A Product Management Career Map Developed by GA’s Product Management Standards Board

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Businesses have shifted from traditional ways of operating to truly becoming
customer-centric digital organizations — and the global pandemic has accelerated this inevitable shift. Product managers, who sit at the nexus of customer needs,
business strategy, and technology, play a critical part in building their companies’ digital fluency so organizations can evolve and transform their products to meet
market and customer demands. 

That said, product management is often ill-defined as a function, especially in
traditional companies, and business leaders and managers have a responsibility to precisely understand product management skills and careers to help these nascent leaders succeed and unlock their full potential. 

By developing and integrating product managers as strategic thinkers who help evolve organizations into being customer-centric, leaders and managers can tap into many benefits:

  1. Improved leadership pipeline and succession planning: Product managers are responsible for many things, but skills development strategies to level-up their subject matter expertise into leadership roles are not often clear. By
    connecting product management skills to a long-term and articulated career path, you can improve your leadership pipeline and increase career satisfaction for your product managers.
  2. Clear hiring objectives: Evaluating candidates against a documented set of skills can decrease bias and help recruiters make vital distinctions between
    hiring project managers, product managers, and product owners. 
  3. Increased product management talent pipeline: Creating consistency around what early-career professionals understand a product manager to be and what they must learn creates access to product management careers for people who don’t already have product managers in their networks.

We formed the Product Management Standards Board with a wide-ranging set of product management leaders across the consumer goods, technology, finance, and education sectors. We’ll channel our collective experience into increasing clarity of and access to product management skills and careers so that the next generation of product management talent can maximize their impact in organizations and the world.

We’ve crafted a career framework as a valuable tool for:

  • Product leaders who want to build capable, well-balanced teams.
  • Aspiring product managers who want to understand what skills they need to enter the field and help lead organizations.
  • Mid-career professionals who wish to understand their career options.
  • HR leaders who want to build transparent, consistent career pathways.

What Defines an Excellent Product Manager?

We drafted a career map that captures our collective thinking about what makes a product manager and the career paths and associated skills required for an employee to one day become a product leader.

Let’s break down each section of the framework and see how they’re used to guide
career progression. 

Associate Product Manager 

To begin a career in product management, individuals often move into associate product management roles from within or outside an organization with some existing understanding of the business, product, and/or customer base. While we firmly
believe anyone can become a product manager starting at the associate level, we commonly see analysts, software engineers, designers, project managers, or product marketers moving into this role. In this stage of career development, product
managers learn to use data to make decisions, influence without authority, and
understand the balancing act of prioritization.

Product Manager

Product managers learn a mix of skills based on their particular product, area of
responsibility, and expertise. Product managers in charge of a new product or feature may heavily focus on research and development. In contrast, product managers
responsible for improving the quality and efficacy of an existing product or feature may focus more on data analysis to understand what drives an improved experience. Squad leadership is critical to ensuring all people understand the goal they are
working towards and what success will look like. Product managers at a large
organization have the opportunity to either specialize in a single domain or can work with their managers to rotate ownership over product areas to develop a breadth of
experience and skills. Product managers at a startup will likely get to experience all of these skills in rapid rotation as their teams iterate quickly to identify
product-market-fit and the right set of features for their product.

Senior Product Manager

The senior product manager level is where product managers start differentiating
between becoming “craftmasters” in the individual contributor path or people
managers on the leadership path. While craftmasters still need to provide inspiring team leadership to those working on the product, they often become particularly versed in a product domain, like product growth and analytics. In contrast, a people manager in this role largely focuses on team management skills. Either way, this role is a critical step in someone’s career as it allows them an opportunity to practice
developing and sharing a vision for a product with their team and working with more moving parts to guide people towards that vision. Understanding and prioritizing these moving parts become a key skill to develop at this level. Additionally, the
responsibilities to make decisions related to product growth also increase here. This level is a product manager’s opportunity to demonstrate an understanding of how business, market, and product intersect to inform the direction of the product and
distinctly articulate how they expect that product to impact the company’s financials.

Director of Product

At this career point, directors of product are making a critical transition from
manager to leader. They have to bring the threads of the product strategy and the product roadmap together and take ownership and responsibility for their decisions and impact. The Director of Product also starts to gain ownership of the cost side of their decisions – at some companies, this can extend as far as P&L ownership for project and product costs. They move into managing a portfolio of products and
connecting the dots between how they work collectively for users and guide teams to work through complex problems to develop goals on a longer, future-driven timeline. 

VP or Head of Product 

Once an individual reaches this leadership level, they have mastered the key
functional skills of product. They are now the pivotal connection point between the rest of the company’s leadership plans and the product team. They have to get
beyond “product speak” and help connect the dots between technology, customers, and
business goals with other leaders and employees across the business. There is a fair amount of time spent aligning resources and plans with other leaders to drive the strategy forward. As product leaders, they are also driving innovative thinking and are responsible for either the entirety of the product or a significant portfolio in terms of the company’s financials. 

A Few Notes

We’ve had many rich discussions while building out the career map and teased out some nuances listed below that may come to mind as you work your way through this framework.

What about a product owner?

While product owners play a critical function, we do not see this as being a distinct job title for someone. If you’re curious about the distinction and who might play a product ownership function in your teams, read Product Dave on Medium. 

What about the difference between startups and large organizations and everything in-between?

Product leaders at a large organization should consider rotating their product
managers between a few different areas before moving them into more senior roles to build a range of skills sustainably. Product managers at a startup will likely get to
experience all of these skills in rapid succession as their teams iterate quickly to
identify product-market-fit and the right set of features for their product.

Does the framework change for “craftmaster” vs. “leadership” paths?

We have focused this framework more on the leadership path, but there is a
continued path as an individual contributor, especially within larger organizations. Senior product managers, principals, and distinguished product management roles often see product managers tackle increasingly complex problems and mentor their colleagues on critical product skills while remaining in the “craftmaster” path.

Where do tangential functions fit in?

Some roles work closely with product managers to enable the full execution of
products, but they are excluded on this map as they are adjacent to a product
manager career path. A few of these functions include pricing analysts, product
marketers, and product operations. 

What happens after VP of Product?

The next step after VP of Product is very dependent on the organization. Some VPs of Product already report to the CEO or a business unit owner, in which case, those roles would be the next step. In other organizations, a Chief Product Officer role
exists and becomes the next step. Data from Emsi shows that there has been a 140% increase in CPO postings from Nov 2019 to Nov 2020; a clear reflection of
organizations’ increasing awareness of the value of the role of product leadership in aligning customer needs, technology, and business strategy, and the increasing
number of opportunities for advancement to the executive suite in this field.

