This is the last call to join the the first cohort of our 60-day coding interview crash course, running from January 4th to February 28th. I'll be going through the course with everyone and will post biweekly updates. If you've already emailed us about joining, you're already accounted for! This will be the final general email about the crash course. All future updates will only be sent to crash course participants.
For those on the fence, here are some answers to frequently asked questions:
What is this? - When I surveyed existing students, the biggest issue wasn't understanding the material, it was making the time to go through the course and having support when needed. The goal is to work through this shortened curriculum together, and have access to others in the community that are working through the same lessons and problems. And since I'll be going through the course with everyone, my inbox (and the team's) is available for any questions you might have about the material. As it'll be top of mind, you'll get help much faster and from multiple sources.
What's required of me? - Simply check your dashboard for each week, and do the work! If you have any questions or feedback about the material, post in the forum or email firstname.lastname@example.org. After the 8 weeks, if you found it useful or land a job, please let us know and consider helping us with an email or video testimonial, or by sharing the site. Share your progress on social with hashtag #algodaily or #algodaily60!
How much time will this take? - Expect it to take about 10-20 hours a week, depending how much you choose to take on.
I don't have time to work through all of the tutorials this week. - No problem. It is A LOT of work to go through all the suggested material each week. As long as you're making strides every day, you can always catch up after the 8 weeks. We try to have one big "focus" for each week (e.g. "trees" or "linked lists"), so you get a chance to catch up each time.
What's in it for you, Jake? - Great question! The big focus of 2021 is not about pumping out more content, but ensuring student success. It is extremely helpful for me and the team to get feedback on what tutorials are confusing, where folks are getting stuck, if there are bugs on the platform, and how we can help you land a better job. Shout out to members Ray, Dmitry, Jacob, and bsanneh for providing useful feedback in just the last few days.
How much will this cost? - It's tacked on as part of our premium membership. If you're already a premium member, just email us to get updates. We think this is extremely fair given that similar cohort based courses are typically in the $1000 - $7000 range. Can't afford the course? I still want to help! Subscribe to the youtube channel for free coding video tutorials.
OK, let's do it! How do I get started? - Reply back to this email for updates as we work through the next 8 weeks. If you've already expressed interest, you're on our list. Feel free to share this on social with hashtag #algodaily or #algodaily60!
Most developers, particularly junior engineers, are wary of systems design interviews. The assumption is that, while algorithms are difficult to master, they can be learned with sufficient practice.
But systems design interviews seem more daunting because of how open ended they are. In the real world, the architecture of a system takes weeks or months to properly think through and plan for. During interviews, not only are you tasked with designing one in 45 minutes, but you may not even have the proper building blocks at this stage in your career.
How can you build a scalable chat app without understanding event queues? Or architect a fast search engine without knowing about indexing?
The fastest way I've found to grasp these building blocks is to seek out existing scalable systems. We can work backwards-- there are certain pieces of software that have revolutionized the industry, and their original white papers often reveal secrets to reliability and scalability.
To that end, we have a surprise at AlgoDaily! We've summarized 7 of the most famous software white papers, and outlined them in plain English. If you go through one a day (roughly 15 minutes per read), you'll be in the zone come interview time.
The above outlines are included as part of the full course, and are immediately accessible to all existing premium members. If you find it useful, we'd love to know!
Not a member? Buy today at the following link: https://algodaily.com/subscriptions/discounted, now 50% off! We'll continue to update you as we add new content.
There's a few computer science principles that can be surprisingly applied to general life. As an example, the notion of greedy algorithms. One can liken the idea of optimal choice at every iteration to trying your best daily so that it works out over the long term -- seems reasonable.
Another one is the idea of instructions, that is, single operations for the processor. Code in aggregate is difficult to comprehend, but when taken line by line, make for a narrative of sorts. The key is that the big concepts are broken down as much as possible, to the point where the processor can accomplish a single task.
This works outside of computer science too. The notion of building a great career over 40 years can be intimidating, but can also be broken down: are you improving technically? Are you taking on harder tasks? Do you demonstrate enthusiasm?
Let's say you do want to improve technically. What area do you want to improve? Maybe your database systems knowledge needs some work. Perhaps you haven't worked much in the browser, but want to learn more.
Once you've determined the area, the actual step may be to read an article or watch a video on the topic. At this point, you have an actionable task that you can do and check off, knowing full well that you've made progress toward your goal. You can't decide that this afternoon you want to "build a great career" or "master database systems", but you can go through a few Lynda videos on the topic or scaffold an app.
If you've been meaning to start something and have been feeling resistance, try breaking things down until you can do something, and see if that helps.
I'm trying something new: AlgoDaily was created to help developers land their dream jobs, but a huge part of why we want them is to build wealth for ourselves and loved ones. I'm going to start profiling software engineers from humble means who've built generational wealth, both on Twitter and this newsletter.
As of this writing, there's a lot of political unrest in the United States-- but imagine growing up in an environment far worse and having to flee. Jan Koum grew up by Kyiv, Ukraine before moving to California, and his family had to rely on a social support program to get by.
Growing up, he often got into trouble, and barely graduated high school. What kept him going was a deep interest in computing and networking, which he tended to by buying and returning books as soon as he read them.
Eventually, he became skilled enough to land a job at the former powerhouse Yahoo!, and quit college to work there full time. But like many smart engineers, he eventually got bored and looked for opportunities outside of Yahoo! One of them was Facebook, but he was quickly rejected.
