Or maybe you've read about other futuristic attacks on power plants or water treatment plants that are modified remotely so as to take them offline.

As the world gets increasingly digitized, the risks and rewards that come with cyberattacks are increasing every day.

This is reflected in the demand for cybersecurity roles – with even entry-level cybersecurity analyst roles often earning more than $100,000/year.

But at the moment, There is an estimated current gap of 3.5 million unfilled cybersecurity roles.

Cybersecurity is a lucrative and in-demand career path that doesn’t look like it will go away anytime soon. But helpful tips for getting into this career are, however, somewhat rare compared to the well-worn paths to data science and software engineering.

I spent some time working in cybersecurity on my own startup, and I also helped launch a cybersecurity bootcamp. Based on my research, and some of the candid insights from the people I worked with, here’s some useful tips if you're considering your cybersecurity prospects.

Look for Entry-level or Mid-level Blue Team Roles

Screenshot of Indeed Search for Cyber Security Analyst (provided by author)

The distinction between blue team and red team roles is a critical inflection point for many cybersecurity careers.

You might be fascinated by tales of hackers tapping away and by movies about incredible hacking prodigies who get into the Pentagon’s systems. But the reality is that entry-level cybersecurity roles often involve getting experience on defense (“blue team”) and preventing attacks rather than starting them.

You’ll get started with an analyst role and work with companies to prevent attacks rather than simulating or trying them out most of the time.

Most “red team”/offensive roles that involve penetration testing are handled by agencies or external teams these days. Even companies that hire internal teams to do that work expect you to have played defense for some years before you’re part of an offensive team.

Certifications related to the offensive side such as the Certified Ethical Hacker (the CEH) are often given out with years of work experience as a prerequisite. You need a minimum of 2 years of security-related experience to even test for the CEH, for example.

If you have a military background, initiatives like this one by Microsoft will help get you structured for success in entry-level cybersecurity roles and will help transition you from military to civilian life.

Network within Associations

Local professional associations and meetups are a great way to get to know the cybersecurity community at large. Mark it as an essential step towards getting started and building both cybersecurity knowledge and a network.

This is a great way to develop your soft skills as well – a critical need in cybersecurity roles involves communicating with multiple teams.

The following article has a list of associations that are cybersecurity-related, as well as a section dedicated to associations that are driven to get more women into cybersecurity.

Certifications are Good, but Experience is Worth its Weight in Gold

There are a bunch of certifications, from CompTIA’s Security+ to others that will help signal your readiness for cybersecurity jobs. Some are more entry-level and require IT competencies such as the A+. But some will require you to have job experience in cybersecurity (such as the CISSP).

There’s a bit of a chicken and egg situation and you might wonder – how can you get job experience if you need job experience to get the job in the first place?

Adjacent job experience can often make a difference here. Many people transition into cybersecurity from IT roles, such as network administration, system administration, or being on helpdesk for IT, which is an entry-level role. You can gain experience here and transition over.

There are also programs tailored for veterans and people with law enforcement backgrounds to get into cybersecurity. Lastly there are many cybersecurity internships being offered to bridge this gap – though with the right backing, training, and the right experience, you can skip ahead to junior-level analyst roles.

Look for Security Operations Centers Analyst Roles

SOC analyst roles are a good way to break into the cybersecurity industry. Security operations centers need analysts to parse through different threats. This entry-level role pays $71k on average, and can be a good way to demonstrate your capabilities before moving on to more advanced roles.

You’ll need technical knowledge, but the depth of experience required here isn’t so deep. This marks it as a great way for people who are self-taught developers, familiar with SQL, Python, web applications and the terminal to move into a cybersecurity role and begin their career.

Like we talked about earlier, this is a defensive “blue team” role that will help you get started down the path to more advanced roles. Here’s a sample job description outlining the role.

As you can see from that job description, you’ll need familiarity with industry-specific tools such as a SEIM (Security information and event management tool) and SOAR (Security orchestration, automation and response tool). You'll also need experience with the command line and scripting, and knowledge of cybersecurity frameworks.