Next Steps: Putting Words Into Action

We formed the Product Management Standards Board to increase clarity of and
access to product management skills and careers so the next generation of product management talent can maximize their global impact in organizations. Our career framework is a first step toward achieving this goal, but it’s only effective if followed by action.

To put this theory into action, we have started using this framework within our
organizations to:

  • Explain career progression and roles across our teams to guide development conversations and linking individual activities to strategic objectives on our product teams.
  • Guide high-potential employees on how to maximize their leadership skills.
  • Evaluate job candidates based on their skills match with the function for which they are applying, rather than exclusively what schools they’ve gone to or previous roles they’ve held. 

If you could benefit from these same actions, we encourage you to join us in using the framework for similar purposes in your organizations. Our industry needs to use a common language around product management, and that language extends beyond our board.

This is a living document, and we’ll be seeking feedback from partners in our
executive teams, industry associations, and peers around the world. We’re also asking you. If you have feedback on how this could be useful for you, please let us know at cheers@ga.co.

By coalescing on what it takes to succeed in product management careers, we can
begin to solve some of the pertinent talent challenges facing the profession and better prepare the next generation of leaders. We look forward to working to standardize product management career paths together.

Five Ways to Build Organizational Data Literacy

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Data is everywhere and in every part of your business; however, data is often left for technical teams to figure out. In recent years, data has been prioritized in digital transformation efforts, with an increasing amount of businesses striving to be data-first. Hoping to leverage new tools, technologies and hiring data analysts and scientists are often overlooking one essential fact: data is for everyone, and every employee can benefit from acquiring data skills.

Businesses who leave skills out of the equation in their data transformation efforts are further widening their skill gaps. In fact, according to Accenture, 74% of employees report feeling overwhelmed when working with data. According to Deloitte, contributors aren’t the only ones; 67% of leaders say they are not comfortable accessing or using data. It’s time to change all of this.

Perhaps this anxiety and discomfort stem from businesses misunderstanding the role every employee has in leveraging data: 

  • Leaders set the vision and use data to ensure that they are making the right business decisions. 
  • Data practitioners solve complex problems with a blend of technical ability in analytics and data science. 
  • The broader organization uses data to understand impact, communicate results, and make decisions. 

All roles can benefit from upskilling to shift mindsets, gain fluency, and build efficiencies across the business, with building literacy across the broader organization being the most urgent priority.

What does data literacy look like?

Data literacy is the ability to create, read, and analyze data, and then communicate that information and use it effectively. To do this, people must understand how data is collected, where it comes from, what it shows, how it can be used, and why it’s important. 

Being data-literate means understanding:

  • Data Culture
    • Literacy Goal:  Understanding the data lifecycle, data roles and responsibilities, and how data flows through an organization. 
  • Data Ethics & Privacy
    • Literacy Goal:   Explain why ethics and privacy are essential and understand the role each employee has to play. 
  • Data Visualizations
    • Literacy Goal:  Learn why common types of visualizations are chosen to promote certain comparisons and interpret the information. 
  • Statistics
    • Literacy Goal:  Describe data and spot trends in visualizations. 
  • Artificial Intelligence (AI)
    • Literacy Goal:  Identify opportunities to integrate AI and data science tools within your workflow.

Giving data skills to all employees will help businesses meet their loftiest data transformation goals. Training all employees comes with many benefits, such as higher decision quality and improved cross-functional communication. According to Deloitte, in companies where all employees train on analytics, 88% exceeded their business goals.

Five Ways to Build a Data-Literate Organization

1. Understand How Data is Being Used in Your Business

Shifting mindsets at the top of the org chart is essential to becoming a data-literate org. Being a role model for your employees helps build trust with your new skills — they will help you form a data-driven agenda. With the right skills, you’ll be able to prioritize projects with the most business impact.  Data literacy also helps you effectively communicate with data practitioners within your organization and help focus your contributors on the data points that truly matter.

2. Define Preferred Data Usage in Your Business 

Data is plentiful, so narrowing that data down to only the most essential points is imperative to success. Understand what data you wish to collect and track, how that data will be used, and what tools and skills are needed to leverage that data successfully. 

3. Get Leadership Buy-in Across the Business

Getting buy-in from leaders across  the business is essential to establishing a data-first culture. Any strategic initiative starts at the top, and leaders that understand the power of a strong data culture will be willing to make the tools, training, and people investments necessary to build one. 

4. Create a Training Plan

Once you know what data you wish to use, consider which skills would be the most beneficial. Remember, everyone can benefit from training. We recommend building literacy skills where there are definite gaps among leaders and across the broader organization.

5. Put New Skills Into Practice

Your plan is in place! Now, give your teams learning opportunities and explain why these skills will matter to the business’s success.After training, provide team members opportunities to practice their new skills by giving them goals directly related to using, communicating with, and becoming more data-proficient.

Continue to offer learning opportunities for those employees who wish to advance past literacy and into hard skills. Consider upskilling your data practitioners to become more efficient.

In an era of increased digitization, many businesses still don’t know how to use data to gain  critical insights and information on goals and objectives. From the intern to the C-suite, it’s more important than ever for all business members to create, read, analyze, and communicate data pertaining to these objectives. Data literacy at all levels can and should be encouraged to future proof the organization and support overall business goals. Investing in upskilling to ensure that everyone is comfortable bringing data to the table has ROIs well beyond cost. 

Thinking about building your teams’ data literacy? Learn more about how our data curriculum can help your business make this powerful pivot.

What Is Digital Transformation?

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We are in the midst of a grand economic experiment t catalyzed by COVID-19 to accelerate digital transformation efforts in almost every business. The days of arguing whether digital transformation is the right path are over. Simply put, companies that don’t modernize will fail. That said, not every company is on the same journey. By the end of 2019, nearly 20% of enterprise organizations had not started digital transformation efforts. Another 40% said they were currently undergoing it, and budgets are rising to match. IDC forecasts global spending for digital transformation rose by about 17.9% in 2019 to $1.18 trillion. Partially due to  COVID-19, that number is expected to increase by another 10.4% in 2020.

If you asked businesses before the pandemic about the importance of digital transformation, most would agree that it was important, but not all would prioritize it in the same ways. However, digitization becomes crucial very quickly when tens of millions of people must work from home, and non-essential businesses are closed to foot traffic.

Look at the shift in global consumer sentiment in the first week of May:

Source: McKinsey

While some of these shifts were temporary, many are not. We see fundamental changes in the way the economy works. A recent IDG survey found that 59% of IT decision-makers have accelerated their efforts with spending likely to grow by more than 10% in 2020. Demand for skills in technology, data analysis, product, marketing, and UX are higher than ever as companies shift to a new model that emphasizes  remote operations.

