AI and Cybersecurity

This piece on the future of AI and Cybersecurity was created by Matthew Redabaugh, Cybersecurity Instructor at Flatiron School.

There’s a fascinating conversation happening today about AI and the impact it may have as it gets adopted. There’s a wide variety of opinions on the 5 Ws.

  1. Who will be impacted? 
  2. Who might lose their job or have their jobs adapted? 
  3. Will particular industries need more personnel thus the impact of AI will create more jobs? 
  4. What will change in everyday life as the technologies we have been accustomed to change due to AI?
  5. Will that change be subtle or drastic?

These are the kinds of questions that people are asking, especially in the field of cybersecurity. The main question I want to answer today is, “What is the relationship between AI and cybersecurity and how might the industry change with AI advancements?”

In this blog post, we’ll delve into the intricate relationship between AI and cybersecurity, debunk common misconceptions, and explore how AI is reshaping the landscape of digital defense.

What is Artificial Intelligence?

Let’s begin by addressing some common misconceptions about what AI is. 

The primary goal of AI is to give computers the ability to work as a human brain does. While this definition isn’t particularly narrow, AI’s scope is also quite broad. For a computer to be considered AI, it must encompass the ability to reason, learn, perceive, and plan. This is often accomplished through the development and implementation of algorithms that rely on statistics and probability to achieve a desired outcome.

Applications for Artificial Intelligence

Some use cases for AI that are being actively worked with are speech recognition and understanding languages, as well as the AI that is being used for travel assistance (updating maps, using AI to scan roads and create efficient routes.) AI empowers cybersecurity professionals to enhance their security posture through automated responses to attacks, to identify phishing schemes, to detect anomalous activity on networks (previously done manually), by analyzing weak passwords and then requiring users to update them, and more.

Is AI Conscious?

A common misconception about AI is that it is currently conscious or will become so in the near future.

One of the most interesting use cases for AI is Sophia, a humanoid robot introduced in 2016. It is the first robot to have been granted personhood and citizenship status in Saudi Arabia. Sophia can hold simple conversations and express facial expressions. Her code is 70% open source and critics who have reviewed her code have said that she is essentially a chatbot with a face because her conversation is primarily pre-written responses to prompted questions. Her existence has sparked an interesting debate over the possibility of having AGI (artificial general intelligence) in the future.

While Sophia’s sophistication in robotics is undeniable, the notion of her “consciousness” remains contested.

AI vs. ML vs. DL

There are two other terms that are often misconstrued or used interchangeably with AI. These are Machine Learning (ML) and Deep Learning (DL). It depends on use context and who may be using these terms as to what their more specific definition is. I consider them as subsets. ML is a subset of AI and DL is a subset of ML.

What is Machine Learning?

Machine Learning is set apart by its ability to learn and respond differently and uniquely by ingesting large amounts of data using human-built algorithms. This is done through either supervised learning, where the computer is given specific parameters by the developer to compare data inputs. Or unsupervised learning, where the computer is fed data and the algorithms allow for the computer to find relationships on its own. 

Applications for Machine Learning

In our daily lives, Machine Learning shapes experiences on music platforms like Spotify and Soundcloud. These platforms use algorithms to predict the best song choice for a user based on their preferences. Youtube employs a similar video-generating algorithm to select a video after one is finished.

Machine Learning in Cybersecurity

Machine Learning is used a lot in the cybersecurity world. Its tools may be used to ingest large amounts of data from networks and highlight security risks based on that data, like malicious access to sensitive information from hackers. This makes threat hunters’ jobs much more manageable. Instead of having to set security alerts and then respond to those alerts, we can use machine learning tools to monitor our environment. Based on prior attacks and knowledge of an organization’s systems and networks we better understand that an attack might be taking place in real time. As you can imagine, these tools are far from perfect, but they’re definitely a step in the right direction. 

What Is Deep Learning?

Deep learning is again an even more precise subset of Machine Learning. It functions in nearly the same way as ML but is able to self-adjust whereas ML requires human intervention to make adjustments.

Applications For Deep Learning

Some examples that are being used today are computers that can do image and pattern recognition. We’ve also seen this done with computers being able to ingest hours and hours of sound from an individual and then mimic their speech patterns. Self-driving cars would also fall into this category as they actively ingest data about the conditions of the road and other cars and road hazards to correct the car’s driving.

