Using Standards to Align Talent and Employers

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In our rapidly changing world, one of the biggest challenges to continued economic growth is the skills gap, which is the difference between the skills employers are looking for, and the skills available among job-seekers. For individuals, the skills gap limits upward mobility and wage growth. For companies, it limits the ability to hire the teams needed to pursue commercial opportunities.

So what’s stopping the skills gap from being quickly solved? A core obstacle is that individuals don’t know what skills to learn given the lack of clear and consistent guidelines from employers and industries as a whole. When organizations are unsure about the skills they need, they often rely on pedigree (e.g., university degrees) or experience (e.g., previous job titles) in place of specifically stated competencies that drive new, digital functions.

This construct perpetuates the skills gap on both sides of the market. Employers constrain their own talent pipelines, as they only consider a fraction of candidates with skills that match their hiring needs. On the other hand, job-seekers underinvest in new skills, as they lack clear guidance on what qualifications are required to access new roles.

The skills gap continues to grow as more automation in the workplace intensifies the need for new skills across teams. A 2017 McKinsey Global Institute report cites that “in about 60 percent of occupations, at least one-third of the constituent activities could be automated.” To stay employable, individuals need to embrace a mindset of lifelong learning that enables them to upgrade their skills, and move into roles that support and complement new technologies.

These new patterns of learning need to be coupled with additional entry points to careers and objective skill requirements that facilitate workforce mobility. Similarly, the McKinsey report predicts that “8 to 9 percent of 2030 labor demand will be in new types of occupations that have not existed before.” Thus, we must ensure workers possess not only the tactical skills but also have mobility mechanisms in place to transition into these new jobs.

For mobility to scale, job-seekers need employers in a given field to align on a set of requirements that once met, provide access to employment opportunities. One example of this alignment has emerged from General Assembly’s Marketing Standards Board, a group of leaders across the consumer, technology, media, and academic sectors who are defining career paths and critical skills in marketing.

For the past year, the group has worked to provide transparency into the marketing profession. The Board started by creating a three-level framework that defines career paths in marketing. In tandem with these efforts, the board launched the Certified Marketer Level 1 (CM1) assessment, which aligns with the foundational level of the framework. The CM1 is recognized as a standard by a growing number of companies who use it to benchmark the skill levels of their teams. Benchmarking has also proven useful for employers who wish to define and diagnose critical skills across their organizations.

The CM1 is also being used as a standard in the hiring process. General Assembly brought together a group of over 30 companies, including Calvin Klein, L’Oreal, Pinterest, Priceline, and others to recognize the skills tested on the CM1 as a common set of requirements used in recruiting. Each company in this group agreed to interview high scorers on the assessment regardless of candidates’ background. This system of skills-based selection provides new career pathways for individuals who may otherwise be overlooked in a system dependent on pedigree and experience. Among job-seekers, we received tremendous interest in taking the CM1 as an entryway to guaranteed first-round interviews with these companies. Approximately 4000 individuals registered to take the CM1 in just a few weeks, and the top 10% of test-takers qualified for a guaranteed interview.

We were delighted but not surprised to see that top scorers came from diverse backgrounds — from college seniors entering the workforce, to career-switchers looking to get their foot in the door, to experienced marketers looking for a new challenge. Likewise, our previous research in The State of Skills: Digital Marketing 2018 report revealed that strong digital marketing talent can be found outside the marketing function, and from fields such as sales and technology. Moreover, this group of top scorers confirmed that experience doesn’t necessarily predict skills. Rather, giving all registrants the chance to demonstrate their skills using a clear set of skill requirements on the CM1 assessment can create access to new job opportunities.

As a result, our employer partners were able to expand the top of their recruiting funnels, and attract more qualified candidates. These employers are helping to address the skills gap in the industry by using a skills-based approach that increases the overall supply of qualified candidates considered for marketing jobs.

General Assembly’s mission has always been to provide transparent pathways to transformational careers. We’re thankful to the Marketing Standards Board and to the companies that have partnered with us to make strides in this direction. Together, we’re working to increase the transparency and openness of the workforce, broaden talent pools, and create more entry points for aspiring marketers around the world.

