{"id":2033,"date":"2016-08-15T21:41:19","date_gmt":"2016-08-16T04:41:19","guid":{"rendered":"https:\/\/www.springboard.com\/?p=2033"},"modified":"2023-07-08T17:48:24","modified_gmt":"2023-07-09T00:48:24","slug":"springboard-data-science-mentor-patrick-grennan","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/springboard-data-science-mentor-patrick-grennan\/","title":{"rendered":"Interview with Springboard Data Science Mentor: Patrick Grennan"},"content":{"rendered":"\n
\"patrick<\/a><\/figure>\n\n\n\n

Q: Thanks to taking the time to talk with me. Could you briefly introduce yourself and what you do?<\/b><\/p>\n\n\n\n

I work at One Medical Group, which is a national primary care provider. We have over 50 offices in 7 markets across the US. We are a primary care office, and we own both our tech systems and our physical doctor’s offices as well – so the doctor you see is employed by the same company that employs me as a data scientist. I work behind the scenes analyzing both operational and clinical data to make things more efficient, to allow for our providers to make better decisions, and to help our business run as well as it can. Another component is trying to build more intelligent systems that can adapt and personalize to our patients needs. We have a fairly small data team, so I\u2019ve worked on things from our clinical data, to marketing data, to operations data – a lot of odds and ends. Since we are a vertically integrated company I get to see how everything relates to each other, and I get a high level view of how what one team does affects another team in ways that can be unforeseen. <\/span><\/p>\n\n\n\n

Q: Could you give me some background on your data science education?<\/b><\/p>\n\n\n\n

At NYU, I was in a specific college called Gallatin where no one has a major – you just make it up. I loved it, but it’s not for everyone. One of the things going into college that I was very interested in was artificial intelligence and everything it touches. I was interested in it not just from a purely theoretical perspective, or from a sociological perspective – but also a technical perspective. So one of the cool things was that I got to study them all, put them all together, and take classes without any prerequisites. <\/span><\/p>\n\n\n\n

Although my education is relevant, it’s not directly relevant to what I do now. But because the program is something that you make up by yourself, you constantly have to explain yourself, you have to bring people along, and you have to be able to draw a narrative thread between classes that don’t really seem like they fit together – which is a valuable skill for data science. Having to explain the narrative – I know this is a data science cliche, is truly one of the most important skills in data science<\/a>. No matter how many algorithms you know or how many analyses you can do, if no one understands on some rudimentary or conceptual level what you’re doing or why you’re doing it, it’s just going to sit in a corner by itself rather than\u00a0moving people to action.\u00a0<\/span><\/p>\n\n\n\n

Q: How did you get a job as a data scientist?<\/b><\/p>\n\n\n\n

The way that I came into data science is through machine learning.<\/strong> I took a bunch of classes related to it, and NYU has a couple of large research groups regarding machine learning. I really got into it in college and that was the thing I wanted to do coming out of college, so that\u2019s what I did.<\/span><\/p>\n\n\n\n

I\u2019m going to put data science into two somewhat arbitrary bins: doing analysis versus building models. When I started doing, I was purely doing models, but over time I’ve gravitated more towards the middle. I think while machine learning is an awesome tool, it’s also not the right tool for every case. Over time I’ve tried to make sure that I use or leverage machine learning in places where it’s going to be useful and where it won\u2019t be overwhelmingly costly to run on an ongoing basis. I’m trying to be more judicious as to where I use machine learning and what techniques I use. Lastly, I’m learning more statistics, and trying to be more focused on analysis than I previously was.<\/span><\/p>\n\n\n\n

\"data<\/a><\/figure>\n\n\n\n

Q: What strategy for learning data science skills was most successful for you?<\/b><\/p>\n\n\n\n

I\u2019d say finding good mentors.<\/strong> I have a number of awesome mentors that I feel I can lean on for a number of different things. Not all of which are data scientists, and not all of which are programmers, per se – but they are people who I can lean on for advice on how to communicate things, and how to explain myself in a way that makes sense. Of all the data science skills, that’s where I was the most deficient when I first started, and having those mentors there to guide me along has been huge. <\/span><\/p>\n\n\n

Get To Know Other\tData Science Students<\/h4>
\"Corey<\/a>

Corey Wade<\/p>

Founder And Director at Berkeley Coding Academy<\/p><\/div>

<\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Samuel<\/a>

Samuel Okoye<\/p>

IT Consultant at Kforce<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Sam<\/a>

Sam Fisher<\/p>

Data Science Engineer at Stratyfy<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

Q: What does your workflow look like?<\/b><\/p>\n\n\n\n

It depends on the cycle that I\u2019m in. I tend to break it down into 3 different sections – the section where I talk to people, the section where I don’t talk to people, and the section where I talk to people again. The first section is mostly fact finding – understanding the needs of the problems we are trying to solve, what we want to know, what the open questions are. I try to get as many people in the room with me as possible to agree with me on the problems we want to solve with our deep dive analysis. Once I have a pretty clear picture of what everyone wants, and I’ve aligned with everyone in terms of what they expect as the result, I tend to go into a phase of deep analysis. One of the hard things about data science is that you want to leave it open-ended for exploration, but you can’t just go in a room for a month without telling [your company] what you\u2019re doing. So setting expectations at that point is super important.<\/span><\/p>\n\n\n\n

