end developer, full-stack developer, data analyst, data scientist // Python developer demand:191k jobs on ZipRecruiter // Python developer average salary: $112k per year on ZipRecruiter. The average salary for entry-level Python developers is closer to $82k.
Ruby
Ruby is one of the more popular scripting languages used for web development. It has been used for a lot of tech companies like Airbnb, GitHub, and Shopify, making it a practical language to learn. The Ruby community is robust and its members are helpful, so there is lots of free and helpful information available.
Right now Ruby is especially popular, but one downside to learning Ruby is that its popularity fluctuates. That being said, there is a vast collection of major companies using Ruby, which means jobs won’t be disappearing whatsoever any time soon.
The average salary of a Ruby developer is higher than that of many other languages, especially if you’re familiar with the Ruby on Rails web application framework. Even entry-level Ruby developers usually earn high salaries, making Ruby a very lucrative language to learn.
Typical Ruby roles: Software engineer, back-end developer // Ruby developer demand:6k jobs on ZipRecruiter // Ruby developer salary: $99k per year on ZipRecruiter. Even entry-level salaries are around $98k on ZipRecruiter.
Swift
Swift is a relatively new programming language, but it’s constantly growing in popularity because of its use in iOS and macOS app development. This means if you’re using an iPhone or any other Apple product, the apps you’re using were most likely built on Swift.
Swift is an easy language to learn but might not be the best first language to learn because of its very specific uses. That being said, Swift developers are one of the most in-demand and hardest to fill tech jobs. So if you choose to learn Swift first, there will be plenty of job opportunities for you.
Swift can also be a very profitable language, with salaries increasing greatly in big tech hub cities like San Francisco and New York City.
Typical roles: iOs developer // iOS developer demand:62k jobs on ZipRecruiter // iOS developer salary: $103k on ZipRecruiter
Java
Java is a widely used programming language used mostly in web and application development. It is an older language but Java programmers are still in high demand due to the complexity of the language. It is not the most beginner-friendly language but there are a lot of jobs available for Java programmers.
Some major companies are using Java on the back end, and companies like Airbnb and Google utilize the language quite a bit. Android apps are also often built in Java, and Java is one of the best languages to use for machine learning. This means that there are a lot of options for Java developers in enterprise systems.
Typical Java roles: Back-end developer, full-stack developer, mobile developer, data analyst, data scientist // Java developer demand:177k jobs on ZipRecruiter // Java developer salary: $106k per year on ZipRecruiter
C#
C# is a general-purpose, object-oriented language built on the foundations of C. It was designed by Microsoft as part of its .NET framework for building Windows applications. This is a great language to learn with lots of opportunity surrounding it. Microsoft is the leader in enterprise software, meaning that a lot of companies are using the .NET framework. If you can write C#, there are a ton of high-paying jobs out there for you. C# may not be as popular as some other languages, but it could be. You’ll find that C# developers have a tremendous level of stability and the ability to work on many different types of applications.
Typical C# roles: Mobile developer // C# developer demand:41k jobs on ZipRecruiter // C# developer salary: $104k per year on ZipRecruiter
PHP
While some may tell you that PHP is a dying language, they’re not telling you the whole story. PHP is a server-side language that is used to build websites and is a part of almost 80% of websites across the web.
PHP is also used to build desktop applications and build command-line scripts. It’s at the forefront for anyone who wants to build websites and isn’t a difficult language to learn.
Because of how many sites worldwide run PHP, there will always be a demand for more developers. PHP is a great first language to learn.
Typical PHP roles: Back-end developer, full-stack developer // PHP developer demand:22k jobs on ZipRecruiter // PHP developer salary: $101k per year on ZipRecruiter
HTML & CSS
These will more than likely be the first two languages you touch when learning to program. They are easy to learn and are absolutely essential in building websites. HTML is the core structure of a webpage and CSS is the visual layout for the page. These are the foundations and building blocks of any website.
Chances are you have probably written or seen HTML or CSS before. Many emails, advertisements, and blogs use them, and knowing them can be extremely useful in many aspects of business.
While you may not get a job as a developer with these two alone, every programmer needs to be able to write them. Web designers can get away with only using these two languages, though it’d be a slim skillset. HTML and CSS are essential for any junior web developer position.
Typical HTML & CSS roles: Front-end developer // Front-end developer demand: 46k jobs on ZipRecruiter // Front-end developer salary: $95k per year on ZipRecruiter
Go
Go (sometimes called Golang) is an open-source programming language that makes it easy to build simple, reliable, and efficient software. It is a relatively new language that is growing in popularity due to its simplicity. It is easy to learn and has a modern syntax.
