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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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Essential Data Skills to Know

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In 2012, IBM revealed that 2.5 quintillion bytes of data were being created per day — an enormous sum that humankind had never known before. Since then, the volume of the world’s data has not only continued to increase, but it’s arriving at a faster and faster pace.

However, data by itself doesn’t have much value. After all, a pile of numbers and data files is just that: a pile of numbers and data files. The real value of data comes from making sense of the abundance of information. That’s why businesses and organizations across countless industries are investing in forward-thinking data talent — to leverage its predictive power, craft smart business strategies, and drive informed decision-making.

The sharp and strategic people who do this job are data scientistsdata analystsmachine learning engineers, and business intelligence analysts — among other titles — and these professionals are in high demand. In 2018, the jobs platform Glassdoor ranked data scientist as the Best Job in America for the third year in a row, with a median salary of $110,000 and more than 4,500 available positions. Additionally, five other data- and analytics-related roles made the list of the top 50 jobs, ranked by number of openings in the field, salary, and overall job satisfaction.

Companies are quickly recognizing the vital need for data knowledge, impacting a vast array of industries including eCommerce, health care, finance, and sales — to name a few. In order to stay competitive and grow their businesses, leaders are investing in their future by strategically training and hiring talent to ensure proficiency in key skills.

Three of the most prevalent technologies transforming how we understand and use data are SQL, Python, and machine learning — and all are great entry points into the field. 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.

What You Can Do With Essential Data Skills

You can get started with SQLPython, and machine learning, three of the most useful data tools, without any formal background. However, each topic has a different set of fundamentals that you’ll need to understand as you progress in your learning. For example, Python will expose you to the world of object-oriented programming, while SQL will expose you to database design concepts. Machine learning will require a good understanding of data analysis.

Dipping your toes in this uncharted water may seem daunting — but it shouldn’t! There’s so much opportunity in the data field for growth, whether or not you’re seeking a full-time role. No matter your position or industry, this knowledge can take your hireability to the next level. Here are just some of the things you can do with data expertise:

  • Become a skilled problem-solver. Programming languages like SQL and Python teach you problem-solving skills that are applicable in many business scenarios you’ll encounter.
  • Be more cross-functional. Having key programming and data skills under your belt makes it easier to work with teams across your organization. Being able to speak the same language as software engineers, business intelligence analysts, and data professionals helps streamline requests, bring clarity to the workflow, and provide insight into technical action items.
  • Build the technology of the future. Data skills enable you to help build new, groundbreaking technologies, including web applications, machine learning models, chatbots, and much more.
  • Expand your career potential. Based on previous projections from the management consultancy firm McKinsey & Company, IBM predicts that by 2020, the number of data science and analytics job listings will grow by nearly 364,000 to about 2.72 million.
  • Improve communication. Data professionals must communicate to non-technical audiences — including stakeholders across the company — in a compelling way to highlight business impact and opportunity. At the end of the day, those stakeholders have to act on and possibly make far-reaching decisions based on data findings.

Want to learn more? In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down these three essential technologies. The 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, and now’s a great time to dive in.

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

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

Python Programming for Beginners

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Python is the No. 1 most popular programming language used by data analystsdata scientists, and software engineers to automate processes, build the functionality of applications, and delve into machine learning. Companies like Google, SpaceX, and Instagram use it to clean data, build predictive and artificial intelligence (AI) models and web apps, and more. It stands out for being simple to read and write, while offering extreme flexibility and having an active community of users and contributors. This makes it a great language for new programmers to learn for a broad range of applications in data science, web development, and beyond.