Time is no longer a luxury for organizations that had not yet started or been in the early stages of planning digital transformation efforts. The new normal requires businesses to be agile and digital.

What is a Digital Transformation?

Digital transformation is the process of remodeling existing business processes to meet the current market — specifically, the needs of the customer. Until recently, that included banks implementing mobile apps and investing heavily in FinTech, or healthcare organizations digitizing records and connecting devices and people seamlessly across a large network, etc.  Digital transformation was previously about supplementing existing offerings with new technologies that met consumers where they were most likely to engage.

Post-COVID-19, digital transformation is still about these things. One of the many challenges large organizations have with digital transformation is that they attempt to implement small efforts within silos in a much larger company infrastructure — digital transformation is bigger than that. It’s about recognizing the core ways tointeract with customers and making smart investments to address specific challenges.

Why is digital transformation different from simple digitization? The latter is about shifting away from paper-based and analog processes. It’s about making data accessible to everyone in an organization and connecting employees at all levels. Digital transformation is about leveraging those changes to improve the relationship between your company and your customers with things like personalized messaging, configurable products and services, and more accessible, catered customer service offerings.

Of course, these efforts can be difficult to execute. To date, less than 30% of them have succeeded, and only 16% have improved performance and resulted in long-term changes. While smaller businesses (those with fewer than 100 employees) are significantly more likely to succeed, enterprise organizations are challenged to realize demonstrable returns. However, it’s not the concept that’s flawed; it’s the process. Too many organizations start from the top, thinking of the technologies and tools and not the people who will implement them.

Digital transformation relies on people at multiple levels. Not only are highly skilled individuals in marketing, IT, and product required to implement new initiatives, the entire workforce must buy into these changes. Without high levels of adoption, large investments in new software and processes can quickly look like mistakes.

Why Are Digital Transformations Important?

More than 80% of decision-makers in technology and engineering see a mismatch between the skills workers have, and the skills companies need. The biggest gaps are in strategic thinking and analysis: data analytics, data science, innovation strategy, and web development, among others. That talent gap with organizations is growing as more companies are eager to bring on top-tier talent to steer their efforts into the next decade. Digitization addresses this by leveraging artificial intelligence and machine learning to support internal workers and enable the development of the right skills for the necessary work.

Furthermore, companies should be looking at the staff they already have to see how they can help support digital transformation goals. The Build vs. Buy Approach to Talent allows companies to build internal competencies that support digital transformation. We know that 75% of digital transformations fail because companies focus on systems instead of including talent as a critical enabler. Of the large chunk that fails, 70% are due to a lack of user adoption and behavioral change. Digital transformation isn’t only about buying the flashiest new tools. It’s about crafting a strategy that your employees are willing and able to implement. You need buy-in from every level of an organization. When employees embrace the concept of digital transformation, technology becomes secondary. As employees work in ways they never have before, this is more important than ever.

This might all sound like a lot of work. Coming into 20212, many companies had long put off this process because of that perception. But, the growth potential is staggering. MGI estimated that an additional $13 trillion could be added to global GDP in just 10 years by implementing AI, automation, and digitization. Despite that, only 25% of the economic potential of digitization has yet been captured. And that’s the average. In some industries, the digital frontier gap is significantly larger — especially in revenue generation, automation, and digitization of the workforce.

Despite the delays before this year, many chief executives now see the value of digital transformation. Two-thirds of CEOs expect to change their business models due to digital technologies, and 77% of digitally mature companies are more likely to grow digital roles in the next three years. These trends have only continued in light of COVID-19.  A July survey showed that the number of days spent at home by employees had grown four-fold. Ultimately, all remote employees require technological support. Think about all the technology that we rely upon that needs adequate support, too: Cloud-based applications. WAN modernization efforts to support a dispersed workforce and maintain network security. Improvements to active directory and identity management.

The impact of digital transformation efforts leads to fundamental changes in departmental models as well. Marketing, for example, is leveraging AI to improve the customer experience across the board. With 80% of companies now using AI chatbots and 84% of customer-focused companies spending heavily to improve mobile experiences, the way organizations engage with prospects and customers has fundamentally changed in the last half-decade.

The Impact of Digital Transformation (Done Right)

Over the past six months, workforce digitization has accelerated faster than at any point in the last twenty years. For organizations ahead of the game, it was a chance to put their innovative efforts to the test. For those who had delayed digital transformation initiatives, it was a major challenge. With limited resources, a highly competitive talent pool, and an uncertain future reshaped by the events of 2020, it’s more important than ever to develop a strategy that guides your business forward. This is a massive opportunity for leaders who understand the moment we are in, to arm their organizations with the tools, resources, and processes needed to succeed.

Where are you observing digital skill gaps within your organization? Learn more about how we can help.

Four New Skilling Solutions for Powerful Data Transformation

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Data is integral to every business. It helps organizations set strategies, report on wins and losses, make smarter business decisions, and is the connective tissue between leaders and teams. However, as businesses lean into a data-first future, through digital transformation, they must also take into account the skills needed to successfully leverage data.

According to New Vantage Partners, only 24% of organizations have successfully become data-driven. Organizations undergoing data transformations don’t typically fail because of tools or technology but because of talent-related challenges, such as cultural resistance and lack of leadership, contributing to a general discomfort communicating with and using data. A study by Accenture confirms that 74% of employees feel overwhelmed or unhappy when working with data.

It’s time to change all of that. Investment in data upskilling for existing talent is a step in the right direction for businesses hoping to benefit from the full use of data and AI. From mindset training for leaders to upskilling functional practitioners on modern practices to fluency for the broader organization, businesses must begin to see the opportunity and importance of data transformation in the context of employee skills.

Introducing Four New Training Programs to Embed Data Skills Into Your Organization

We’ve had the pleasure of helping businesses, such as Guardian and Booz Allen Hamilton, build data-driven workforces from within through upskilling and reskilling. Our work with the AI & Data Science Standards Board and our customer and industry research helps us to understand what training each employee — from leader to contributor— needs to successfully leverage data within their roles.

As the digital landscape continues to evolve, we saw an opportunity to further enable teams to transform into data-driven organizations. Over the last few months, we’ve been hard at work refreshing existing training programs for leaders and functional practitioners and building new ones for the broader organization, all connecting to the most emergent data-skilling needs.