While this particular outline calls for 2 years+ of related experience in security operations, there are quite a few that don’t require too much entry-level experience.

SOC analyst roles are generally regarded as a good entry-level position for people looking to pivot and transition deeper into a cybersecurity career.

Practice Your Scripting Skills, Especially with Linux

Image from Wikimedia Commons

In order to be fully effective in cybersecurity, understanding how to code and systematically look through logs is essential.

With active, large corporate sites, there might be thousands or even tens of thousands of hits every minute. And sifting through and being able to identify threat actors requires some knowledge of programming to decipher it all.

If you’re looking to advance into cybersecurity, it’s good to know how to programmatically work and analyze the flood of data that comes with it.

This will involve getting comfortable with the terminal, working on Python and shell scripts, and other programming skills. You’ll want to think about deploying real-time algorithms that are able to sift through data accurately and reliably.

In general, you’ll want to be strong enough with Linux commands or other terminal commands to do the following:

1. View critical system information and status

2. Be able to detect running processes and programs and start/stop them

3. Install software and be able to update it securely (configuration of automatic installation may be required)

4. Connect into remote systems using tools such as SSH

5. Go through events logs and be able to systematically understand what is happening to a system

6. Set up events logs in such a way that you can be maximally efficient at responding to threats

7. View how the network is set up and make changes as needed, and make system changes as needed as well

Out of these skills, a couple of programming mainstays will help you a lot.

Knowledge of regular expressions, a way of finding patterns within large blocks of code, will quickly help you go through logs to find out what is happening. And BASH scripts are useful for interacting with the terminal in a more systematic way,

You can sharpen these skills by playing games or reading through materials. If you’re on a Mac or Linux computer, you’ll already have a head start with a Linux-like or Linux terminal wrapped up as a default app. Otherwise, you can practice with Microsoft Powershell.

There are also courses devoted to the Terminal, from offerings such as Colt Steele’s course on Udemy to Codecademy’s interactive Pro course devoted to the topic. Or if you prefer a more systematic approach that ties together these technical skills as well as the tools and compliance measures you need to consider, look at something like Springboard’s cybersecurity bootcamp.

Learn Commonly Used Cybersecurity Tools

Image of Kali Linux from Wikimedia Commons

There are a suite of cybersecurity-specific tools, such as Kali Linux, that will be useful to any aspiring analysts. Playing around with them can give you a lot of practice and understanding of the modern cybersecurity stack.

Here are a few examples of tools you should be familiar with for the cybersecurity analyst level:

1. NMap is an open source tool that lets you easily map out different network ports, and allows you to do basic vulnerability scanning.

2. Wireshark lets you sniff packets of information from a network. You can use it to break down real-time data on a live network, giving you detailed information on the data passing within a network.

3. John The Ripper is a password cracker meant to detect when there are weak Unix passwords. This is an important check to make sure that users with admin privileges have secure accounts so that an external attacker cannot have access to system privileges they would need to escalate an attack.

4. Snort is open source software that allows you to detect network intrusion. It can do live traffic analysis and figure out if there’s malicious software on inbound requests, allowing you to more easily defend a network from malicious actors.

5. Metasploit allows for deeper analysis of the results from tools like NMap and Wireshark. While it’s used primarily as a penetration testing tool, you can use it to lay the foundation for a good network defense. It used to be open-source and totally free, until a company acquired the software. You can still use the free community edition to practice getting familiar with it, however.

Learn Commonly Used Compliance Frameworks

Cybersecurity is not just a technical endeavor. You have to understand different compliance frameworks and rigorously go through them.

Cybersecurity, after all, isn’t just about technical skills. It’s about standards and audits, as well as defining processes so that security can be effectively applied across organizations.

You’ll want to be familiar with some general compliance frameworks as well as industry-specific ones, especially for highly sensitive and regulated industries such as military or health work.

Here are just a few to consider:

• CMMC, specifically for military contractors who want to deal with the US Department of Defense.