The common large language models like ChatGPT and Google’s Bard are considered deep learning as well.

Deep Learning In Cybersecurity

The ability for DL tools to mimic speech poses a genuine concern for cybersecurity professionals as it will allow for attackers to perform spear phishing attacks that are much more convincing.

Using AI For Good In Cybersecurity

Elevating Cybersecurity Blue Teams

One of the most important tools in the field of cybersecurity is something we call a SIEM. This stands for Security Information and Event Management. Traditionally a SIEM tool would be used by security operations center analysts to give us a clear picture of what is happening on an organization’s computer networks and applications, detect any malicious activity and provide alerts to the analysts so that they can respond accordingly. 

With Machine Learning, these tools have been upgraded so that if a security event occurs, the response is automated instead of the security team having to do this manually. 

These new tools we call SOARs: Security Orchestration, Automation, and Response. To give you an example, if a user in your organization was hacked and their account was being used by someone else, with a SIEM, if it’s working as intended, it may alert the security team that an account is being used maliciously. The analyst would inform the necessary parties and take that account offline or take the network down where that compromised account is being used. 

With a SOAR, whatever response that would be taken by the security analyst to remediate the issue, is now automated. SOARs use the concept known as playbooks, prebuilt and automated remediation steps that initiate when certain conditions are met. This transition not only expedites incident response but also minimizes potential human errors, significantly enhancing an organization’s cybersecurity posture. This still requires human intervention because this technology is still far from perfect.

Combat Phishing Attacks & Spam

AI is being used in the cybersecurity field to help our security personnel identify and classify phishing attacks and spam. It’s also being used to help with malware analysis where we can run the code of a discovered exploit through an AI tool and it may tell us what the outcome of that malware would have on our environment.

Expedite Incident Response

We can use AI to help us with Incident Response, as I mentioned earlier, with the automated remediation efforts that can happen with SOAR tools. AI can also be used to gather data to predict fraudulent activity on our networks which can help the security team address a potential liability before data is stolen or malware is installed on a system.

Prevent Zero-Day Attacks

With Machine Learning, cybersecurity professionals have a much better chance of protecting themselves against zero-day attacks. This is when a system or application vulnerability was previously unknown to the application’s developer. With Machine Learning, that vulnerability could be identified before an exploit occurs. In addition, machine learning could identify an intrusion before data is stolen or an exploit is carried out.

AI Uses for Bad Actors

Even with all the positive possibilities of AI and Cybersecurity, there is a dangerous side. The same technologies being used to protect our networks can and are being used to make hacking easier. 

Trick Network Security

If machine learning tools are implemented on a network, proficient hackers may be able to identify this. They can then act accordingly to deceive the machine learning tool into thinking that the hacker is a regular user.

Elaborate Phishing Campaigns

A very scary use case for AI being used by hackers is to create far more convincing phishing campaigns. The major cause of breaches is still mainly a human element. And, phishing is still one of the most common ways that hackers cause data breaches.

At the moment, phishing attacks are generally pretty easy to identify. International hackers may use bad grammar or send from an obviously fake email. They may try to hide links to websites that can easily be determined to be falsified. But with the introduction of AI, all of these mistakes can be fixed. 

ChatGPT can easily pass as a human. It can converse seamlessly with users without spelling, grammatical, and verb tense mistakes. That’s precisely what makes it an excellent tool for phishing scams.

Convincing Impersonations Of Public Figures

Another thing cybersecurity professionals are worried about is AI being used to mimic speech patterns, which would make spear phishing campaigns much more difficult to detect. I can easily imagine a world in which Twitter employees are being bombarded with fake emails from Elon Musk, or fake phone calls because his voice would be so easily recreated by AI. And this could happen with just about any CEO or any personnel from any organization.

The Road Ahead

AI is going to make us more efficient and more productive, as almost all technologies have done throughout history. But, as we navigate the evolving landscape of AI in cybersecurity, it is paramount to remain vigilant against its misuse.