Here’s a quick overview of those programs:

  • Data Literacy On Demand [New]: Data literacy for all employees has become a must-have for businesses striving to build a data-first culture. This flexible training solution fits right into your employee’s workflow and gives them the foundational knowledge they need to start interpreting and communicating with data. 
  • Building Data Literacy [New]: For deeper, more targeted data literacy training, we created a brand new workshop, Building Data Literacy. Use Building Data Literacy to train smaller cohorts of employees or as a deeper, more hands-on follow-up to Data Literacy On Demand. 
  • AI for Leaders [Refreshed]: We refreshed our AI for Leaders workshop to better focus on giving organizations a place to start when considering AI. This approach for getting started with AI was validated by our AI & Data Science Standards Board members. 
  • Advanced Analytics Accelerator [Refreshed]: Advanced Analytics Accelerator is one of our most popular data programs. We used client feedback to develop a new assessment approach and refresh the curriculum to better meet learner needs. New assessments help show learner uplift and mastery of concepts covered in the program.

These new programs will allow you to: 

  • Take the First Step With Data & AI: Move transformation initiatives forward by giving every audience in your business foundational data and AI skills. 
  • Stay on the Cutting-Edge With Content Validated by Experts: Give your people real world, actionable insights with training programs that are created with and delivered by subject matter experts. 
  • Reach Employees With Relevant Training: Maximize learner retention with curricula designed and delivered in the right format for your learning objectives.

More to Come

Over the next few months, we will be releasing more useful workforce insights, updated training programs, and more. Keep your eyes on the GA blog or get in touch with us to hear the latest.

Want to learn more about how we can help your organization lean into a data-first future? Download the full catalog of GA’s data skilling solutions here.

How to Finance a Career Change

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With the growing costs of traditional education programs, launching a new career can feel a lot like a chicken or egg dilemma. Unfortunately, when it comes to education, not everyone has the financial flexibility to make a large down payment, commit to a repayment plan, or invest in their future.

This was the case for Sharif York. With a background in 3D animation, he knew that he wanted to pursue a career through General Assembly’s User Experience Design Immersive (UXDI) bootcamp, but his financial situation prevented him from taking that critical next step. Sharif’s life changed when he discovered our income share agreement (ISA) program, Catalyst. Catalyst gives full-time students the space to focus more on class — not payments — by allowing them to pay their tuition after they secure a job. With the financial stress out of the way, Sharif was able to land a product designer role at AT&T in 2019 — less than a month after graduating and all without a loan or upfront payments. 

What were you doing before you came to GA? What prompted you to make a career change?

Before GA, I was pursuing a career in 3D animation, but that industry requires you to move to specific regions. I was also freelancing as an animator and web designer. After learning more about user experience (UX), user interface (UI), and product design, I realized the money was great, you can work across so many different industries because they all need an improved product (regardless if it’s digital or tangible), and it has plenty of growth opportunities. One Christmas, I asked for four giant UX and interaction design text books. — let’s just say my entire summer was spent studying those books. 

Why did you choose GA over other programs?

I discovered GA early on while researching user experience. During the first year studying UX, I never took the bootcamp, but two or so years later I decided to take the leap. I saw other programs, but GA stood out to me due to its Atlanta location at Ponce City Market, regular free events, and other opportunities that helped get out of my comfort zone. Inspiring colleagues of mine had also taken the course and landed jobs soon after. 

How did Catalyst help in your decision to enroll at GA? Would you have moved forward with it if GA did not offer an ISA program? 

The ISA did help. It allowed me to take the full-time program, and work on the weekends. I would have likely moved forward regardless, but it would have been much harder to balance work and GA.  

What made you choose Catalyst over the other financing options we offer? 

The Catalyst program allowed me to take the course without having to pay anything out of pocket during the class. This definitely helped in my situation because I couldn’t work as many hours and be a full-time student. Catalyst offered a way to take the course and make the payments in the future, which was ideal.

Describe your experience with the Outcomes program at GA. What was the job search like? How long did it take for you to get a job? 

The Outcomes Team is the best. But it will only work if you want it to work. If you take your homework seriously, push yourself to apply to jobs, and work on personal branding, it will pay off. I can’t tell people enough, if you don’t take Outcomes seriously enough, your experience will be much tougher. It took me less than a month to get a job. I went full speed ahead on LinkedIn after the course, met up with industry professionals for coffee or a Zoom, and reached out to people who work at specific companies to discuss their roles. This all helped me land a job less than a month after the course.

What are your biggest takeaways from the program? How did the skills you learned at GA help you with your current role?

The collaboration skills I learned from Outcomes, in-class work, and group work are the biggest things that helped me in my current position. Regardless if you’re a UX designer or a developer, you need to be engaged with the projects and your team. On the job, there’s nobody who will hold your hand while you’re working on complex products and problems: sometimes it can take months to figure out a role. If you can’t be a part of a team and collaborate with others, it will be very hard moving forward. I say that as a person who used to take a long time to open up.   

Since graduating, how has GA impacted your career?

I got a job at AT&T, and I met incredible people who are now a part of my network. I also made some close friends who helped me get out of my shell and realize the importance of new connections. 

Do you have any advice for students who are hesitant to take that leap and switch careers? 

Your educational or professional background isn’t the key to landing a new job. For instance, I saw a lawyer come to GA and get a UX job right away. Rather, experience is everything, and companies are finally realizing that. Gain that experience at GA while you’re working on your projects. Embrace the help of your classmates and instructor. If you put in the work both in class and during the job hunt, success will come. And if you have a goal of switching into a better career, just do it. If you hesitate and think about it too long, the opportunity will come and go. Never give up!!!

Explore Your Options

8 Best UX Design Portfolio Examples

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The UX portfolio website has superseded the business card as a UX designer’s most essential professional networking tool. Especially these days, as the UX design industry pivots abruptly to a predominantly remote professio­­­n, UX designers communicate their professional identities virtually through their online presence to navigate the constricted job market successfully. 

In my middling work experience as a UX designer over the years, I’ve been involved in countless UX designer portfolio reviews on both sides of the hiring process. Having personally benefited from industry mentorship in my own career, I’m excited to share what inside intelligence I can back with the design community, to encourage emerging UX designers to represent themselves more effectively to hiring managers and potential clients.

As with any other good user experience, a UX portfolio website should consider the user’s mindset during a visit. Bear in mind, companies typically automate their hiring processes using HR software, with workflows designed to evaluate as many qualified applicants as quickly as possible. Conciseness is merciful to reviewers digging through a pile of applications. The reviewer expects immediate access to all the information they need within the online portfolio to accomplish their evaluation. Within twenty seconds, the UX recruiter should understand your pitch, get a sense for your work, and have your contact information at their fingertips, ready to take the next step in their hiring workflow.

For full disclosure, I’ve pulled all of these examples from my own personal orbit, and included friends and colleagues who I respect and want to uplift. Let’s explore my thought process when looking at how each designer’s site uniquely succeeds and looking for patterns to model a great UX design portfolio.