Not one to despair, Jan kept on looking for new ideas and eventually bought an iPhone. Realizing that this was going to change the way people interacted forever, he started working with a friend to build a communication app for it. He chose the name WhatsApp because it sounded like “what’s up”.
At first, WhatsApp was largely unpopular— but then Apple added “push notifications”, making it much more useful. By the time a fellow named Mark Zuckerberg asked Jan to dinner to discuss a deal, the app had 400 million users. Today it has over 2 billion.
The big takeaways from Jan’s story? First, rejection can lead to an even better path in life. And secondly, technological shifts reveal some of the best business opportunities and pivots.
If you learned something from this thread, consider following me on Twitter at @jzraps. Also be sure to check out the most accessible technical interview course available-- sale extended through this weekend.
Getting hired for a tech job is not the easiest thing to do. Although there's undoubtedly a significant shortage of tech specialists in America, it doesn't make the playing field less competitive. That's why you need to play your cards right and go for those skills that will be highly in-demand in the next couple of years. Here we've compiled a list of the most sought-after skills in the industry based on multiple online reports including Indeed Hiring Lab.
Machine learning, which falls under the artificial intelligence umbrella, is a powerful skill that is expected to change several industries including drug development and the stock market. This technology involves programming a computer to learn by itself without human intervention. Through different programming practices, a machine learning engineer can make a machine identify objects, patterns, and anomalies in datasets.
Since there's no designated bachelor's degree for machine learning, people who want to become machine learning engineers need to opt for a computer science degree, bootcamps, or self-learning. However, don’t worry, because there are some great machine learning bootcamps out there, such as the ones offered by Galvanize. At the school, you'll learn the fundamentals of mathematics and statistics, as well as programming languages like Python, among others.
Data science is the ability to study data to get meaningful insights to solve problems, identify phenomenons or errors. Data scientists are some of the leading tech professionals today, and this trend will only continue to grow. Companies that don't have the help of a data scientist are at risk of falling behind in their marketing strategies and business decisions. So this could give you an idea of how essential this skill is for every company.
Now, if you'd like to become a data scientist, you'll need to have some mathematical knowledge, as well as statistical and analytical thinking. There are some outstanding schools out there that can help you become a certified data scientist. Springboard's data science program, for instance, will introduce you to the most crucial aspects of machine learning, such as data filtering, data analysis, and even data visualization, among other relevant subjects.
Software engineering is another fundamental tool for companies to optimize their operations. When there are repetitive and redundant tasks, a software engineer could create a solution that helps smoothen and quicken the process. On top of that, they are responsible for creating, maintaining, and updating software. That's why a software engineer is usually a long-term position.
With the evolution of big data, the need for cloud engineers has also grown. Companies no longer rely on large rooms to hide and save their databases; they're using cloud systems that are more secure and accessible. Cloud engineers are the ones responsible for building and maintaining these systems to ensure they remain efficient and safe from potential threats.
To become a cloud engineer, you need to understand platforms such as Microsoft Azure or Amazon Web Services (AWS). In order to pursue this career, you need to get a certification that validates your proficiency in any of these platforms. Some of the best AWS bootcamps** **out there are the ones from Coding Dojo or Code Fellows. Both schools provide students with flexible payment methods and schedules.
This is the era of data; almost everything relies on it. Therefore, digital products and platforms must be created in a way that they're able to have efficient data systems. SQL is the coding language that is used to create and maintain database programs. This skill is especially important for data scientists or other professionals who work with databases and data-driven systems. If you want to get some examples of real-life situations when you use SQL-driven platforms, just think about technology or devices that depend entirely on their data systems like news platforms and Amazon’s virtual assistant, Alexa.
SQL is a programming language, so there are some basic courses where you can learn this skill. such as General Assembly's beginners SQL bootcamp. In this course, you'll learn everything you need to know to master this skill in a short period of time.
As mentioned, data is everything these days, so it's no surprise to hear that companies want to keep it as safe as possible. If fallen into wrong hands, datasets that contain a lot of sensitive information could jeopardize the actual safety of the masses and put businesses in a bad financial state. Therefore, it has become imperative for most organizations to hire cybersecurity engineers to keep their data safe. After all, a criminal cyberattack could become a company's worst nightmare. A cybersecurity engineer is a professional who creates policies and systems to protect data.
Flatiron School's cybersecurity bootcamp could help you understand all the processes involved in the creation of cybersecurity policies and systems. In this course, you'll be trained by cybersecurity veterans and you'll have one of the best labs in America for your practices.
Python is a very in-demand tech skill these days and will continue to be so in the next couple of years. It is a programming language used by data scientists and machine learning engineers, among others. If you're new to tech terminology and know nothing about coding, Python can be the best first language you learn because it is easy and straightforward to use. The syntax is simple and some even say it's similar to writing in English.
If you want to dive into this skill, you can take a look at some of the best Python bootcamps out there, such as the ones from Coding Dojo or Code Fellows.
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Welcome to the most accessible guide to technical interviews. AlgoDaily was created to be a gentle, visual introduction to patterns around solving data structures and algorithms challenges.
We believe that technical interviews are a matter of practicing well. We've referenced hundreds of resources on habit change, education design, and algorithms to design the best and most streamlined learning experience.