The Career Hacking PodcastHey, and welcome back to the Career Hacking podcast! The official podcast of WehnerEd.com. The web's most comprehensive online education resource for those looking to level-up their skills and empower their lives to reach new levels of success.

In today’s episode we’ll answer a question we probably get asked the most often and it’s a great first question when deciding if a new career is right for you. The question we’re answering today is which jobs are most in demand today?

As mentioned in the first episode, WehnerEd focuses on (digital skills and careers) as technology continues to overtake each industry.

Regardless of where your passions reside and which industry you work in, a digital skillset will create new opportunities for you and will likely boost your salary while increasing your work flexibility.

To stay relevant and attract new customers it’s a must for companies to manage a website, apps, digital marketing presence and increasing numbers are utilizing AI and smart algorithms to optimize products   internal decision making. Any roles that help to build a company’s online presence, integrate technology into their products, or leverage data to optimize and grow profits are going to be the jobs with the most growth over the coming decade. Grocery stores use AI to send me personalized coupons and boost their sales. Sports teams are tracking player performance on the field to build optimal performance. Fashion and music industries are using code to analyze historical trends data to minimize risks and nearly guarantee hits with their audiences.

So then, it makes sense that the most in-demand jobs right now are the ones that help these companies complete their digital re-inventions. These roles include web developers, mobile app developers that create software for android and iOS devices, software engineers, digital marketers, Data Analysts, Data Scientists, and artificial intelligence.

 

Let’s start with a web developers.

[For those that aren’t familiar, these entry level roles generally come in two flavors: front-end and backend development.

  1. For example, if a sleek sports car catches your eye, the streamlined profile and crisp edges are the work of the ‘front-end web developer.’ The raw horsepower and tuned suspension that leads to impressive driving performance is the work of the ‘backend web developer.’ Finally, the ‘full-stack developer’ oversees and manages the whole sports car development. They must have expertise in eye-catching design and user experience (front-end) and understand all of the cooperative interconnections through to the deepest depths of the engine components and fuel lines (backend + database).
  2. In direct terms, the front end developer creates all of the forward-facing web interfaces that users directly interact with. If you can see it on the screen, the front-end developer made it happen. As described on bloc.io, “The front end of an application is less about code and more about how a user will interpret the interface into an experience.”]

Additionally,

A full-stack developer has the combined knowledge of both front end and backend developers. In some cases they architect and manage the whole web design process including the front-end the backend, databasing and all of the interconnections in between. Full-stack developers are familiar with the entire web development process and are responsible for planning designs ahead of time and guiding development teams through implementation. Additionally, some of the best companies that have their pick of the best talent only hire full-stack developers. In this case, each developer has a strong understanding of the entire development strategy even if they’re personally working to only create a portion of it.

Picture a tunnel being bored toward the center from opposite sides of a mountain. Each tunnel doesn’t have a precise understanding of the other side, and could make a mistake when attempting to meet in the middle. Because Full stack developers have a broad skill set of both front and back end development, they are able to create value by seeing problems before they arise and architecting efficient means of simultaneous development.

Finally, an additional type of web developer are those who create interactive websites using Wordpress. Rather than creating completely custom sites like twitter and facebook with unique functionality not offered anywhere else, some web developers create websites for small businesses using a foundation called WORDPRESS…… Wordpress is an open sourced and  PHP-based website creation tool which makes creating a website incredibly easy for people like you and me. Where WordPress developers create their value is their familiarity for the vast number of themes and plugins that are available to customize a Wordpress website. With the help of several Wordpress developers over the past few months, I have been able to identify a sleek and modern theme to represent the brand of WehnerEd and have integrated add-on functionality for publishing this podcast, scheduling 1-on-1 consulting calls and more. Probably the fastest way you can start earning money by learning a new digital skill is by mastering Wordpress and PHP to create fantastic websites for aspiring businesses.