I’ll leave you with this quote from Sal Khan, the CEO and founder of Khan Academy:

“If we act with fear, and say, ‘hey we just need to stop doing this stuff’ what’s really going to happen is the rule followers might pause, might slow down, but the rule breakers, the totalitarian governments, the criminal organizations, they’re only going to accelerate. And that leads to what I am pretty convinced is THE dystopian state, which is the good actors have worse AIs than the bad actors. We must fight for the positive use cases. Perhaps the most powerful use case, and perhaps the most poetic use case, is if AI (artificial intelligence) can be used to enhance HI (human intelligence), human potential, and human purpose.”

11 Best Websites to Practice Coding for Beginners in 2023

Indeed’s Best Jobs of 2023 ranked America’s most highly prized careers based on demand, pay, and potential for growth. These careers included:

  • Full-stack developer (#1)
  • Data engineer (#2)
  • Back end developer (#6)
  • Site reliability engineer (#7)
  • Director of data science (#25)

That means half of the top ten best jobs in America require coding skills‌. But, if you don’t have coding skills, where do you go to learn them?

At Flatiron School, we help pave the way for your transition into the tech industry. Our comprehensive programs are designed to provide you with the foundational knowledge you need to develop the coding and programming skills that are in such high demand. Our programs help students discover their full potential and pursue the career of their dreams.

But maybe you’re just ready to dip your toe in and explore your options? There are several coding for beginners resources online.  
So whether you’re looking to learn a new coding language or try out programming for the first time, it’s worth looking into coding practice sites for beginners.

11 Best Coding Practice Sites

Coding is not a spectator sport. It’s great to watch tutorials and read books on how to code, but to truly develop your programming proficiency, you must write the language yourself. Here are some of the best places to practice your coding skills.

1. Coderbyte

As you begin to develop your coding skills, you may be unsure what to practice first. It helps to work on real-world problems other coding professionals have faced—and Coderbyte has exactly that. With over 2,000 challenges on front and back end development, data structures, and algorithms that professionals have faced in their interviews, you’ll be able to hone your skills on examples that really matter.

Pros of Coderbyte

‌Coderbyte lets you use over 30 different programming languages and has a library of over 3 million solutions you can learn from.

Cons of Coderbyte

Coderbyte has a user interface that’s often complex for new users to navigate, so it might take some time getting used to this site.

2. Pluralsight

If you’re seeking a comprehensive learning platform that allows you to learn at your own pace, go with Pluralsight. You’ll develop a strong skill set in Python, JavaScript, HTML, and CSS, just to name a few. You can also receive learning recommendations based on what you’re focusing on.

Pros of Pluralsight

The platform provides a personalized learning experience as you can customize your training sessions with multiple features, including multiple language support.

Cons of Pluralsight

Users have minimal interaction with course instructors and industry experts, which makes it difficult to gain valuable feedback.   

3. Edabit

Unlock over 10,000 interactive coding challenges with Edabit. This free platform provides courses that are simple and practical. You can also access beginner tutorials to make the most of your learning experience. Plus, the challenges are ranked by difficulty, so you know exactly what level you’re at in your programming expertise.

Pros of Edabit

Learners gain access to a code editor that’s built into the platform. That way, users can create a code without having to switch to another application.

Cons of Edabit

You don’t gain a certificate for completing any of the tutorials. Also, some users have stated that the platform used outdated programming languages. 

4. CodinGame

Wanna play a game? CodinGame lets you practice your coding through fun games and code challenges. With single-round matches and both solo and multiplayer modes, this platform gives you a chance to practice coding the fun way.  

Pros of CodinGame

There are over 25 available programming languages.

Cons of CodinGame

Some users take issue with the size of the timers displayed on the programming tests. Unless you look carefully for the timer, you may miss the opportunity to submit your questions on time. 

5. CodeChef

Competition can be the best motivator to learn, and CodeChef offers exactly that. This platform lets users measure their skills by practicing more than 3,000 problems. You can compete against other coders, which creates great coding practice for beginners. But don’t worry—the competition is friendly, and participants often write posts and tutorials to help each other learn.

Pros of CodeChef

CodeChef users will be pleased to know that there is an active, supportive community that encourages growth. 

Cons of CodeChef

Users report that some practice problems lack clarity.