Python in Everyday Life: Real-World Examples

Here are some fascinating ways in which Python is shaping the world we live in:

  • Artificial intelligence: Python is especially prevalent in the AI community, again for its ease of use and flexibility. For example, in just a few hours, a business could build a basic chatbot that answers some of the most common questions from its customers. To do this, programmers could use Python to scrape the contents of all of the email exchanges with the company’s customers, identify common themes in these exchanges with visualizations, and then build a predictive model that can be used by the chatbot application to give appropriate responses.
  • File-sharing applications: When the file-storage platform Dropbox was created in 2007, it used Python to build the desktop applications and server infrastructure responsible for actually sharing the files. After more than a decade, Python is still powering the company’s desktop applications. In other words, Dropbox was able to write a single application for both Macs and PCs that still works today!
  • Web applications: Python is used to run various parts of some of today’s most trafficked websites, including Pinterest, Instagram, Spotify, and YouTube. In fact, the visual bookmarking platform Pinterest has used Python in some form since it was founded (e.g., to power its web app, build and maintain data pipelines, and perform analyses).
  • Hollywood special effects: Remember that summer blockbuster with the huge explosions? A lot of companies, including Lucasfilm’s Industrial Light & Magic (ILM), use Python to help program those awesome special effects. By using Python, companies like ILM have been able to develop standard toolkits that they can reuse across productions, while still retaining the flexibility to build custom effects in less time than ever before.

Simplicity in Code

Here’s a cool example of just how simple Python is. Below is code that tells the computer to print the words “Hello World”:

In Python:

Python hello world

Yup, that’s really all it takes! For context, let’s compare that to another popular programming language, Java, which has a steeper learning curve (though is still a highly desirable skill set in the job market).

Java Hello World

Clearly, Python requires much less code. This powerful language’s ease of use makes it relevant far beyond data — coders have adopted it to perform all sorts of functions that you encounter every day.

Python at Work

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

  • Data analyst: A data analyst could use Python to save time by automating tedious tasks or performing advanced calculations.
  • Data engineer: A data engineer could use Python to build a data pipeline that takes data from one system, aggregates it or changes its shape, and moves it into another system.
  • Software engineer/web developer: A software engineer or web developer could quickly use Python to build the next great web app.

Why You and Your Business Need to Understand Data Science

As the world becomes increasingly data-driven, learning to leverage key technologies like Python, SQL, and machine learning will create endless possibilities for your career and your organization. Now is a great time to dive in.

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.

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.

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

Download the paper to learn more.

Boost your business and career acumen with data.

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

Download the eBook

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

The Path to a Diverse, Vibrant Tech Community

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CodeBridge General Assembly Per Scholas Graduation

Anthony Pegues, second from right, with Per Scholas CEO Plinio Ayala (far right), and fellow graduates from CodeBridge, a web development training partnership between General Assembly and Per Scholas.

Anthony Pegues was a part-time janitor in the suburbs of New York City who sought a way into a rewarding career. He saw tech — and web development specifically — as a viable path, but didn’t have the resources to get the skills he needed to be ready for a job in the field.

Unfortunately, Pegues’ situation is all too common. There are plenty of tech jobs available, and people who are eager to fill them. But many passionate, prospective developers from underserved and overlooked communities do not have the resources, time, or opportunities to pursue their passions and get the skills they need to transform their careers.

At General Assembly, our central mission is to create pathways so that everyone with the dedication and commitment to reshape their career can do so, regardless of their prior experience or ability to pay for the training they need to get there. To this end, we’ve spent the last few years launching and refining strategies and programs that break down barriers and contribute to the diversity of the tech sector.

But there’s still much more work to do.

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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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We Stand With the LGBTQ+ Community

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LGBTQ Work Protection Statement

Individuals thrive professionally and personally when they can live openly and without fear. The strength and security of our communities — and economy — depends on it.

At General Assembly, we’re in the business of empowering people to pursue work they love and careers that allow them to realize their passions. We’re also big believers that when people bring their whole selves to work — and all the identities, experiences, and ideas that make them unique — they’re more productive, engaged, and innovative.

Apparently, the Department of Justice doesn’t agree. On the heels of the president’s surprise ban on transgender service members in the military, on July 26 the Department of Justice issued a brief that states that Title VII — the law that protects workers from sex discrimination — does not extend to the LGBTQ+ community.

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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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