1. Total class: Liya Xu

Liya Xu is an accomplished UX designer and Amazon alum, now returning to graduate school to study design management at Pratt. She leverages her technical know-how combined with her visual sensibility to craft all-around excellent applications. Really, check out her work.

This online design portfolio has the character of a fashion spread, with well-selected attributes and succinctly written content. She allows the viewer plenty of breathing room in the empty space of the layout, to process the impact of her UX portfolio content. The case studies fall in reverse chronological order, most recent and impressive work at the top. A visitor gains immediate access to an example of work “above the fold,” peeking up from the bottom of the home screen. The experience conveys an overall modern, professional effect.

2. Authenticity: Seka Sekanwagi

Seka Sekanwagi works at Cash App as a UX researcher and comes from a well-rounded background in product design, interaction design, UX, and UI. His degree is actually in industrial design, crafting objects and tools, and bringing that same human-centered mindset to his design work. A genuine empathetic interest in other people drives his user research, questioning the meaning behind core user needs and translating them into tangible quality improvements.

The imagery and copywriting of Seka’s design portfolio establish his credibility while expressing his individuality. Selectively-edited messaging demonstrates the level of thoughtfulness that goes into his work output. He formats his work qualifications in simple typesetting, reducing the cognitive load on the visitor, and inviting them to review his qualifications at their leisure.

3. Perfect Pitch: Roochita Chachra

Roochita Chachra is an Austin-based UX designer and recent General Assembly immersive graduate who is highly active in the local creative community. Roochita enters UX design from the adjacent worlds of graphic design and digital marketing and is transitioning her career focus to allow her more opportunity to conduct user research, prototype, and problem-solve.

Whenever repositioning for a new avenue of design, it takes self-restraint to hide old projects which don’t reflect your updated professional image. A UX design portfolio needs to represent the type of work you’re looking for, not just what you’ve done. Roochita focuses hers on the UX design process, and supports it with plenty of explanations and artifacts to show the output.

4. Pure Enthusiasm: Ljupcho Sulev

Ljupcho Sulev approaches his UX projects with a passion and a positive attitude. Originally from Macedonia, he works for SoftServe out of Sofia, Bulgaria. I had the opportunity to collaborate with Ljupcho on a project, conducting user interviews and analyzing research side-by-side for weeks. His sunny disposition brightens the spirits of his team members and elevates the work.

Ljupcho’s profile is sparse and direct. He highlights his career achievements by pairing photography with bold infographics, letting his enthusiasm pop off the screen. The minimal design aesthetic allows the content to take priority over the visuals. 

5. Scannability: Aimen Awan

Aimen Awan is a UX designer with a background in software engineering and information experience design. Aimen optimizes her case studies for the viewer to scan quickly, with summaries at the top denoting her role and responsibilities on the project. Scrolling down the page, project artifacts illustrate the design process, increasing the fidelity successively up to the final product.

When developing a UX portfolio for a job search, take a lean approach like Aimen — gather feedback, and iterate on your design. We designers are all susceptible to over-designing our work, nitpicking well past diminishing returns. The most useful design portfolio feedback comes from submitting actual job applications and gauging the response, so the earlier you have something ready to share, the better. Think of it as a user test — submitting a batch of applications and fishing for feedback from hiring leads. Every response is a valuable piece of data and should help you refine your messaging and presentation.

6. Approachability: Ke Wang

Ke Wang writes his UX portfolio with a tone of casual levity, with bonus points for rhyming, and his About section reads like a social media status update. He pulls it off because his case studies scroll through examples of his overwhelming talent and work.

Website design covers some crucially important goals which require some entirely human skills. Relating to the site visitor in an approachable way is the hallmark of intuitive user experience and a good heuristic of success.

7. Clear Storytelling: Phill Abraham

Phill Abraham is a graduate of General Assembly’s User Experience Design Immersive course. Like many other UX designers, Phill arrived through a circuitous career path, with a background in psychology and experience in documentary film. He is actively involved in the local design scene, building out his book of projects.

Each case study shapes a compelling narrative of Phill’s design process. A project from his experience as a documentary filmmaker bolsters his UX portfolio and speaks to his capability to perform as a professional. Documentary is, after all, a quintessential form of user research. Phill applies his storytelling sensibility in presenting the case studies, outlining his thorough process step-by-step. As the visitor scrolls down the page, they experience a neat narrative arch outlining the scenario, the design process, and the final product.

8. The Resume Homepage: Samantha Li

Samantha is a Design Manager at Capital One and an all-around UX champion. An active organizer within the design community, she mentors students and early-career UX designers working to break into the industry. Her own UX portfolio website outlines her career journey in the form of an extended resume, dense as a novel. An evaluator doesn’t even have to click to find all of the relevant information.

The resume homepage is a great design pattern for more established professionals with a long list of accomplishments. As a best practice, scrutinize what you publish diligently. Password-protecting case studies helps avoid any disputes over showing sensitive client work, and you may need to censor any personal data that may appear in your photographs and artifacts.

Conclusion

Job hunting poses challenges even for design professionals with advanced experience. Candidates need to squeeze their credentials into a digestible size to communicate their entire work history to reviewers in a short window of attention. The importance of every element of the online UX design portfolio becomes amplified, and dialing in the nuances of messaging makes a difference in getting noticed. Emerging UX designers face an uphill challenge as they’re fleshing out their portfolio projects. UX professionals in the job market are judged by their list of accomplished projects, a frustrating situation for early-career UX designers who may be struggling to get their foot in the door with shorter resumes. The only course of action is bootstrapping through some initial projects — side projects, student projects, volunteer work, and ultimately paid UX design jobs — to demonstrate applied skills. A great UX portfolio effectively communicates your ability and value to potential clients.

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Why Should You Become a Data Scientist?

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Data is everywhere

The amount of data captured and recorded in 2020 is approximately 50 zettabytes, i.e., 50 followed by 21 zeros(!) and it’s constantly growing. Other than data captured from social media platforms, as individuals, we are constantly using devices that measure our health by tracking the number of footsteps, heart rate, sleep, and other physiological signals more regularly. Data analytics has helped greatly to discover patterns in our day-to-day activities and gently nudge us towards better health via everyday exercise and improving our quality of sleep. Just like how we track our health, internet sensors are used on everyday devices such as refrigerators, washing machines, internet routers, lights etc., to not only operate them remotely but also to monitor their functional health and provide analytics that help with troubleshooting in case of failure. 