• The OWASP Top Ten is a list of the top ten security risks for web applications that is openly published – a good structured way to think about cybersecurity risks.

• HIPAA governs the data security practices in the healthcare industry. There are strict fines and consequences for deviating from those standards, which are set in US law.

• GDPR is a set of data and cybersecurity laws passed in the European Union that are driving standards forward for how websites can collect and process data.

• SOC-1 and SOC-2 audits are mostly focused on startups or other companies selling to large banks and other financial institutions. Specific rules and processes are checked in order to assure large financial institutions that they’re dealing with credible partners.

On this last point, it’s critical to keep up with evolving technical standards, tools, and compliance frameworks.

While it’s probably most worth keeping up on the news when it comes to new laws and new responses to attacks, a well-rounded cybersecurity interest in technical updates, compliance updates, and community updates is best to really craft a meaningful and long-lasting career in cybersecurity.

Resources that can help in this regard are newsletters such as Security Weekly, and the Sophos Naked Security site, as well as general news sites such as Hacker News.

Wrapping up

Having technical interests and some programming skills doesn’t just restrict you to careers in data science, web development, and IT. You can defend essential systems and data as a cybersecurity analyst, and transition into a lucrative and future-proof career in cybersecurity.

If you feel like you need help with 1:1 mentorship from industry experts and a curriculum built by hiring managers, Springboard has a cybersecurity bootcamp backed by a job guarantee that ensures you can leverage this tangible, useful advice towards a new career in cybersecurity.

I work for Springboard — we’ve put a lot of research into machine learning training and resources. At Springboard, we offer the first online course with a machine learning job guarantee.

What helps a lot when confronted with a new problem is to have a primer for what algorithm might be the best fit for certain situations. Here, we talk about different problems and data types and discuss what might be the most effective algorithm to try for each one, along with a resource that can help you implement that particular model.

Remember: the proof is in the pudding: the best approach to your data is the model that empirically gives you the best results. This guide is meant to hone your first instincts and help you remember what models might be the most effective for each problem, and which would be impractical to use.

Let’s start by talking about the variables we need to consider.

Unsupervised learning vs supervised learning

Unsupervised learning is where you allow the machine learning algorithm to start learning and outputting a result without any explicit human processing of the data beforehand.

Supervised learning involves some labeling and processing of the training data beforehand in order to structure it for processing.

The kind of learning you can perform will matter a lot when you start working with different machine learning algorithms.

Space and time considerations

There are space and time considerations for each machine learning algorithm. While in practice you’ll likely work with optimized versions of each algorithm packaged in a framework, it is good to consider how the algorithms you choose can affect performance.

The output

Third, and perhaps most important, is the output that you want to get. Are you trying to categorize data? Use it to predict future data points? What you’re looking to get as a result and what you want to do to your data will largely determine the algorithmic approaches you should take.

Some examples

You’re looking to build a simple predictive model with a well-structured dataset without too many complications.

Your best bet here is probably linear regression, something that can take a whole host of factors and then give you a predictive result with a simple error rate explanation and a simple explanation for which factors contribute to the prediction. It doesn’t take much computational power to run a linear regression either.

Resource: Linear Regression — Detailed View

You’re looking to classify data that’s already been labeled into two or more sharply distinct types of labels (e.g., trying to determine if children are likely male or female based on their weight and height) in a supervised setting.

The first instinct you should have when you see a situation like this is to apply the logistic regression model. After running the model, you’ll see that it forces every data point into two different categories, allowing you to easily output which point belongs to which category. The logistic regression model can also be easily generalized to working with multiple target and result classes if that’s what your problem demands.

Resource: Building a Logistic Regression

You’re looking to place unlabeled continuous data into different groups (e.g., putting customers with certain recorded traits and trying to discover categories/groups they can belong to).

The first natural fit for this problem is the K-Means clustering algorithm, which will group and cluster data by measuring the distance between each point. Then there are a variety of clustering algorithms, such as Density-Based Spatial Clustering of Applications with Noise and Mean-Shift algorithms.