Next, moving away from development for the web we get to Mobile App developers

  • iOSis the operating system for Apple’s mobile devices including iPhone, iPad, and iPod Touch. An iOS developer is responsible for developing apps that are lightweight, responsive, intuitive, web-enabled, social, visually appealing, media-rich, and addicting.
    • Main language to learn is Swift, but there are many other design and infrastructure skills that are critical to learn as well. Check out the  full run down on the Digital learning plans page on our site, or check for links in the podcast show notes
  • Android- As the most popular mobile operating system in the world by a long shot, you better believe that Android developers will be in high demand long term.
    • Main language to learn is Java, but there are many other design and infrastructure skills that are critical to learn as well. Check out the  full run down on the Digital learning plans page on our site,

Additionally, many software roles exist that require skills specific to a company’s products. Rather than running on the web or on your mobile devices, software such as turbo tax is designed to run on your windows or Mac computers.  On hiring websites you’ll see roles for Java or C++ software engineers.

 

Our next hottest careers are Data Analyst & Data Scientists

  • A data analyst is the first line of defense between a new set of data and the data scientist. The analyst creates queries to form an initial understanding and prepare the data for the data scientist to complete further analyses on… that then drive outcomes for the business. The analyst is also responsible for making easily-interpreted visuals of data sets to communicate whether the data is useful or not toward business goals.
  • A data scientist has the impressive job of creating tangible outcomes using a combination of machine learning and statistical models from heaps of parsed data from the data analyst. Examples of data science outcomes include grocery stores using your purchase history to send you targeted coupons, and industrial companies using data captured from a fleet of products to predict individual failure modes before they happen. Data analyst roles are generally the entry-level jobs for careers in data science while the data science roles require strong statistics and problem solving skills paired with programming skills to actually process the large data sets.

 

Digital Marketer

  • These individuals manage external company brand and develop outlets to attract customer engagement through social media online advertising.
  • A key to being a successful digital marketer is finding where desired customers exist on the web and attracting them to the company website where products can be purchased.
  • A successful Digital marketer is able to find their targeted customers in their spots on the web so that targeted ads can more efficiently be deployed to users who are more likely to buy products. In the early days of buying ads on the web, advertisers would pay for banners to appear in sidebars of sites all over the web. Nowadays ads are much more efficient and are only posted in specific places for their specific audiences to see. For example, imagine you’re a digital marketer for a company that sell beauty supplies such as nail polish and makeup. Do you think your TV commercials will be more effective shown during a wrestling match or during the bachelor? Similarly, by analyzing population and demographic trends on the web, effective digital marketers are able to find their targeted customers in specific places and display ads to users who are more likely to buy products. These practices have made advertising much more efficient.
  • The best digital marketers also frequently use tools to analyze their performance. Are certain sponsored keywords on google driving better results? Are more site visits driven by tweets with certain hastags? By reviewing past results and constantly retooling strategies, today’s digital marketers earn competitive salaries by driving big value for their employers.

 

Finally, the last set of in-demand roles that I’ll review today are those in Artificial Intelligence, Machine Learning, and Deep Learning. I’m truly in awe at the developments taking place in these fields and the potential impacts they'll likely have on our future. While I must admit that careers in these fields are the toughest to achieve on this list, there is a significantly higher demand for these jobs than there are people available to fulfill them. By definition, that makes these jobs the hottest on the list, and again, they may not be as hard to achieve as you may think with companies like Udacity providing degrees in machine learning for less cost than a single semester at a university. For a nearly-guaranteed job placement once your firmly grasp these skills, they could certainly be worth taking a look.

 

First off, what is artificial intelligence? At face value it’s actually not as complicated or futuristic as it sounds. AI is largely already integrated into our lives in many ways you may not be familiar with.

  • Have you recently made a reservation online and received a confirmation in your email inbox? If you use gmail, Google automatically creates a reminder in your Google calendar after recognizing the email and running a simple script of code.
  • Have you noticed how Netflix makes a list of recommended movies based on movies you’ve watched and other’s you’ve rated? This is another way that AI is already engrained in our lives.
  • Amazon does the same to recommend new products for you to buy, and Facebook uses AI to curate the advertisements that it shows next to your news feed.

Basically, AI is simply a hard-coded programming script (that is, a chunk of code that doesn’t frequently get updated) that provides a useful output from a raw and otherwise useless input. In the case of Netflix, this raw data is a list of movies you’ve watched and the output is the curated list of movie suggestions.