Analysis generally takes place within the timespan of a week, depending on how big the questions are. It can also be within the timespan of a day, depending on what team I’m working on and how fast they need the answers. During the middle phase I block off significant chunks of uninterrupted time to go heads down, find the right data, validate what I’m seeing, and to make sure what I’m reporting is statistically significant. <\/span><\/p>\n\n\n\n

Q: Do you have an example of a problem you have worked on like this?<\/b><\/p>\n\n\n\n

One example that is pretty common is retention. There are a lot of different factors that go into retention: how people use their apps, how intrinsically valuable people feel your services are, how often they interact with you through it, and what kind of services do you provide.<\/span><\/p>\n\n\n\n

There are lot of dimensions from which we look at retention – we look at it from a purely consumer perspective, from the impact of our ability to manage population health, from the business-to-business perspective. There are a number of different cycles where it would start with us focusing on one very specific question. I typically like to focus on one large abstract question, and that can be the most broad and general of questions. A question that can go on the top of the presentation so that everyone can be aware of what’s going on – a North Star that we can align with. If everyone can agree that knowing the answer to that question would be valuable, you don’t have to explain why you are doing the analysis.<\/span><\/p>\n\n\n\n

Generally you want a question that people have already been asking themselves. It certainly should be a proxy to a common business question that people want an answer to. And sometimes, if you’re working with a product team, no one knows to be asking that question yet. If that’s the case – it allows you to work as a team to really evangelize why you should be asking this question in the first place.<\/span><\/p>\n\n\n\n

Q: Do you have any favorite tools<\/a> and\/or coding languages?<\/b><\/p>\n\n\n\n

My favorite language is Python<\/strong> – you can write just about anything in it in a way that is easy, and there’s a framework for everything. In some respect it supports both procedure and programming, which is pretty handy because sometimes things don’t fit into one paradigm or another. We also use <\/span>Tableau<\/span><\/a> here, quite heavily. I think it’s a totally awesome tool for sharing data and disseminating data across an organization. <\/span><\/p>\n\n\n\n

One of the tools which I’ve recently become a big fan of is using the Google Suite or Dropbox Suite for interactive documents. It\u2019s my new favorite method of disseminating analysis that is not being presented in a very formal context. The reason why is that it makes collaboration really easy, and people tend to look at a powerpoint and a document differently, even if the quality of the information is the same. If we look at analysis as mementos, we can look back at years later to understand \u201cwhy did we do that or what did we discover in that thing?\u201d I’m actually a bigger fan of using a paper format rather than a presentation format. It\u2019s easier to consume, you can write more information, and collaboration is easier.<\/span><\/p>\n\n\n\n

\"pablo<\/a><\/figure>\n\n\n\n

Q: What are employers looking for when they are hiring a data scientist?<\/b><\/p>\n\n\n\n

It tends to vary from company to company. One thing I tell people that is that data science means something different to just about every company<\/strong>. Even the most qualified people, and the people who really can do it all, will not be the perfect fit for every position and will not get hired for every position. One of the things I always recommend for people looking for a data science position is: Look for a position that does the type of data science work that you are interested in doing and learning. Generally the positions tend to fall to either the applied-engineering side or a statistics and analytics side. The types of interviews you will have in each will be wildly different. I’ve had coding challenges in my interviews in the past, and others where there were presentations I\u2019ve had to give, and those are very different interviews. I\u2019d say look at the type of job and title, and see where that falls on the spectrum between analyst and engineer to give you a sense of what to prepare for. <\/span><\/p>\n\n\n\n

Data science<\/a> is causing a significant shift in our daily lives. Simultaneously, credit must be given to all of the data scientists<\/a>, machine learning engineers, and deep learning researchers that work around the clock to improve our lives.<\/p>\n\n\n\n

Patrick is a mentor at Springboard<\/a> for the Foundations of Data Science<\/a> and Data Analytics for Business<\/a> workshops.<\/strong><\/p>\n\n\n\n

Companies are no longer just collecting data. They\u2019re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists \u2013 the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.<\/p>\n\n\n\n

In this article, we\u2019ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you\u2019re more drawn out to the creative side or interested in the strategy planning part of data architecture, there\u2019s a niche for you. <\/p>\n\n\n\n

Is Data Science A Good Career?<\/h2>\n\n\n\n

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It\u2019s an interdisciplinary field that keeps the work varied and interesting.<\/p>\n\n\n\n

10 Data Science Careers To Consider<\/h2>\n\n\n\n

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.<\/p>\n\n\n\n

<\/div><\/div><\/div><\/div>\n\n\n\n

<\/p>\n\n\n\n

Data Scientist<\/h3>\n\n\n\n

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research \u2013 all depending on the field they operate in.<\/p>\n\n\n\n

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. <\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