Resource: The 5 Clustering Algorithms Data Scientists Need to Know

You’re looking to predict whether a string of characters or a grouping of traits falls into one category of data or another (supervised text classification) — e.g, whether a review is positive or negative.

Your best bet here is probably Naive Bayes, which is a simple but powerful model that can be used for text classification. With some text pre-processing and cleaning (being especially careful to remove filler stop words such as “and” that might add noise to your dataset), you can get a remarkable set of results with a very simple model.

Another decent bet is logistic regression, which is a simple model to grasp and explain, and less hard to pick apart than Naive Bayes (which will often assign probabilities word by word rather than holistically labeling a text snippet as being part of one group or another).

Moving on to something more powerful, a Linear Support Vector Machine algorithm will likely help improve your performance. If you want to skip right ahead here, you can (though I suggest trying both models and comparing which one works best — Naive Bayes has an absurdly easy implementation on frameworks like scikit-learn and it isn’t very computationally expensive so you can afford to test both).

Lastly, bag-of-words analysis could also work — consider doing an ensemble of different methods and testing all of these methods against one another, depending on the dataset in question.

Resource: Multi-Class Text Classification Model Comparison and Selection

You’re looking to do unstructured learning on large-scale image or video datasets (e.g., image classification).

The best algorithm to tackle going through different images is a convolutional neural network that is organized similarly to how animal visual cortexes are analyzed.

Measured by performance (reduced error rate) in the ImageNet competition, the SE-Resnet architecture comes out on top, though as the field is still developing, new advances come out almost every day.

You should be aware, however, that convolutional neural networks are dense and require a lot of computational power — so make sure that you have the hardware capability to run these models on large-scale datasets.

Resource: Review of Deep Learning Algorithms for Image Classification

You’re looking to classify result points that come out of a well-defined process (ex: number of hires from a pre-established interview process, wherein you know or can computationally infer the probabilities of each event).

The best option for this is probably a decision tree algorithm that will clearly explain what the split points are between classifying something into one group or another.

Resource: Decision Trees in Machine Learning

You’re looking to do time series analysis with well-defined, supervised data (e.g., predicting stock prices based on historical patterns in the stock market arranged on a chronological basis from the past to the present).

A recurrent neural network is set up to do sequence analysis by containing an in-stream internal memory of data it processes, allowing it to take into account the relationship between data and the time horizon and order it’s deployed in.

Resource: Recurrent Neural Networks and LSTM

Wrapping up

Take the recommendations and resources above, and apply them as a sort of first instinct for your modeling — it’ll help you jump into any work you do just a little bit faster. If you’re interested in being mentored by a machine learning expert in learning how to train your instincts further, check out Springboard’s AI/Machine Learning Career Track.

The problem is that most people get caught up on the AI hype, mixing technical discussions with philosophical ones.

If you’re looking to cut through the AI hype and work with practically implemented data models, train towards a data engineer or machine learning engineer position.

Don’t look for interesting AI applications within AI articles. Look for them in data engineering or machine learning tutorials.

A Twitter screenshot that perhaps best summarizes

These are the steps I took to build this fun little scraper I built to analyze gender diversity in different coding bootcamps. It’s the path I took to do research for Springboard’s new AI/ML online bootcamp with job guarantee.

Here’s a step-by-step guide to getting into the machine learning space with a critical set of resources attached to each one.

1. Start brushing up on your Python and software development practices

You’ll want to start off by embracing Python, the language of choice for most machine learning engineers.

The handy scripting language is the tool of choice for most data engineers and data scientists. Most tools for data have been built in Python or have built API access for easy Python access.

Thankfully, Python’s syntax is relatively easy to pick up. The language has tons of documentation and training resources. It also includes support for all sorts of programming paradigms from functional programming to object-oriented programming.

The one thing that might be a bit hard to pick up is the tabbing and spacing required to organize and activate your code. In Python, the whitespace really matters.

As a machine learning engineer, you’d be working in a team to build complex, often mission-critical applications. So, now is a good time to refresh on software engineering best practices as well.

