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Alumni Story: From Idea to Kickstarter Sensation

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Student Chris Place

Many people have creative product ideas, but don’t know how to turn them into a reality. That rang true for Product Management grad Chris Place, who wanted to solve a common problem: People aspire to bring lunch to work, but often fail. He turned to GA’s Product Management course in Hong Kong to give him the tools to create and launch Prepd, a sleek lunchbox and companion app that aims to make meal prep fun.

“GA helped me understand marketing and creative storytelling,” Place said. “How can I tie together my product skills with a compelling marketing plan to bring my product to launch?” After the course, he leveraged his learnings to launch a wildly successful Kickstarter campaign that raised $1.4 million to make Prepd a reality. “We never expected this to get this big,” Place says.

Announcing Our New Course for Software Engineers

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sei_courseillustrationssvg-large-email

Allow us to reintroduce ourselves.

We’re excited to announce that our flagship program just got a full upgrade for 2019: Web Development Immersive (WDI) is now Software Engineering Immersive.

Keeping our programs tightly linked to market demand is at the core of General Assembly’s mission. It’s part of our commitment to ensuring our graduates can secure great jobs and build meaningful careers using their new skills.

To keep ahead of rapidly changing industry needs, we do our research, working closely with employers, practitioners, and students to make impactful updates that help grads launch new careers. We dive into questions including:

  • What roles are employers looking to hire?
  • What types of jobs do our graduates get, and with what titles?
  • What are broader trends across the industry?
  • And, most importantly, how can we synthesize all of this to ensure our students have the most relevant, in-demand skills they need to succeed?

Since 2012, more than 8000 adults have taken WDI — a rigorous full-time, three-month program with dedicated job support. More recently, we’ve invested in expanding our offering in a few significant ways, leading us to shift our emphasis to software engineering.

What’s New

  1. We added a deep computer science focus.

In the simplest terms, we’re arming our students with the theory behind how computers and applications work. We’ve added 30 hours of in-class and online instruction in computer science concepts. This new content equips students with the ability to describe the “why” behind what they’re doing as they create algorithms, data structures, and design patterns — skills already fundamental to the learning experience in WDI. The ability to understand and demonstrate the “why” is critical for succeeding in technical interviews, and our hands-on approach gets them ready through mock interview questions and challenges.

  1. Spotlight on high-demand languages and frameworks.

As the skills and tools that drive web development evolve, companies have gone from wanting static webpages to needing sophisticated web applications that respond to client needs in real time. Knowing HTML, CSS, and basic JavaScript is no longer enough; roles now require a full suite of engineering skills in order to create complex, scalable web applications. Over the years, we’ve made countless upgrades to our curriculum, integrating high-demand languages and frameworks like Ruby on Rails, Python, Django, React, Angular, and Ember.

  1. Free foundational prep course.

We know our courses are tough; it’s what makes them so effective at landing people jobs. However, we also believe that, with the right preparation, dedication, and support, anyone can make it. To help ensure that students are ready to hit the ground running on day one of class, we’re offering totally free training that covers the foundational elements of software engineering.

  1. $0 upfront tuition options.

We want students to be able to focus on what really matters: their education. To create more pathways into our classrooms, we’ve launched payment opportunities like our Catalyst program. This income share agreement empowers students to take our courses at no upfront cost and only begin paying back their tuition once they have secured a job. Learn more about our flexible financing options here.  

  1. Real-world development workflows.

To ensure our grads enter the workplace ready to perform, we now go beyond full-stack training by replicating real-world engineering scenarios. Our enhanced emphasis on version control, writing specifications, the product development life cycle, design patterns, code refactoring, unit tests, and managing dependencies rounds out the essential competencies for today’s software engineers.

What Hasn’t Changed

Our proven approach to developing industry-relevant curriculum remains the same: we partner with top employers and practitioners in the field to ensure our offerings are tailored to meet today’s needs. And, as with all Immersive course participants, SEI students receive dedicated support from expert career coaches from their first day of class to their first day on the job. Diving deep into personal brand building, technical interview prep, exclusive networking events, portfolio development, job search roadmaps, and more, we’re there at every step of the job hunt with guidance to keep grads motivated and accountable.

Read all about SEI, its new components, and frequently asked questions about the program here. If you have any questions, feel free to get in touch with us at admissions@ga.co.

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

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marketing funnel image

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.

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.

The Study of Data Science Lags in Gender and Racial Representation

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data science gender race disparity

In the past few years, much attention has been drawn to the dearth of women and people of color in tech-related fields. A recent article in Forbes noted, “Women hold only about 26% of data jobs in the United States. There are a few reasons for the gender gap: a lack of STEM education for women early on in life, lack of mentorship for women in data science, and human resources rules and regulations not catching up to gender balance policies, to name a few.” Federal civil rights data further demonstrate that “black and Latino high school students are being shortchanged in their access to high-level math and science courses that could prepare them for college” and for careers in fields like data science.

As an education company offering tech-oriented courses at 20 campuses across the world, General Assembly is in a unique position to analyze the current crop of students looking to change the dynamics of the workplace.

Looking at GA data for our part-time programs (which typically reach students who already have jobs and are looking to expand their skill set as they pursue a promotion or a career shift), here’s what we found: While great strides have been made in fields like web development and user experience (UX) design, data science — a relatively newer concentration — still has a ways to go in terms of gender and racial equality.

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What Makes for Great Product Design?

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User experience (UX) design separates a good product from a great product.

Harnessing skills like user research, wireframes, and prototyping, UX designers have a unique perspective when it comes to understanding the interactions between users, business goals, and visual and technology elements. For companies, their work fosters brand loyalty and repeat business. For consumers, it means frustration-free online experiences, intuitive mobile apps, efficient store layouts, and more.

Watch below, as design experts from The New York Times, PayPal, Zola, and other top companies share how they design simple, user-friendly, and beautiful products.

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