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GA’s Credentials team’s mission is to help people get recognized by employers for what they can do, no matter where they come from. To learn more and get involved, get in touch with us at credentials@ga.co. To learn more about the Marketing Standards Board and the CM1 assessment, visit https://generalassemb.ly/marketing-standards-board.

10 Sentences A Product Manager Should Never Say

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Your words can be a powerful ally or your worst enemy. It all depends on how you use them. So, how often do you think deeply about what you are going to say before you say it?

Product managers, in particular, cannot afford to be careless in their speech.

After all, good product management demands leadership and requires frequent conversations with other teams as well as different external stakeholders. These are not casual conversations; instead, they have some urgency and gravity. The success or failure of the product may depend on how well the product manager communicates with others.

But mastering the art of effective communication is not easy. If you are not careful, your words can undermine your effectiveness and authority.

That is why PMs must root out responses that convey a negative attitude and shut down communication, hindering their progress as a team.

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Digital Marketing 101: How the Loyalty Loop is Replacing the Marketing Funnel

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During the past few decades, the marketing funnel served as the primary model for how people learn about a product, decide to buy, and (hopefully) become loyal customers, helping spread the word to others.

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A Machine Learning Guide for Beginners

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Ever wonder how apps, websites, and machines seem to be able to predict the future? Like how Amazon knows what your next purchase may be, or how self-driving cars can safely navigate a complex traffic situation?

The answer lies in machine learning.

Machine learning is a branch of artificial intelligence (AI) that often leverages Python to build systems that can learn from and make decisions based on data. Instead of explicitly programming the machine to solve the problem, we show it how it was solved in the past and the machine learns the key steps that are required to do the same task on its own.

Machine learning is revolutionizing every industry by bringing greater value to companies’ years of saved data. Leveraging machine learning enables organizations to make more precise decisions instead of following intuition.

There’s an explosive amount of innovation around machine learning that’s being used within organizations, especially given that the technology is still in its early days. Many companies have invested heavily in building recommendation and personalization engines for their customers. But, machine learning is also being applied in a huge variety of back-office use cases as well, like to forecast sales, identify production bottlenecks, build efficient traffic routing systems, and more.

Machine learning algorithms fall into two categories: supervised and unsupervised learning.

Supervised Learning

Supervised learning tries to predict a future value by relying on training from past data. For instance, Netflix’s movie-recommendation engine is most likely supervised. It uses a user’s past movie ratings to train the model, then predicts what their rating would likely be for movies they haven’t seen and recommends the ones that score highly.

Supervised learning enjoys more commercial success than unsupervised learning. Some common use cases include fraud detection, image recognition, credit scoring, product recommendation, and malfunction prediction.

Unsupervised Learning

Unsupervised learning is about uncovering hidden structures within data sets. It’s helpful in identifying segments or groups, especially when there is no prior information available about them. These algorithms are commonly used in market segmentation. They enable marketers to identify target segments in order to maximize revenue, create anomaly detection systems to identify suspicious user behavior, and more.

For instance, Netflix may know how many customers it has, but wants to understand what kind of groupings they fall into in order to offer services targeted to them. The streaming service may have 50 or more different customer types, aka, segments, but its data team doesn’t know this yet. If the company knows that most of its customers are in the “families with children” segment, it can invest in building specific programs to meet those customer needs. But, without that information, Netflix’s data experts can’t create a supervised machine learning system.

So, they build an unsupervised machine learning algorithm instead, which identifies and extracts various customer segments within the data and allows them to identify groups such as “families with children” or “working professionals.”

How Python, SQL, and Machine Learning Work Together

To understand how SQLPython, and machine learning relate to one another, let’s think of them as a factory. As a concept, a factory can produce anything if it has the right tools. More often than not, the tools used in factories are pretty similar (e.g., hammers and screwdrivers).

What’s amazing is that there can be factories that use those same tools but produce completely different products (e.g., tables versus chairs). The difference between these factories is not the tools, but rather how the factory workers use their expertise to leverage these tools and produce a different result.

In this case, our goal would be to produce a machine learning model, and our tools would be SQL and Python. We can use SQL to extract data from a database and Python to shape the data and perform the analyses that ultimately produce a machine learning model. Your knowledge of machine learning will ultimately enable you to achieve your goal.