Learn to use collaborative tools such as Github. Get into the habit of writing thorough unit tests for your code using testing frameworks such as nose. Test your APIs using tools such as Postman. Use CI systems such as Jenkins to make sure your code doesn’t break. Develop good code review skills to work better with your future technical colleagues.

One thing to read: What is the best Python IDE for data science? Take a quick read-through so you can understand what toolset you want to work in to implement Python on datasets.

I use the Jupyter Notebook myself, since it comes pre-installed with most of the important data science libraries you’ll use. It comes with an easy, clean interactive interface that allows you to edit your code on the fly.

Jupyter Notebook also comes with extensions that allow you to easily share your results with the world at large. The files generated are also super easy to work with on Github.

One thing to do: Pandas Cookbook allows you to fork into live examples of the Pandas framework, one of the most powerful data manipulation libraries. You can quickly work through an example of how to play with a dataset through it.

2. Look into machine learning frameworks and theory

Once you’re playing around with Python and practicing with it, it’s time to start looking at machine learning theory.

You’ll learn what algorithms to use. Having a baseline knowledge of the theory behind machine learning will let you implement models with ease.

One thing to read: A Tour of The Top Ten Algorithms For Machine Learning Newbies will help you get started with the basics. You’ll learn that there isn’t a “free lunch”. There is no algorithm that will give you the optimal result for each setting, so you’ll have to dive into each algorithm.

One thing to do: Play around with the interactive Free Machine Learning in Python Course — develop your Python skills and start implementing algorithms.

3. Start working with datasets and experimenting

You’ve got the tools and theory under your belt. You should think about doing little mini-projects that can help you refine your skills.

One thing to read: Take a look at 19 Free Public Data Sets for Your First Data Science Project and start looking at where you can find different datasets on the web to play around with.

One thing to do: Kaggle Datasets will let you work with lots of publicly available datasets. What’s cool about this collection is you can see how popular certain datasets are. You can also see what other projects have been built with the same dataset.

4. Scale your data skills with Hadoop or Spark

Now that you’re practicing on smaller datasets, you’ll want to learn how to work with Hadoop or Spark. Data engineers work with streaming, real-time production-level data at the terabyte and sometimes petabyte scale. Skill up by learning your way through a big data framework.

One thing to read: This short article How do Hadoop and Spark Stack Up? will help you walk through both Hadoop and Spark and how they compare and contrast with one another.

One thing to do: If you want to start working with a big data framework right away, Spark Jupyter notebooks hosted on Databricks offers a tutorial-level introduction to the framework, and gets you to practice with production-level code examples.

5. Work with a deep learning framework like TensorFlow

You’re done exploring machine learning algorithms and working with the different big data tools out there.

Now it’s time to take on the sort of powerful reinforcement learning that has been the focus of new advances. Learn the TensorFlow framework and you’ll be on the cutting edge of machine learning work.

One thing to read: Read What is TensorFlow? and understand what’s going on below-the-hood when it comes to this powerful deep learning framework.

One thing to do: TensorFlow and Deep Learning without a PhD is an interactive course built by Google that combines theory placed into slides with practical labs with code.

6. Start working with big production-level datasets

Now that you’ve worked with deep learning frameworks, you can start working towards large production-level datasets.

As a machine learning engineer, you’ll be making complex engineering decisions on managing large amounts of data and deploying your systems.

That would include collecting data from APIs and web scraping, SQL + NoSQL databases and when you’d use them, use of pipeline frameworks such as Luigi or Airflow.

When you deploy your applications, you might use container-based systems such as Docker for scalability and reliability, and tools such as Flask to create APIs for your application.

One thing to read: 7 Ways to Handle Large Data Files for Machine Learning is a nice theoretical exercise into how you would handle big datasets, and can serve as a handy checklist of tactics to use.

One thing to do: Publicly Available Big Data Sets is a list of places where you can get very large datasets — ready to practice your newfound data engineering skills on.

