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.

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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Creative Design Inspiration – 5 Ways to Motivate Your Design Team

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2018 99u Conference General Assembly
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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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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Digital Marketing 101: Creating Your Digital Marketing Calendar

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This post is part of our Digital Marketing 101 series. Sign up to get the full series!

Everything we’ve discussed so far in this Digital Marketing 101 series has focused on what to do and a bit about how to do it. But in marketing, timing is everything, and the two parts of timing in marketing are frequency and consistency. So here we’re going to move past what and how and look into when. The most valuable tool in your digital marketing arsenal will help you know when to do something, help you maintain your frequency, and, more importantly, your consistency. That tool is your digital marketing calendar.

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Digital Marketing 101: Measuring Your Digital Marketing Efforts

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This post is part of our Digital Marketing 101 series. Sign up to get the full series!

“You can manage only what you measure.” There are many different versions of that mantra, and all of them hold true. Just as in fitness and weight loss, if you don’t start with a baseline, take regular measurements, and see what’s working, you can’t make data-driven decisions.

In this second post of six in the series “Digital Marketing 101,” we’re offering up highly practical tasks for you to determine how best to grow your digital presence using data backed by marketing analytics.

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