At the core of the program is a system of categorizing or organizing the raw data. You can categorize movies by their directors, main actors and actresses, genre, length, subject topics, user ratings and popularity. Wth a livery of these attributes, a program can be developed to provide individualized recommendations to users. AI is only as intelligent as the programmer’s logic enables. It can be simple or complex, but only as smart as the developer defines.

This distinction is where Machine Learning improves upon AI. Machine Learning takes AI a step further in that it’s code allows for self-optimization based on feedback. For example, Alphabet uses machine learning for their self driving car technology developed by Waymo. Have you used a sign-up form recently and had to click the box saying “I’m not a robot?” The last few I have done have miscellaneous pictures from the roadside and ask me to click on the images that contain cars or street signs. This is an example of how machine learning gets smarter. Waymo loads endless amounts of raw data into their self-driving car databases so that the system can be continuously learning. Based on loads of known image data about what’s a car and what’s a street sign, the driving algorithm them makes guesses about where these objects appear in new image data. The data is also fed into these forms so that people can confirm or disagree whether the objects actually appear where the software thinks it does. With this new information, the software then grows smarter and is able to better predict in future datasets. In this way, machine learning has an extra layer of complexity beyond AI code that allows it to continuously improve and perform better over time.

 

Machine learning

An artificial intelligence developer creates software that enables computers to achieve ‘smart' functions. These operations are generally narrowly defined and are only useful in specific situations. I.e. IBM Watson competing on Jeopardy, Deep Blue defeating a chess grandmaster, and Google reading your emails to provide handy services. Unlike machine learning, artificial intelligence software doesn't¬†learn or improve on its own and is only as smart as the static code that enables it. Whereas artificial intelligence is only as ‘intelligent' as the rules that are defined by the programmer, machine learning software takes it a step further as it has the ability to ‘learn' by optimizing its rules along a certain dimension. Machine learning is designed to optimize in a pre-defined area by testing many different methods to achieve the best result. A great example of machine learning is Google self driving cars project…. Constantly learning and improving (human confirmation of street signs and traffic signals) to make driving increasingly safe 

 

Deep learning takes this a step even further in that it’s theoretically able to teach itself even beyond the knowledge of humans. Whereas you can only consume a small amount of books and media in 24 hours, a deep learning computer could consume heaps of information in this same time period, save it all to memory, and use the information to make intelligent inferences about the world.

Take for example, Jarvis from Iron Man

  • Tony Stark asks Jarvis to run an analysis of all the known elements to find an optimal power source for the arc reactor in his chest.
  • Conversational computer that can make inferences and jump to conclusions… even if those conclusions haven’t yet been tried before by humans
  • In the future, we can potentially ask a deep learning  questions like “is the earth warming faster this year than before the industrial revolution, and if so, how do we fix it?

More info on machine learning:

https://www.youtube.com/watch?v=z-EtmaFJieY

 

So you might be thinking to yourself- great… Ross. I get that digital careers are growing faster than ever, but without any semblance of relevant experience nor a background in mathematics or engineering, how realistic is it really that I could get a role in one of these fields? The truth is that getting a job in a digital field isn't as far away as you may think. Today's online education courses provide significant improvements over the traditional education that you're accustomed to… where you're sitting in a classroom with a large group and a single instructor. Affordable classes are available that can work around your schedule while also providing assignments that can train you in the skills needed for these highly saught after jobs in only a matter of months. College degrees are no longer absolute requirements for technical jobs and instead… the hiring process places more emphasis on a candidate’s experience with a given skill set more so than the name on their diploma. By mastering the skills you learn in online courses through hobbies and projects on your own…. or freelancing gigs you complete for your own customers… you can learn the jobs that seem so inaccessible to you today.

 

The career hacking podcast strives to make our content as interesting and approachable as it is helpful. If you found this episode helpful, please share your support by giving us a 5-star review in iTunes and recommend our podcast within your own circles. These will help us reach a larger audience and jumpstart more people who could be settling for less. Please also consider supporting us at Patreon.com/WehnerEd. You can find additional resources… view show notes… submit your own questions for the podcast…  or receive personalized 1-on-1 career coaching at our website: WehnerEd.com