Go is a low-level language ideal for systems programming — it’s compiled and runs close to the metal. Its main uses are for Google applications, large IT companies, and data science.
While it may not be the most popular language at the moment, Go was designed by Google as an alternative to C++ and Java, and job demand for Go programmers will continue to rise with the parent company. This would be a great first language to learn — and training is not hard to come by — and would guarantee some good job security. Being a newer language, you might not need as much experience to get a great job.
Typical roles: Data scientist // Go developer demand:89k jobs on ZipRecruiter // Go developer salary: $110k per year on ZipRecruiter
Elm
Elm is a functional programming language that compiles to Javascript. This is a great choice for front-end developers. Due to Elm compiling to Javascript, web browsers can execute it on a web page.
Elm is domain-specific, meaning it only functions on the client portion of a web application. UI/UX designers love Elm for its multiple libraries — one being Elm/HTML, which allows an Elm programmer to write HTML/CSS within Elm.
This probably won’t be your first language and you’ll need to know more languages to attract employer attention, but having this skill can greatly increase your chances of being a well sought-after front-end developer.
Typical Elm roles: Front-end developer
C/C++
C is a great first programming language to learn because it’s at the root of many other programming languages. C++ is a modern enhanced version of C and is widely used for computer science and programming.
The benefit of C/C++ is that they give developers the ability to use compilers for a variety of platforms, which makes applications written in these languages largely transportable.
Why should you learn this first? Learning C/C++ will make learning most other languages easier and provide you with a much larger skill set than starting with another language. People who know C/C++ will learn other languages easily, whereas knowing another language and trying to learn C/C++, is not as easy of a transition.
Typical C/C++ roles: Mobile developer // C developer demand:175k jobs on ZipRecruiter // C developer salary: $101k per year on ZipRecruiter
Kotlin
Kotlin is a general-purpose, free, open-source programming language originally designed by JetBrains for the Java Virtual Machine. This means that Kotlin is interoperable with Java and supports functional programming languages.
Kotlin is not as popular as Java, but many programmers who use Kotlin are convinced that it’s the superior language. Google would probably agree, seeing that many Google applications are built with Kotlin. Kotlin is also much easier to learn than Java and cuts off 40% of the lines of code you would need to write for the same result in Java.
Should you learn Kotlin? Yes. This is a newer language, having only been around since 2011. As is often the case with newer languages, there is less competition in the job market. It is also in higher demand because there are less experienced programmers focusing on it. You can also expect a high average salary above industry standard.
Typical roles: Mobile developer // Kotlin demand:4k jobs on ZipRecruiter // Kotlin salary: $128k per year on ZipRecruiter
Matlab
Matlab is a language that you probably aren’t familiar with, but it can be a lucrative language if you’re interested in science and engineering. It is used to create machine learning and deep learning applications.
Despite the complexity of machine learning and deep learning, Matlab is actually a relatively easy language to learn. Jobs are not as easily available for Matlab programmers, but the ones you can find pay very well.
MathWorks, which developed Matlab, has great resources about the language.
Typical roles: Machine learning engineer // Matlab demand:5k jobs on ZipRecruiter // Matlab salary: $101k per year on ZipRecruiter
Rust
Rust is a multi-paradigm programming language designed for performance and safety. For these reasons, many major tech companies use Rust.
Rust is ideal for low-level development, much like C++, but it provides a guarantee of memory safety. The demand for Rust programmers has drastically increased over the last few years, and Rust is a very loved language among programmers.
Unfortunately, there are far less jobs available for Rust developers compared to bigger languages like C++, but the ones you can find are usually far more interesting and cutting edge.
Typical Rust roles: Game development, web development // Rust demand:2.7k jobs on ZipRecruiter // Rust salary: $92k per year on ZipRecruiter
If I know what job I want already, then what languages should I learn?
If I want to become a software engineer
Languages you’d learn: JavaScript, HTML, Ruby, CSS
Perfect for: Practical types, tinkerers
What you can do: The ever-reliable engineer. It’s the prototypical job in tech, but it’s a pretty vague term. A software engineer can analyze user needs and use code to create software, fix software, or improve software. Some software engineers call themselves developers and vice-versa
Learning the languages of a software engineer will lead to a lot of opportunities in tech. If you look ahead, you can see these coding languages pop up in other disciplines. For example, it’s not uncommon for an experienced software engineer to have acquired the skills to become a data scientist.