To round out the analogy, an app developer, with no understanding of machine learning, might choose to use SQL and Python to build a web app. Again, the tools are the same, but the practitioner uses their expertise to apply them in a different way.

Machine Learning at Work

A wide variety of roles can benefit from machine learning know-how. Here are just a few:

  • Data scientist or analyst: Data scientists or analysts use machine learning to answer specific business questions for key stakeholders. They might help their company’s user experience (UX) team determine which website features most heavily drive sales.
  • Machine learning engineer: A machine learning engineer is a software engineer specifically responsible for writing code that leverages machine learning models. For example, they might build a recommendation engine that suggests products to customers.
  • Research scientist: A machine learning research scientist develops new technologies like computer vision for self-driving cars or advancements in neural networks. Their findings enable data professionals to deliver new insights and capabilities.

Machine Learning in Everyday Life: Real-World Examples

While machine learning-powered innovations like voice-activated robots seem ultra-futuristic, the technology behind them is actually widely used today. Here are some great examples of how machine learning impacts your daily life:

  • Recommendation engines: Think about how Spotify makes music recommendations. The recommendation engine peeks at the songs and albums you’ve listened to in the past, as well as tracks listened to by users with similar tastes. It then starts to learn the factors that influence your music preferences and stores them in a database, recommending similar music that you haven’t listened to — all without writing any explicit rules!
  • Voice-recognition technology: We’ve seen the emergence of voice assistants like Amazon’s Alexa and Google’s Assistant. These interactive systems are based entirely on voice-recognition technology powered by machine learning models.
  • Risk mitigation and fraud prevention: Insurers and creditors use machine learning to make accurate predictions on fraudulent claims based on previous consumer behavior, rather than relying on traditional analysis or human judgement. They also can use these analyses to identify high-risk customers. Both of these analyses help companies process requests and claims more quickly and at a lower cost.
  • Photo identification via computer vision: Machine learning is common among photo-heavy services like Facebook and the home-improvement site Houzz. Each of these services use computer vision — an aspect of machine learning — to automatically tag objects in photos without human intervention. For Facebook, these tend to be faces, whereas Houzz seeks to identify individual objects and link to a place where users can purchase them.

Why You and Your Business Need to Understand Data Science

As the world becomes increasingly data-driven, learning to leverage key technologies like machine learning — along with the programming languages Python (which helps power machine learning algorithms) and SQL — will create endless possibilities for your career and your organization. There are many pathways into this growing field, as detailed by our Data Science Standards Board, and now’s a great time to dive in.

In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down these three data sectors. These skills go beyond data to bring delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown.

Individuals can use data know-how to improve their problem-solving skills, become more cross-functional, build innovative technology, and more. For companies, leveraging these technologies means smarter use of data. This can lead to greater efficiency, employees who are empowered to use data in innovative ways, and business decisions that drive revenue and success.

Download the paper to learn more.

Boost your business and career acumen with data.
Find out why machine learning, Python, and SQL are the top technologies to know.

Download the eBook

UX Design Explained in 60 Seconds

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User experience (UX) design is one of the tech industry’s core disciplines: Considering users’ potential actions is a key component of designing a website, application, or other products. UX is a skill that just about every type of company needs in order to grow — and demand for it is only increasing.

But what is UX design, really? To get to the heart of it, we talked to design experts from The New York Times, PayPal, Zola, and more.

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SQL for Beginners

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Today we’re constantly bombarded with information about new apps, hot technologies, and the latest, greatest artificial intelligence system. While these technologies may serve very different purposes in our lives, many of them have one essential thing in common: They rely on data. More specifically, they use databases to capture, store, retrieve, and aggregate data.

This begs the question: How do we actually interact with databases to accomplish all of this? The answer: We use Structured Query Language, or SQL (pronounced “sequel” or “ess-que-el”).

Put simply, SQL is the language of data — it’s a programming language that allows us to efficiently create, alter, request, and aggregate data from databases. It gives us the ability to make connections between different pieces of information, even when we’re dealing with huge data sets.