7. Practice, practice, practice, build towards a portfolio and then a job

Finally, you’ve gotten to a point where you can build working machine learning models. The next step to advance your machine learning career is to find a job with a company that holds those large datasets so you can apply your skills every day to a cutting-edge machine learning problem.

One thing to read: 41 Essential Machine Learning Interview Questions (with answers) will help you practice the knowledge you need to ace a machine learning interview.

One thing to do: Go out and find meetups that are dedicated to machine learning or data engineering on Meetup — it’s a great way to meet peers in the space and potential hiring managers.

Hopefully, this tutorial has helped cut through the hype around AI to something practical and tailored that you can use. If you feel like you need a little bit more, the company I work with, Springboard, offers a career track bootcamp dedicated to AI and machine learning with a job guarantee, and 1:1 mentorship from machine learning experts.

I started working on a few digital marketing roles that required web development, data analysis and other skills, working through and building analytics systems in Python, and tinkering with websites in Ruby, HTML and CSS. I’ve placed several other autodidacts into their dream jobs in my most recent role as the head of growth for a data science and machine learning education company.

I also just finished writing a 80-page guide to how to get a programming job without a degree.

Through it all, I’ve shortlisted several useful resources that I myself review frequently, and share with different students. My experiences have really helped me learn what useful resources help people down their career path and which don’t.

I’ve been a marketer. I can tell which resources barely add value and which do. I’m going to point you to the ones that really do add tremendous value. Consider it an insider tip.

So, without further ado, here are the resources I’ve found that genuinely work for me and different students.

Start with finding your community

The one thing that’s added the most value for me personally are strong communities focused on learning. In workplace settings, 80% of learning takes place between mentors and mentees. You want to surround yourself with a supportive community whenever you’re learning something new so you can benefit from the same effect.

Here are some of the communities that I found useful:

• A subreddit within the larger Reddit community, the learnprogramming subreddit is dedicated to programming resources and for programming learners. It’s a great resource where people will upvote the top resources to learn programming for your consumption. I found it because I’m a frequent reddit user and to my delight, it’s ended up being one of my top resources to consult on a frequent basis.
• I then went on to the ubiquitous Stack Overflow. Here, you can see a variety of programming challenges and supplied answers from experts in different programming communities. I came here both to see the answers compiled from experts in the field, and to pose questions myself.
• I then started browsing Hacker News. It’s a daily curated feed of the most valuable and relevant technology and programming news out there. Community members are responsible for upvoting and downvoting both articles and comments, ensuring that quality submissions come to the forefront. I’ve found the articles to be very high-quality and well-vetted here.
• I’ve reached out to different employees of companies, including Google, Facebook and more through here, contacting them through their Hacker News accounts and emails they’ve provided me. It’s been an invaluable resource for making career connections and for getting great resources to learn from.
• Then, moving on, I found the Quora programming community. With many of the initial users based in Silicon Valley, the site has become a hotspot for reaching out to intelligent and technically skilled folks. I’ve consulted mega-threads related to learning programming and asked questions to further my learning here.
• Finally, I found Slashdot, a large programming community filled with IT professionals. It tends to be filled with people who use SourceForge. While the community seems to focus more on older closed-source solutions, it can still be a useful repository of knowledge, and I browse it occasionally.

The really cool thing with these programming communities is that they are all rich repositories of genuinely helpful career resources. I managed to reach out to different helpful mentors and also consult tons of threads and experiences from people who work in the tech industry.

Then look through actual code and build your portfolio

Now that you’re done looking at different communities that can help you on your programming journey, you can turn around and do what I did. Look for repositories of code where you can start contributing!

• GitHub is the world’s largest living repository of code. The code here is updated by different contributors on an almost-hourly basis, with many of the fundamental building blocks of different programming languages constantly being hosted and upgraded here. Look through different blocks of code, contribute some code of your own, or host projects on Github for collaboration. You can also search for the “awesome” repositories to get a list of curated resources on different programming topics. That’s how I started seeing the power of mega-lists of programming resources.
• Bitbucket is another set of Git repositories, more suited to the needs of distributed teams. You can use it to upload your code and you can take a look at other repositories. The main difference between it and GitHub is that you can have unlimited private repositories, unlike GitHub’s pricing when it comes to making repositories private. While this makes Bitbucket much more attractive to private teams, it also means that most of the open-source projects out there are hosted on GitHub. This is more attractive based on the large community of programmers actively looking over open-source projects.