If I want to become a front-end developer
Languages you’d learn: JavaScript, HTML, CSS
Perfect for: Creative types, fans of a great user experience, anyone who wants to create a website
What you can do: HTML, JavaScript, and CSS are the front-facing languages of the web. There’s usually some debate about the best languages to learn for a specific career, but everyone agrees on these three for front-end web development. They work in harmony and together create everything you see on the web.
Every website you’ve visited today is the result of a front-end developer. Everything on the web was created by a front-end developer who worked with a designer and a back-end developer to bring it to life. You’re probably pretty creative, but you also like things that work. As a front-end developer, you can make something functional and is pretty.
If I want to become a back-end developer
Languages you’d learn: PHP, Java, Ruby, Python, SQL
Perfect for: The well-organized, dependable types
What you can do: Back-end developers are the unsung heroes of the web. Your favorite site doesn’t just look great, it performs well. You can also thank a back-end developer for lightning-fast search results from your favorite online retailer.
Front-end developers are using code to create what a user sees on a site and a back-end developer is making that a reality. Back-end developers use PHP, Java, Python, Ruby, and other server-side languages to pull information from a database into an application that’s then returned to the user through front-end language.
Perfect for: Anyone who likes seeing the whole picture, working at a startup
What you can do: Looking at all the languages, you can probably guess what this job is all about. As a full-stack developer, you’re working on the front-end and back-end of a website or app. It’s a great job for anyone who likes to do a little bit of everything and for anyone who likes being involved in a project from start to finish.
You won’t be expected to master all the front-end and back-end languages to be a full-stack developer, so don’t feel overwhelmed. Full-stack developers are a great fit for startups that have a lot of needs, but not a lot of resources. You can work on how a website or app looks and troubleshoot when something goes wrong.
Languages you’d learn: C#, Swift, Java, Kotlin, C/C++
Perfect for: App lovers, people who enjoy a stock Android experience
What you can do: Mobile developers create the apps you use daily. They combine the skills you typically see in front-end and back-end developers to create and launch apps. The only difference is you have a choice to make when choosing what language to learn.
If you want to create iOS apps, you’ll need to learn Swift. Android app developers need to learn Java, but Android apps can also be created with C# and C/C++. Kotlin is a newer programming language that has been gaining popularity and many recommend learning Kotlin to stay ahead of the curve.
If I want to become a data scientist
Languages you’d learn: Python, R, SQL, Java
Perfect for: Anyone who seeks a challenge, early adopters, problem solvers, fortune tellers
What you can do: Data scientists need to learn statistical computer languages and data management systems. There are other data science opportunities that use these languages, but are not as technical or don’t require coding experience. A data analyst is a great opportunity for someone who likes to use data to solve problems.
More like, what can’t you do. Data scientists are relatively new to the tech scene but have quickly become the hottest job in America. Data scientists use data to tell powerful stories and provide insights that can be used to solve problems or predict future outcomes. Machine-learning, algorithms, and AI are just a few fields you can get into as a data scientist.
There really is not a wrong programming language to learn. As you can tell from above, there is a demand for programmers with knowledge of any language. The beautiful thing about programming is that finding a job is based on aptitude. The fact that you were able to learn one language means that you are capable of learning another. Once you have learned a language it becomes much easier to learn another, and, the more languages you learn, the easier it is to keep learning more.
Many companies will actually let you pick the language you take your skills and aptitude test in during an interview. This may not even be the primary language they use, but if you can write great code in one language, you can learn to do it in another. This is why there really is no wrong language to learn.
What are the best ways to learn a language?
When it comes to learning to code, there is an endless number of both free and paid methods available online. A simple Google search will pull up countless games, courses, bootcamps, and degree programs that will teach you how to program in the language of your choice.
What is my next step?
Starting with free resources online is the best way to see if you’re passionate about coding. This will give you an idea if programming is right for you, and let you dabble with different languages to decide what you want to focus on.
Your next step is to start. Get on your favorite search engine and start learning as much free information about programming as you can.
We cannot emphasize enough how important it is to understand if you have a passion for coding, and if you do, to understand what path you want to take. Programming touches everything from the font of this article to understanding deep trends in markets to predicting the course and spread of a pandemic.
The only way to make your change is to begin. Just like Ed below, all it takes is passion and dedication to change your future forever. Don’t be scared.
Sources: All programming popularity charts are based on data from GitHut 2.0, created by littleark.
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