Modern applications can use SQL to deliver valuable pieces of information that would otherwise be difficult for humans to keep track of independently. In fact, pretty much every app that stores any sort of information uses a database. This ubiquity means that developers use SQL to log, record, alter, and present data within the application, while analysts use SQL to interrogate that same data set in order to find deeper insights.

SQL at Work

A wide variety of roles can benefit from using SQL. Here are just a few:

  • Sales manager: A sales manager could use SQL to increase sales by comparing the performance of various lead-generation programs and doubling down on those that are working.
  • Marketing manager: A marketing manager responsible for understanding the efficacy of an ad campaign could use SQL to compare the increase in sales before and after running the ad.
  • Business manager: A business manager could leverage SQL to streamline processes by comparing the resources used by various departments in order to determine which are operating efficiently.

SQL in Everyday Life: Real-World Examples

We’re constantly interacting with data in our lives, which means that, behind the scenes, SQL is probably helping to deliver that information to us. Here are a few examples:

Extracting Data

At its most basic, SQL is about accessing data locked away in databases. Think about the last time you received a report about how your company or team is performing. This probably had some key metrics like sales figures, conversion rates, or profit margins based on data stored in a system like a customer relationship management (CRM) or eCommerce platform.

A developer or analyst, or maybe even you, used SQL in order to access the data needed to produce that report.

Web Applications

Think about the last time you looked up the name of a movie on IMDb, the Internet Movie Database. Perhaps you quickly noticed an actress in the cast list and thought something like, “I didn’t realize she was in that,” then clicked a link to read her bio.

As you were navigating through that site, SQL may have been responsible for returning the information you “requested” each time you clicked a link.

Synthesizing Data to Make Business Decisions

With SQL, you can combine and synthesize data from different sources, then use it to influence business choices.

For example, if you work at a real estate investment firm and are trying to find the next up-and-coming neighborhood, you could use SQL to combine city permit, business, and census data to identify areas that are undergoing a lot of construction, have high populations, and contain a relatively low number of businesses. This might present a great opportunity to purchase property in a soon-to-be thriving neighborhood!

Why You and Your Business Need to Understand Data Science

On a high level, data professionals collect, process, clean up, and verify the integrity of data. They apply engineering, modeling, and statistical skills to build end-to-end machine learning systems that uncover the ability to predict consumer behavior, identify customer segments, and much more. They constantly monitor the performance of those systems and make improvements wherever possible.

Looking at the field as a whole, there’s a wide array of tools available to help data experts perform tasks ranging from gathering their own data to transforming it into something that’s usable for their needs.

In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down these three prevalent technologies that are transforming how we understand and use data. The first two are programming languages used to gather, organize, and make sense of data. The last is a specific field in which data scientists and machine learning engineers, using Python and other technologies, enable computers to learn how to make predictions without needing to program every potential scenario.

These skills have surprising uses beyond data, bringing delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown. Download the paper to learn more.

Boost your business and career with data.

Find out why SQL, Python, and machine learning are the top technologies to know.

Download the eBook

5 Ways to Inspire Your Design Teams

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Tyler Hartrich, faculty lead for General Assembly’s User Experience Design Immersive course, leads a session at the 2018 99u Conference. Photos by Craig Samoviski.

As design educators, we at General Assembly prepare students for their careers — but how can we ensure designers continue to grow their skills beyond the classroom? Industry-leading work emerges from teams that persistently enrich themselves by fostering new skill sets and perspectives. But between deadlines, client fire drills, and day-to-day trivialities, a focus on growth can often be put on the back burner. In the long-term, this can result in uninspired designers who don’t grow to their full potential, and teams that opt for the easy way out instead of taking on risks, challenges, and explorations that drive innovation.

When Adobe approached General Assembly about leading a session at the 99u Conference — an annual gathering for creative professionals to share ideas and get inspired to help shape the future of the industry — we knew it would be a great opportunity to guide leaders in creating natural spaces for learning within their teams and workflows.

In our sold-out session “Onboard, Engage, Energize: Tactics for Inspiring a Crack Design Team,” Tyler Hartrich, faculty lead of GA’s full-time User Experience Design Immersive course, and Adi Hanash, GA’s former head of Advanced Skills Academies, shared insights on how directors and managers can structure spaces for learning within their teams, and encourage new approaches to problem-solving. The presentation was developed in collaboration with Senior Instructional Designer Eric Newman and me, GA’s director of product design.