6. Project Euler

Project Euler offers a chance to solve challenging math problems with script. Over 1 million users have solved at least one problem on the site.

Pros of Project Euler

The site offers 111 programming languages.

Cons of Project Euler

If math isn’t your strongest subject, Project Euler may not be for you. The math-oriented programming languages get quite complex as you progress through the challenges.

7. TopCoder

Join a community of 1.7 million technical experts at TopCoder. On the learning side, they have an abundance of weekly challenges and explanations, along with challenging competitions that help you rise to the coding occasion.

Pros of TopCoder

The site is one of the most established platforms with an active user base.

Cons of TopCoder

Some users have experienced difficulty navigating the program’s user interface. Also, if you submit a support request, it may go unnoticed as their support system lacks efficiency.

8. One Month

Learning coding and web development in a span of 30 days with One Month. You can select from a variety of basic courses that cover HTML, Python, SQL, Ruby, and more! You also get to create real-world projects.

Pros of One Month

This user-friendly resource is great if you’re just looking to learn the fundamentals of coding and web development.

Cons of One Month

If you decide to switch to another programming platform, just note that there are no refunds available.

9. Geektastic

With detailed solutions to their multiple-choice and peer-reviewed coding challenges, Geektastic has a wealth of resources for programmers and a growing community of over 26,000 developers.

Pros of Geektastic

In addition to their interactive challenges and competitions, coders that rank high enough might even be allowed to join the review team. Members of this team get paid to review coding submissions for clients seeking a solution to their own coding projects.

Cons of Geektastic

Some users have raised concerns with how the challenges are scored, stating that they don’t reflect a candidate’s programming skills well. 

10. Geeks for Geeks

Made by developers for developers, Geeks for Geeks offers coding content for programmers of all skill levels, including beginners. Exercises in data structures, machine learning, web development, and much more are available.

Pros of Geeks for Geeks

Competitive challenges offer interactivity and a space to share coding solutions.

Cons of Geeks for Geeks

Geeks for Geeks primarily provides information in English, which means non-English speakers could run into trouble with the resources available. The website also has limited multimedia resources, mostly relying on text-based information.  

11. HackerEarth

What’s great about HackerEarth is that users can create and customize their coding assessments for technical positions. 

With HackerEarth, you’ll join a community of 7.6 million developers, participate in several programming challenges and customize your tests for a specific role.

Pros of HackerEarth

Not only does the website offer support in multiple languages, but it also includes AI proctoring to ensure exam results are accurate and reliable. 

Cons of HackerEarth

You might run into trouble navigating to specific problem types because the website has troublesome indexing and prioritization. 

Blogs to Help You Learn

They may be less interactive than competitions and online courses, but plenty of coding for beginners blogs are available to help new students gain programming proficiency. Here are some of our favorites.

1. The Crazy Programmer

This blog isn’t designed to give hands-on coding experience, but there’s a wealth of programming knowledge on pretty much everything else. From useful books and articles to tutorials and Q&As, The Crazy Programmer is a great blog to follow for those just learning to code.

2. The Blog

If you’re looking for courses or content that will grow your coding skills, The Blog will help you look in the right places. Written by a community of programming professionals, this blog is devoted to grading the most useful coding content so readers know they’re relying on quality sources. 

The blog touches on a wide range of topics, though, so those looking for resources on a specific language may find their content hit and miss.

3. Better Programming

As its name suggests, this blog is devoted to improving your programming. With posts on a range of topics in web design and coding, Better Programming features content from multiple industry pros on both introductory and advanced content. There’s truly something for everyone. As with, those concentrating on a specific topic may want something more focused.

Flatiron School: The Ultimate Coding Solution

What makes us different from coding websites? Here at Flatiron School, we work tirelessly to help students gain the foundational coding skills they need to begin a career in the tech industry. Combining flexible program options, industry-leading education, and up to 180 days of Career Coaching upon graduation, Flatiron School gives our students the jumping-off point they need to begin a rewarding tech career.  

Our programs contain a mixture of lectures, group work, instructor guidance, and community support to both equip our students with technical skills and prepare them to work effectively on a team. No matter what level a student begins at, Flatiron School’s Software Engineering program can take you from a complete beginner to industry-ready in as little as 15 weeks. 