Organizations are capturing data to better understand their products and help their consumers. Industrial plants today are installed with a variety of sensors (accelerometers, thermistors, pressure gauges) that constantly monitor high-valued equipment in order to track their performance and better predict downtime.  As internet users, we’ve experienced the convenience that results from capturing our browsing data — better search results on search engines, personalized recommendation on ecommerce websites, structured and organized inboxes, etc. Each of these features is an outcome of data science techniques of information retrieval and machine learning applied on big data. 

On the enterprise side, digital transformation such as digital payments and ubiquitous use of software and apps has propelled data generation. With a smart computer in every palm and a plethora of sensors both on commercial and industrial scale, the amount of data generated and captured will continue to explode. This constant generation of data drives new and innovative possibilities for organizations and their consumers through approaches and toolsets rooted in data science. 

Data science drives new possibilities

Data science is the study of data aimed towards making informed decisions.

On the one hand, monitoring health data and data analytics is guiding individuals to make better decisions towards their health goals. On the other hand, aggregation of health data at the community level in a convenient and accessible way sets the stage to conduct interdisciplinary research towards answering questions like, Does the amount of physical activity relate to our heart health? Can changes in heart rate over a period of time help predict heart disorders? Is weight loss connected with the quality of our sleep? In the past it was unimaginable to support such research with significant data points. However, today, a decade worth of such big data enables us to drive research on the parameters connected to different aspects of our health. It’s significant that this research is not restricted to laboratories and academic institutions but are instead driven by collaborative efforts between industry and academia.

Due to the infusion of such data, many traditional industries like insurance are getting disrupted. Previously, insurance premiums were calculated based on age and a single medical test that was performed at sign up. Now, there are efforts taken by life insurance providers to lower premiums through regular monitoring of their customers fitness trackers. With access to this big data, insurance providers are trying to understand and quantify health risks. The research efforts described above would drive quantifiable ways to measure overall health risk by fusing a variety of health metrics. All these new products will heavily rely on the use of advanced analytics that uses artificial intelligence and machine learning (AI/ML) techniques to develop models that predict personalized premiums. In order to drive these new possibilities for insights, the application of data science toolsets approaches goes through a rigorous process.

Data science is an interdisciplinary process

A data science process typically starts up with a business problem. Data required to solve the problem can come from multiple sources. Social media data such as text and images from social media platforms like Facebook and Instagram would be compartmentalized from enterprise data such as customer info and their transactions. However, depending on the problem to be solved, all relevant data are collected and can be fused across social media and enterprise domains to gain unique insights to solve the business problem.

A data science generalist works on different data formats and systematically analyses the data to extract insights from it. Data science can be subdivided into several specialized areas based on data format used to extract insights: (1) computer vision, i.e., field of study of image data, (2) natural language processing, i.e. analysis of textual data, (3) time-series processing, i.e. analysis of data varying in time such as stock market, sensor data, etc. 

A data scientist specialist is capable of applying advanced machine learning techniques, to convert unstructured data to structured format by extracting the relevant attributes of an entity from unstructured data with great accuracy. No other area has seen the impact of the data science generalist or the specialist more than in the product development lifecycle, across a gamut of organizations of all sizes.

Data scientist as a unifier in the product development lifecycle

The role of a data scientist spans across multiple stages of the product development process. Typically, a product development goes through the stages of envisioning, choosing different features to build and finally, designing those specific features. A data scientist is a unifier across all of these stages in the modern world. Even during the envisioning part, data analysis on the marketing data enables the decision on what features need to be built in terms of the need from the maximal number of customers and from a competitive standpoint. 

Once the feature list has been decided, the next step is designing those specific features. Typically, such design activities have been in the realm of designers and to a lesser extent developers. Traditionally, the designer designs features and then makes a judgment call based on user experience studies with a small sample size. However, what might be a good design for 10 users might not be a good design for 90 other users. In such situations, the designers’ judgment cannot necessarily address the entire user base. 

Organizations run different experiments to gather systematic data to audit the progress of the product. With data science toolsets, deriving the ground truth no longer needs to be constrained by such traditional design approaches. Based on the nature of the feature design, data from A/B experiment testing can provide input to both developers and designers alike on design options and product decisions that are optimal for the user base. 

Data science is the future

The spectrum of the data scientist’s role and contribution is vast. On one end, the data scientist can drive new possibilities through data-backed insights in areas like healthcare, suggest personalization options for users based on their needs, etc. On the other end, the data scientist can drive a cost-based discussion on which feature to design or what optimal option to choose. Data scientists are now the voices of customers throughout the product development process, and the unifiers through an interdisciplinary approach.

Just like making a presentation, editing documents and composing emails have become ubiquitous skills today, data science skills will pervasively be used across different functional roles to make business decisions. With the explosion in the amount of data, the demand for data scientists, data analysts, and big data engineers in the job market will only rise. Organizations are constantly looking for data professionals who can convert data into insights to make better decisions. A career in data science is simulating — the dynamic and ever-evolving nature of the field tied closely with current research keeps one young!

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15 Data Science Projects to get you Started

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When it comes to getting a job in data science, aspiring data scientists need to act like artists. Yes, that’s correct, and what I mean by that is those looking to enter this field need to have a data science portfolio of previously completed data science projects. What better way to prove to your future data science team that you’re capable of being a data scientist than proving you can do the work?

A common problem for data science entrants is that employers want candidates with experience; but how do you get experience without having experience? If you’re looking to get that first foot in the door, it would behoove you to undertake a couple of data science projects to show future employers you’ve got what it takes to use big data to identify opportunities and succeed in the field.

The good news is that we live in a time of open and abundant data. Websites like Kaggle offer a treasure trove of free data on everything from crime statistics to Pokemon to Bitcoin and more. However, the wealth of easily accessible data can be overwhelming, which is why we’ve taken it upon ourselves to present 15 data science projects you can execute in Python to showcase and improve your skills. Our diverse collection of project ideas covers a variety of topics from Spotify songs to fake news to fraud detection and techniques such as clustering, regression, and natural language processing.

Before you dive in, be sure to adhere to these four guidelines no matter which data science projects you choose:

1. Articulate the Problem and/or Scenario

It’s not enough to do a project where you use “X” to predict “Y”; you need to add some context to your work because data science does not occur in a vacuum. Tell us what you’re trying to solve and how data science can address that. Employers want to know if you can turn a problem into a question and a question into a solution. A good place to start is to depict a real-world scenario in which your project would be useful.

2. Publish and Explain Your Work

Create a GitHub repository where you can upload your Jupyter Notebooks and data. Write a blog post in which you narrate your project from start to finish, talk about the problem or question at the heart of the project, explain your decision to clean the data in a certain way or why you decided to use a certain algorithm. Potential employers need to understand your methodology.