At the event, we outlined the following five ways leaders can encourage their teams (and themselves) to keep learning and improving throughout their careers, including an exercise to spur creativity, reflection, and action. Read on to learn more, and find out how you can perform the exercise with your own team.

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Eric Ries on 5 Lessons Companies Can Learn From Startups

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Since the Great Recession in 2008, startups have become a major force in society. Today’s entrepreneurial culture — with lower financial barriers to launching a business and people’s increasing desire for flexibility, freedom, and purpose in their work — has bred a whole generation of young companies that have quickly scaled and revolutionized a wide range of industries. A number of those companies, like Airbnb and Uber, have achieved explosive growth and evolved into bonafide conglomerates in recent years.

Meanwhile, older organizations looking to remain relevant and thrive are striving to figure out the practices that allow these startups to excel — and how their corporations can adopt them in order to catch up.

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General Assembly Joins the Adecco Group in Transforming the World of Work

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General Assembly Adecco announcement

GA was founded on the principle of empowering people to pursue the work they love. In the eight years since we opened our first campus, we have had the privilege of working with students, governments, and the world’s largest companies to create opportunities to radically transform careers and economic prospects.

Today I’m excited to announce that we have reached an agreement to be acquired by the Adecco Group. This is a milestone, a reflection of the world waking up to the skills gap we face, and the opportunity to reshape the relationship and connection between education and the world of work. It’s the result of the passion, commitment, and hard work of thousands of individuals. It’s also the output of the incredible focus and determination of our students, our instructors, and the tireless GA team. For all of those reasons, I’m thrilled to get to share the news.

The Adecco Group is a Swiss-based, truly global company operating in 60 countries that offers 360° HR solutions from flexible to permanent employment, career transitions, and talent development services through its network of independent brands. On my first trip to Switzerland to meet CEO Alain Dehaze, I was deeply impressed by the Adecco Group’s commitment to its people, values, and mission, and struck by what a powerful platform it could be for General Assembly’s vision. We were exuberant at the idea of joining forces, and shaping the future of work, talent, and education. The possibilities to expand the scope of what we can do, and the impact we can make, are almost limitless.

Because of the unique structure of the Adecco Group, we were able to craft a structure where General Assembly will run as a fully independent company underneath its large umbrella. We will, however, be able to leverage the knowledge and network of the world’s largest human capital company. Our mission and vision won’t change, but our ability to provide opportunities to our alumni, students, instructors, and clients will massively increase. In all the important ways we will still be GA, only better.

When my co-founders Matt Brimer, Brad Hargreaves, and Adam Pritzker and I started GA, we wanted to build a community focused on “learning by doing” in New York City. Today, that idea has evolved into a global school that helps amazing individuals and Fortune 500 teams. We have 20 campuses on four continents, more than 50,000 full- and part-time alumni, and over 500 team members who work incredibly hard on behalf of our worldwide community.

I am excited about the power of our partnership with the Adecco Group and what we can do together. The future of work has never been more important and I look forward to helping shape it for many years to come.

How the Marines Prepared Me for a Career in Coding

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While stationed in Okinawa, Japan, in 2008, I wouldn’t have guessed that my time in the Marine Corps would have prepared me for a future in coding. At the time, the 30 Marines in my platoon had access to just one shared computer. It served only two functions: completing online training requirements, and looking up one’s online military record. I never suspected that nine years later I would be designing and building websites and applications in an intensive software engineering course, General Assembly’s Web Development Immersive, now called Software Engineering Immersive (SEI) course.

My path toward coding was a winding one. As a Marine on active duty, I was stationed in Japan, Kenya, Sudan, Italy, and Pakistan. Later, after transferring to the Marine Corps Reserve, I pursued a bachelor’s degree in international affairs from George Washington University. While studying at GW, I worked at the nonprofit Veterans Campaign, where I was tasked with helping to rebrand the organization. Though I had little technical experience, I created an entirely new web presence for the organization and migrated its old content to the new website.

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