If you’re committed to a career in tech, we’ll teach you the skills you need to succeed. 

Apply today to get started. Not ready to apply? No problem – test out our material with Free Software Engineering Prep Work or download the course syllabus.

If you’re an employer looking to bring new talent to your team, check out our tech training solutions and see how you can invest in your company’s growth. 

Data Science vs. Software Engineering: Industry Trends and Future Predictions

The road to the future isn’t paved with asphalt—it’s a path defined by ones and zeros. For centuries, infrastructure was in the hands of physical labor engineers. Today, it’s the masters of data science and software engineers who will move today’s societies into the next stage of technological advances.

In this piece, we’ll examine industry trends in data science vs. software engineering, forecast the direction of both fields, and consider how they’ll impact each other in the future.

Industry Trends in Data Science

Hiring Growth

The industry is actively seeking data scientists. Estimates show data science employment is expected to grow by a staggering 36% from 2021 to 2031. The future is in the hands of skilled data analysts, data engineers, and data architects who can use data analysis to extract valuable, actionable insights.

Gathering Big Data

Projections suggest more than 150 billion devices will be generating 175 zettabytes of data by 2025. Much data will be generated and analyzed in real-time, providing almost-instant feedback for improved results (think content recommendation systems).

These mountains of captured data will drive company decisions, strategies, and future projections. People who can design complex new analytical models and then train machine learning systems on those models will be invaluable.

Analyzing TinyML and Small Data for Data-Driven Devices

The Internet of Things (IoT) has ramped up the need for data scientists who work with TinyML and small data. IoT devices are being developed for nearly every industry, calling for experts to gather and implement that data.

Smart homes. Smart transit. Entire smart cities. All of these call for small, low-powered devices that compute their ML and datasets. These TinyML devices require the expertise of data scientists to collect and analyze billions of data points. They’re then stored in the cloud, streaming new command instructions for these smart devices to act upon in real time.

This is where data science meets the true cutting edge of the future of data-driven devices.

Using AutoML

Analyzing monumental amounts of data collected from databases, platforms, and devices calls for integrating metrics using Automatic Machine Learning (AutoML). 

Data scientists will rely on automated tasks to gather accurate data streams. Likewise, industries will need their expertise to define what tasks are suitable for ML and to train ML on their information models to improve accuracy.

Industry Trends in Software Engineering

High Growth

Like data science, software engineering will likely see extremely fast growth. The industry is expected to grow 25% from 2021 to 2031.

For over a decade, businesses have relied more heavily on digital solutions to traditional scaling challenges. From digital platforms to app development in emerging fields like AI and ML, software engineers are in high demand to build the future.

Utilization of Agile Methodology and DevOps

Software development teams have significantly benefited from advances in Agile Methodology and DevOps programming and share centralized datasets while developing quickly and efficiently. 

Teams consistently share daily progress in small sections of completed applications, later joined to build a final product. Redundant processes like testing for errors and security issues are automated.

Agile and DevOps will trend upwards together as both continue to benefit from increased usage and advances in AI and ML.

Cloud-Based Platform Development

The reliance on cloud computing will increase. Centralized data repositories shared by development teams, massive amounts of storage, and an added layer of security will help businesses scale at cost.

AI and ML Automation

While the AI and ML fields are currently experiencing an explosion of public interest, they’ve been trending for many years. From every sector, automating processes reliably while using ML to train systems for specific tasks to increase accuracy is critical. The roles of AI and ML in Agile and DevOps are expected to continue driving innovations in development.

Advances in Cybersecurity

Cyber threats are rising exponentially. In 2022, there was an average of 1,168 weekly cyberattacks—a 38% rise compared to 2021. Paired with a proportionate increase in complexity and sophistication, companies both large and small are seeking news ways to secure their systems against the onslaught. 

There will continuously high demand for Software Engineering skills in Cybersecurity to keep systems and information secure.

Mobile App Development

The more we rely on smartphones for our daily needs, the greater the need for mobile-friendly app development and support systems. Whether it’s for companies needing to develop AI-based customer support, for apps using gamification to help people in personal development, or even for GPS-based beacon technology, there’s never been a greater need for mobile-first software development.