3. Use Domain Expertise

If you’re trying to break into a specific field such as finance, health, or sports, use your knowledge of this area to enhance your project. This could mean deriving a useful question to a pressing problem or articulating a well-thought-out interpretation of your project’s results. For example, if you’re looking to become a data scientist in the finance sector, then it would be worthwhile to show how your methods can generate a return on investment.

4. Be Creative and Different

Anyone can copy and paste code that trains a machine learning algorithm. If you want to stand out, review existing data science projects that use the same data and fill in the gaps left by them. If you’re working on a prediction project, try coming up with an unexpected variable that you think would be beneficial.

Data Science Projects

1. Titanic Data

Working on the Titanic dataset is a rite of passage in data science. It’s a useful dataset that beginners can work with to improve their feature engineering and classification skills. Try using a decision tree so you can visualize the relationships between the features and the probability of surviving the Titanic.

2. Spotify Data

Spotify has an amazing API that provides access to rich data on their entire catalog of songs. You can grab cool attributes such as a song’s acousticness, danceability, and energy. The great thing about this data source is that the project possibilities are almost endless. You can use these features to try to predict genre or popularity. One fun idea would be to try to better understand your own music, training a machine learning classifier on two sets of songs; songs you like and songs you do not.

3. Personality Data Clustering

You’ve probably heard the phrase, “There are X types of people.” Well, now you can actually find out how many types of people there really are. Using this dataset of almost 20k responses to the Big Five Personality Test, you can actually answer this question. Throw this data into a clustering algorithm such as KMeans and sort this into K number of groups. Once you decide on the optimal number of clusters, it’s incumbent on you to define each cluster. Come up with labels that add meaning to each group and don’t be afraid to use plenty of charts and graphs to support your interpretation.

4. Fake News

If you have an interest in natural language processing, building a classifier to differentiate between fake and real news is a great way to demonstrate that. Fake news is a problem that social media platforms have been struggling with for the past several years and a project that tackles this problem is a great way to show you care about solving real-world problems. Use your classifier to identify interesting insights about the patterns in fake versus real news; for example, tell us which words or phrases are most associated with fake news articles.

5. COVID-19 Dataset

There probably isn’t a more relevant use of data science than a project analyzing COVID-19. This dataset provides a wealth of information related to the pandemic. It provides a great opportunity to show off your exploratory data analysis chops. Take a deep dive into this data and through the use of data visualization unearth patterns about the rate of Covid infection by county, state, and by country.

6. Telco Customer Churn

If you’re looking for a straightforward project that is extremely applicable to the business world, then this one’s for you. Use this dataset to train a classifier that predicts customer churn. If you can show employers you know how to prevent customers from leaving their business you’ll most definitely grab their attention. Pro tip: this is a great projection to show your understanding of classification metrics besides accuracy such as precision and recall.

7. Lending Club Loans

Like the Telco project, the Lending Club loan dataset is extremely relevant to the business world. Here you can train a classifier that predicts whether or not a Lending Club loanee will pay back a loan using a wealth of information such as credit score, loan amount, and loan purpose. There are a lot of variables at your disposal, so I’d recommend starting with a handful of features and working your way up from there. See how far you can get with just the basics.

Also, this is a fairly untidy dataset that will require extensive cleaning and feature engineering, which is a good thing because that is often the case with real-world data. Be sure to explain your methodology behind preparing your dataset for the machine learning algorithm — this informs the audience of your domain expertise.

8. Breast Cancer Detection

This dataset provides a simpler classification scenario in which you can use health-related variables to predict instances of breast cancer. If you’re looking to apply your data science skills to the medical field, this is certainly worth a shot.

9. Housing Regression

If classification isn’t your thing, then might I recommend this ready-made regression project in which you can predict home prices using variables like square footage, number of bedrooms, and year built. A project such as this can help you understand the factors driving home sales and let you get creative in your feature engineering. Try to involve outside data that can serve as proxies for quality of life, education, and other things that might influence home prices. And if you want to show off your scraping skills, then you can always create your own dataset by scraping Zillow.

10. Seeds Clustering

The seeds dataset from UCI provides a simple opportunity to use clustering. Use the seven attributes to sort the 210 seeds into K number of groups. If you’re looking to go beyond KMeans, try using hierarchical clustering, which can be useful for this dataset because the low number of samples can be easily visualized with a dendrogram.

11. Credit Card Fraud Detection

Another project idea for those of you intent on using business world data is to train a classifier to predict instances of credit card fraud. The value of this project to you comes from the fact that it’s an imbalanced dataset, meaning that one class vastly outweighs the other (in this case, non-fraudulent transactions versus fraudulent). Training a model that is 99% accurate is essentially useless so it’s up to you to use non-accuracy metrics to demonstrate the success of your model.

12. AutoMPG

This is a great beginner regression project in which you can use car features to predict their fuel efficiency. Given that this data is from the past, an interesting idea you can use is to see how well this model does on data from recent cars, as a way to show how car fuel efficiency has evolved over the years.

13. World Happiness

Using data science to unlock what’s behind happiness? Maybe you can with this dataset on world happiness rankings. You can go a number of ways with this project; you can use regression to predict happiness score, cluster countries based on socio-economic characteristics, or visualize the change in happiness throughout the world from the years 2015 to 2019.

14. Political Identity

The Nationscape Data Set is an absolute goldmine of data on the demographics and political identities of Americans. If you’re a politics junkie it’ll be sure to satisfy your fix. Their most recent round of data features over 300,000 instances of data collected from extensive surveys of Americans. If you’re interested in using demographic information for political ideology or party identification this is the dataset for you. This is an especially great project to flex your domain expertise in study design, research, and conclusion. Political analysis is replete with shoddy interpretations that lack empirical data analysis and you could use this dataset to either confirm or dispel them. But be warned that this data will require plenty of cleaning, which is something you’ll need to get used to, given that’s the majority of the job.

15. Box Office Prediction

If you’re a movie buff, then we’ve got you covered with the TMDB dataset. See if you can build a workable box office revenue prediction model trained on 5000 movies worth of data. Does genre actually correlate with box office success? Can we use runtime and language to help explain the variation in the revenue? Find out the answers to those questions and more with this project.

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How to Get a Job in Data Science Fast

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You want to get a data science job fast. Obviously, no one wants one to get a job slowly. But the time it takes to find a job is relative to you and your situation. When I was seeking my first data science job, I had normal just Kevin bills and things to budget for, plus a growing family who was hoping I’d get a job fast. This was different from some of my classmates, while others had their own versions of why they needed a job fast, too. I believe that when writing a how-to guide on getting a data science job quickly, we should really acknowledge that we’re talking about getting you, the reader, a job faster. Throughout this article, we’ll discuss how to get a job as a data scientist faster than you might otherwise, all things considered.