A data scientist\u2019s career starts with a solid mathematical foundation, whether it\u2019s interpreting the results of an A\/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. <\/p>\n\n\n\n

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses<\/a> and certifications that demonstrate their expertise and willingness to learn.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

The average salary of a data scientist in the US is $156,363<\/a> per year.<\/p>\n\n\n\n

Data Analyst<\/h3>\n\n\n\n

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.<\/p>\n\n\n\n

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps<\/a> to acquire these skills. <\/p>\n\n\n\n

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

The average base salary of a data analyst is $76,892<\/a> per year.<\/p>\n\n\n\n

Business Analyst<\/h3>\n\n\n\n

Business analysts often have an essential role in an organization, driving change and improvement. That\u2019s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. <\/p>\n\n\n\n

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master\u2019s degree, especially in more complex industries or corporate environments.<\/p>\n\n\n\n

Employers also value a business analyst\u2019s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

A business analyst can earn an average of $84,435<\/a> per year.<\/p>\n\n\n\n

Database Administrator<\/h3>\n\n\n\n

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. <\/p>\n\n\n\n

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it\u2019s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. <\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Database administrators earn an average salary of $77,391<\/a> annually.<\/p>\n\n\n\n

Data Engineer<\/h3>\n\n\n\n

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. <\/p>\n\n\n\n

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Data engineers earn a whooping average salary of $125,180<\/a> per year.<\/p>\n\n\n\n

Database Architect<\/h3>\n\n\n\n

A database architect\u2019s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. <\/p>\n\n\n\n

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

If you\u2019re aiming to excel as a database architect but don\u2019t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don\u2019t forget programming languages – SQL, Python, or Java. <\/p>\n\n\n\n

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard<\/a>.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Data architecture is a very lucrative career. A database architect can earn an average of $165,383<\/a> per year.<\/p>\n\n\n\n

Machine Learning Engineer<\/h3>\n\n\n\n

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. <\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K<\/a> per year, one of the highest-paying AI careers<\/a>.<\/p>\n\n\n\n

Quantitative Analyst<\/h3>\n\n\n\n

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. <\/p>\n\n\n\n

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. <\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

A quantitative analyst earns an average of $173,307<\/a> per year.<\/p>\n\n\n\n

Data Mining Specialist<\/h3>\n\n\n\n

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.<\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. <\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

Data mining specialists earn $109,023<\/a> per year.<\/p>\n\n\n\n

Data Visualisation Engineer<\/h3>\n\n\n\n

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data\u2019s context. <\/p>\n\n\n\n

General Requirements<\/h4>\n\n\n\n

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.<\/p>\n\n\n\n

Average Salary<\/h4>\n\n\n\n

The average annual pay of a data visualization engineer is $103,031<\/a>.<\/p>\n\n\n\n

Resources To Find Data Science Jobs<\/h2>\n\n\n\n

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.<\/p>\n\n\n\n

Job Boards<\/h3>\n\n\n\n

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. <\/p>\n\n\n\n

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.<\/p>\n\n\n\n

Online Communities<\/h3>\n\n\n\n

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.<\/p>\n\n\n\n

Network And LinkedIn<\/h3>\n\n\n\n

Don\u2019t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the \u201cEasy Apply\u201d options, as you\u2019ll be competing with thousands of applicants who bring nothing unique to the table.<\/p>\n\n\n\n

FAQs about Data Science Careers<\/h2>\n\n\n\n

We answer your most frequently asked questions.<\/p>\n\n\n\n

Do I Need A Degree For Data Science?<\/h3>\n\n\n\n

A degree is not a set-in-stone requirement to become a data scientist<\/a>. It\u2019s true many data scientists hold a BA\u2019s or MA\u2019s degree, but these just provide foundational knowledge. It\u2019s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.<\/p>\n\n\n\n

Does Data Science Need Coding?<\/h3>\n\n\n\n

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.<\/p>\n\n\n\n

Is Data Science A Lot Of Math?<\/h3>\n\n\n\n

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.<\/p>\n\n\n\n

What Skills Do You Need To Land an Entry-Level Data Science Position?<\/h3>\n\n\n\n

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you\u2019re acing problem-solving, critical thinking, and effective communication.<\/p>\n\n\n\n

Since you\u2019re here\u2026<\/strong>Are you interested in this career track? Investigate with our free guide to what a data professional actually<\/em> does<\/a>. When you\u2019re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp<\/a> which will help you land a job or your tuition back!<\/p>\n","protected":false},"excerpt":{"rendered":"

Q: Thanks to taking the time to talk with me. Could you briefly introduce yourself and what you do? I work at One Medical Group, which is a national primary care provider. We have over 50 offices in 7 markets across the US. We are a primary care office, and we own both our tech […]<\/p>\n","protected":false},"author":15,"featured_media":2067,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[67],"tags":[],"marketing_tags":[],"class_list":{"0":"post-2033","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/2033"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=2033"}],"version-history":[{"count":3,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/2033\/revisions"}],"predecessor-version":[{"id":47749,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/2033\/revisions\/47749"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/2067"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=2033"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=2033"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=2033"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=2033"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}