The Intersection of Data Science vs. Software Engineering: How the Two Fields Will Impact Each Other

It’s easy to think of these two fields as separate entities – as data science vs. software engineering. But, as multiple revolutionary technologies grow exponentially, we’ll see the intersection of technology fueled by advances in both fields—data science and software engineering. 

Increasingly sophisticated software tools will integrate cutting-edge AI and ML, requiring new skill sets from data scientists and software engineers alike. 

Software engineers will draw from massive repositories to streamline and optimize code development. 

Data privacy and the ethics surrounding intellectual property will call on advanced data analytics. At the same time, combining 5G, IoT, and advances in AR and VR will transform every industry.

Data scientists and software engineers will play fundamental roles in shaping the future of banks, hospitals, customer service, and ethical data mining. At the same time, new fields will be emerging, creating demand for roles that don’t exist yet.

Advances in AI and ML

With new applications developed every day that tap into the power of neural networks and large language models, the world will need analysts and other data experts. Data analysts will formulate how to use millions of new data points. Meanwhile, software engineers will experience increased demand to help companies accelerate data use to gain an edge over competitors.

Programs designed to help the user automate tasks will require the expertise of data scientists and programmers to integrate AI and ML upgrades to features. Companies based on a digital platform model will need constant new integrations for greater datasets from users and features to help the user make smarter, faster purchases using advanced predictive models.

Advances in IoT

IoT is going to change the way we look at the world. Every device, appliance, and gadget will operate on analytics designed by today’s data scientists, with code optimized for even the simplest systems. At scale, everything from traffic patterns on our roads to railways and airport runways will require complex models that other machines will read and interpret. 

Cities will rely on well-crafted information systems and dynamic analytics to manage electricity use, waste management, hospital equipment, and other systems. The potential use cases are virtually endless.

Advances in AR and VR

Along with powerful CPUs and other advances, we’ll probably see accelerated movement into practical applications for AR and VR. They’ll factor into production tools, virtual work environments, live events, and virtual game environments — supporting and enhancing experiences in reality. This technology means coding opportunities galore, but it also means opportunities to collect unique new user data.

Advances in 5G

5G does for internet bandwidth what AI is doing for intelligent computing. This technology will open doors for front end developers to design radically upgraded visuals for desktop and mobile.

New predictive models will transform the market in ways we have yet to imagine. Improvement in computing data means collecting real-time information on users and their connections to one another.

Join the Technology Revolution in Data Science and Software Engineering

If you’re considering a future in data science or software engineering, get a taste of what Flatiron School offers: download a syllabus and try your hand at our free lessons.

Or, you may be looking to hire the best talent. If so, learn how we help businesses with our hire-to-retire dream team recruitment services and multiple technology training solutions.

How to Become a Software Engineer Without a Degree

Pursuing a traditional 4-year degree in computer science has long been the traditional career path for software engineers. But today, many software engineers are finding alternative paths to their careers. In fact, according to a 2022 survey, only 41.32% of software developers have a bachelor’s degree. 

With the evolving tech landscape, software engineering has become one of the most rapidly growing fields. There are more computer games, web and mobile apps, network control systems, and operating systems being developed than ever before, and they aren’t going anywhere anytime soon. 

As industry opportunities expand, so do the pathways to this lucrative career. Here’s how to become a software engineer without a degree, including the necessary skills you need to acquire and tips to help pick the right route for you.

What we cover here:

What Skills Do Software Engineers Need?

Software engineers need computer programming, coding, object-oriented design, and software development skills. Other life and social skills such as problems solving, logical thinking, and great communication can help you get a leg up in the software engineering field as well.

These required skills can be learned quickly, especially if you exhibit the 7 signs you could be a successful software engineer. Here are the key skills you will need for this career:

Essential Tech Skills

  • Programming Languages: Proficiency in languages such as Java, Python, Ruby, C++, or JavaScript are crucial for designing and implementing software solutions. 
  • Software Testing: Knowledge of basic and advanced debugging and testing techniques is required, even in the age of AI testing. 
  • Software Development: Engineers familiar with the ins and outs of developing software have a more solid understanding of the structures behind software performance.
  • Object-Oriented Design (OOD): Essentially, this approach involves defining, understanding, constructing, and identifying the objects of a system.