Consult and participate in Wikis

When I was finished incorporating code repositories and programming communities into my daily routine, I turned around to Wikis — constantly updated troves of knowledge with tons of user-updated information. I looked to add knowledge, get in touch with other knowledge contributors, and absorb as much as possible.

The following Wiki’s were paricularly useful:

• The learnprogramming subreddit community has already been mentioned above as a great resource. The subreddit has a Learnprogramming Wiki, a collaborative effort between members of that community to create a living, valuable resource that can help you with the very basics of code, from formatting questions to how to debug.
• Wikibooks is a living library of different user-contributed books. Many of them are on programming topics such as this Wikibook on C++ programming, a resource I consulted when I was looking into the language.
• Finally, the Kaggle Wiki is a data science focused Wiki filled with different resources in the space. It’s the creation of Kaggle, an online community of data science admirers who come together to compete on the best machine learning models. You can be certain that the Wiki will contain a lot of resources that will be valuable to your learning journey on programming and data science. This was a resource I recommended often to people looking to learn data science.

Finally, find different approaches to finding jobs

I know what it can feel like to be on the job hunt. You need all of the resources you can get. I was in that place once so I started compiling a list of the most effective job boards and places to find a technical job as I looked into the process.

Here is a list of different job boards you should go to if you’re looking for a programming job and don’t have a degree that can be particularly fruitful for your job search. They’ve been approaches that I’ve battle-tested. Consider it a final conclusion of useful, supplemental resources to finding you the career you deserve.

LinkedIn

Sometimes it’s good to start at the most obvious place. LinkedIn has a large number of technology jobs that you can find quite easily. You can sign up for a free trial of the premium version and quickly look through different jobs.

LinkedIn can also be a great way to research hiring managers and get a sense of what a company is like before you even apply there. You’ll be able to see what the organizational hierarchy looks like by scrolling from one profile to another — and you’ll be able to see what skills the company emphasizes, either by looking at the profiles of those who were hired or by using your trial Premium account and looking at job postings or company pages.

You’ll want to think about how to optimize your LinkedIn profile so you can get the most out of this career-oriented social network. I worked hard on my LinkedIn profile, and now, I get tons of recruiters reaching out to me out of the blue.

Hacker News

Besides being a great repository of technical articles and a community that curates people who are interested in the cutting edge of technology, Hacker News also serves as a job portal of sorts for Y Combinator companies. These are technology companies that might be as young as a two-person startup and also those who have started fully maturing (as an example, Dropbox, Airbnb, and Quora were all at one time or another incubated by Y Combinator).

The jobs section of the site features different YC companies and their hiring needs. There are also monthly threads started by a bot called Ask HN: Who is hiring? where discussions about urgent job opportunities are surfaced that may be hard to find elsewhere. Here’s an example of the latest “who’s hiring” thread in May 2017.

By commenting on different articles and reaching out to different members in the Hacker News community as mentioned before, you’ll reach out to many users who are senior figures in the startup world. You might find your way to different mentors and somebody who can introduce you to the right hiring manager.

AngelList

An online repository for different startups. The jobs on offer here tend to be with earlier stage companies working at the edge of technology. One great perk about this is that entrepreneurs may be more willing to accept people from non-traditional backgrounds to work with them — especially if you’re willing to accept and maybe even embrace the risk that comes with working in a startup.

I managed to get a job by applying to jobs on AngelList, which was as simple as a one-click apply. It was also a great way for me to see what startups were hiring — I highly recommend it!

I hope this list of resources I used is helpful for you! If you want more material like this, please check out my guide to how to get a programming job without a degree.