Other Skills

  • Problem-solving: The ability to analyze complex problems and break them into manageable components is crucial for software engineering.
  • Communication: Excellent written and verbal communication skills can help engineers articulate ideas and job requirements to coworkers and clients.
  • Teamwork: Many aspects of the career require collaboration, especially on larger projects that involve the efforts of a team.

But how does one go about acquiring the skills needed to become a software engineer, especially if you don’t have 4 years to go back and earn a traditional degree?

How Do You Become a Software Engineer Without Earning a College Degree?

To become a software engineer without a degree, pursue self-study and alternative professional development (such as online courses, workshops, learning platforms, and bootcamps) to learn the necessary skills and knowledge needed to enter the field. Doing so helps you to gain practical experience while building a comprehensive foundation for a career in software engineering.

Important Courses for a Software Engineer

A well-rounded curriculum should include foundational programming and specialized courses that include theoretical concepts and practical applications. This varies but may include:

  • Computer/Software Science
  • Data Structures and Algorithms
  • Object-Oriented Programming
  • Front End Development
  • Back End Development
  • Artificial and Machine Learning

Ultimately, the best programs should send you off with a final portfolio that demonstrates your high-level, ready-to-work knowledge, skills, and experience.

Pros and Cons of Earning a Degree, Self-Learning, and Software Engineering Bootcamp

There are several pathways to becoming a Software Engineer, with pros and cons for each avenue. Here’s an overview.

Earning a Traditional 4-Year Degree


  1. Comprehensive education
  2. Clear credibility 
  3. In-depth, diverse knowledge
  4. Built-in networking opportunities


  1. Time-consuming 
  2. Often prohibitively expensive
  3. Less focus on practical skills
  4. Little real-world experience 
  5. Curriculum may not address new technologies



  1.  Learn at your own pace
  2. Cost-effective, especially compared to 4-year tuition
  3. Ability to tailor learning to interests
  4. Source learning materials from open-source contributors


  1. May lack a well-rounded structure
  2. Potential gaps in knowledge without guidance
  3. Must be incredibly dedicated, committed, and self-motivated

Software Engineering Bootcamp


  1. Accelerated pace to gain skills quickly
  2. Emphasis on practical, real-world skills and application
  3. Networking and career support
  4. Guided learning and accountability structure
  5. Less expensive than a traditional 4-year degree program
  6. Flexibility to complete software engineering courses online
  7. Up-to-date curriculum addresses new and evolving technologies


  1. Financial investment 
  2. Time commitment to complete coursework

Flatiron’s Software Engineering Course

College courses can be beneficial, but there are quicker ways to make it from beginner to a high-paying career in software engineering. That’s where Flatiron’s Software Engineering course comes in. 

Our bootcamp teaches you the skills needed to become a successful software engineer, with courses that cover everything from design to maintenance. In as little as 15 weeks, you can learn everything you need to know to break into the industry and land your first job. Download the Software Engineering Syllabus to see the skills you’ll learn. 

Need some flexibility as you complete the course? Both part- and full-time course schedules are available, with online and in-person campuses that fit your schedule and availability.

Apply to Jumpstart Your Software Engineering Career

Whether you’re a budding full stack developer or completely inexperienced, our Software Engineering program will take you from beginner to industry-ready in as little as 15 weeks.

Apply to Flatiron School today and get started toward a fulfilling career in tech.

What is Artificial Intelligence?

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.


  • Suited for well-defined domains with known and explicitly defined rules


  • 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.


  • 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


  • 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. 

Ace Interview Prep With AI

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.

Why Cybersecurity Certifications Matter: A Look at the CompTIA Security+ Certification

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. 

The certification prepares career changers for success in roles including Systems Administrator, Security Administrator, Security Analyst, and Security Engineer. Across the world, there are over 500,000 Security+ certified professionals. Among IT professionals that hold CompTIA certifications in North America, 62% hold a Security+

Certification vs. Hands-on Training

Are certifications everything? 

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.


Flatiron School Launches Artificial Intelligence Curriculum Enhancements For Students

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.

Tech Verticals For New Grads

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.

Earth Day: How Tech Can Fight Climate Change and Improve Sustainability

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.

Together, we can make the world a better place.