Getting a job faster is not an easy task in any industry, and getting a job faster as a data scientist has additional encumbrances. Some jobs, extremely well-paying jobs, require a nebulous skill set that most adults could acquire after several years in the professional working world. Data science is not one of those jobs. For all the talk about what a data scientist actually does, there’s a definite understanding that the set of skills necessary to successfully execute any version of the job are markedly technical, a bit esoteric, and specialized. This has pros and cons, which we’ll discuss. The community of people who aspire to join this field, as well as people already in the field, is fairly narrow which also has pros and cons.

Throughout this article, we’ll cover two main ways to speed up the time it takes to get a data science job: becoming aware of the wealth of opportunities, and increasing the likelihood that you could be considered employable.

Becoming Aware of the Wealth of Opportunities

Data science is a growing, in-demand field. See for yourself in Camm, Bowers, and Davenport’s article, “The Recession’s Impact on Analytics and Data Science” and “Why data scientist is the most promising job of 2019” by Alison DeNisco Rayome. It’s no secret however that these reports often only consider formal data science job board posts. You may have heard or already know that there exists a hidden job market. It stands to reason that if this hidden job market exists, there may also be a number of companies who have not identified their need for a data scientist yet, but likely need some portion of data science work. Here’s your action plan, assuming you already have the requisite skills to be a data scientist:

1. Find a company local to your region. This is easier if you know someone at that company, but if you don’t know anyone, just think through the industries that you’d like to build a career in. Search for several companies in those fields and consider a list of problems that might be faced by that organization, or even those industries at large.

2. Do some data work. Try to keep the scope of the project limited to something you could accomplish in one to two weekends. The idea here is not to create a thesis on some topic, but rather to add to your list of projects you can comfortably talk about in a future interview. This also does not have to be groundbreaking, bleeding edge work. Planning, setting up, and executing a hypothesis test for a company who is considering two discount rates for an upcoming sale will give you a ton more fodder for interviews over a half-baked computer vision model with no clear deliverable or impact on a business.

3. You have now done data science work. If you didn’t charge money for your services on the first run, shame on you. Charge more next time.

4. Repeat this process. The nice thing about these mini projects is that you can queue up your next potential projects while you execute the work for your current project at the same time.

Alternatively, you could consider jobs that are what I call the “yeah but there’s this thing…” type jobs. For example, let’s say you’re setting up a database for a non-profit and really that’s all they need. The thing is… it’s really your friend’s non-profit, all they need is their website to log some info into a database, and they can’t pay you. Of course you should not do things that compromise your morals or leave you feeling as though you’ve lowered your self worth in any way. Of course you’d help out your friend. Of course you would love some experience setting up a database, even if you don’t get to play with big data. Does that mean that you need to explain all of those in your next job interview? Of course not! Take the job and continue to interview for others. Do work as a data engineer. Almost everyone’s jobs have a “yeah but” element to them; it’s about whether the role will help increase your likelihood of being considered employable in the future.

Increasing the Likelihood That You Could Be Considered Employable

Thought experiment: a CTO comes to you with a vague list of Python libraries, deep learning frameworks, and several models which seem relevant to some problems your company is facing and tasks you with finding someone who can help solve those issues. Who would you turn to if you had to pick a partner in this scenario? I’ll give you a hint — you picked the person who satisfied three, maybe four criteria on what you and that team are capable of.

Recruiting in the real world is no different. Recruiters are mitigating their risk of hiring someone that won’t be able to perform the duties of the position. The way they execute is by figuring out the skills (usually indicated by demonstrated use of a particular library) necessary for the position, then finding the person who seems like they can execute on the highest number of the listed skills. In other words, a recruiter is looking to check a lot of boxes that limit the risk of you as a candidate. As a candidate, the mindset shift you need to come to terms with is that they want and need to hire someone. The recruiter is trying to find the lowest risk person, because the CTO likely has some sort of bearing on that recruiter’s position. You need to basically become the least risky hire, which makes you the best hire, amongst a pool of candidates.

There are several ways to check these boxes if you’re the recruiter. The first is obvious: find out where a group of people who successfully complete the functions of the job were trained, and then hire them. In data science, we see many candidates with training from a bootcamp, a master’s program, or PhDs. Does that mean that you need these degrees to successfully perform the function of the job? I’d argue no — it just means that people who are capable of attaining those relevant degrees are less risky to hire. Attending General Assembly is a fantastic way to show that you have acquired the relevant skills for the job.

Instead of having your resume alone speak to your skill, you can have someone in your network speak to your skills. Building a community of people who recognize your value in the field is incredibly powerful. While joining other pre-built networks is great, and opens doors to new opportunities, I’ve personally found that the communities I co-created are the strongest for me when it comes to finding a job as a data scientist. These have taken two forms: natural communities (making friends), and curated communities. Natural communities are your coworkers, friends, and fellow classmates. They become your community who can eventually speak up and advocate for you when you’re checking off those boxes. Curated communities might be a Meetup group that gathers once a month to talk about machine learning, or an email newsletter of interesting papers on Arxiv, or a Slack group you start with former classmates and data scientists you meet in the industry. In my opinion, the channel matters less, as long as your community is in a similar space as you.

Once you have the community, you can rely on them to pass things your way and you can do the same. Another benefit of General Assembly is its focus on turning thinkers into a community of creators. It’s almost guaranteed that someone in your cohort, or at a workshop or event has a similar interest as you. I’ve made contacts that passed alongside gig opportunities, and I’ve met my cofounder inside the walls of General Assembly! It’s all there, just waiting for you to act.

Regardless of what your job hunt looks like, it’s important to remember that it’s your job hunt. You might be looking for a side gig to last while you live nomadically, a job that’s a stepping stone, or a new career as a data scientist. You might approach the job hunt with a six-pack of post-graduate degrees; you might be switching from a dead end role or industry, or you might be trying out a machine learning bootcamp after finishing your PhD. Regardless of your unique situation, you’ll get a job in data science fast as long as you acknowledge where you’re currently at, and work ridiculously hard to move forward.

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What is Data Science?

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It’s been anointed “the sexiest job of the 21st century”, companies are rushing to invest billions of dollars into it, and it’s going to change the world — but what do people mean when they mention “data science”? There’s been a lot of hype about data science and deservedly so, but the excitement has helped obfuscate the fundamental identity of the field. Anyone looking to involve themselves in data science needs to understand what it actually is and is not.

In this article, we’ll lay out a deep definition of the field, complete descriptions of the data science workflow, and data science tasks used in the real world. We hope that any would-be entrants into this line of work will come away reading this article with a nuanced understanding of data science that can help them decide to enter and navigate this exciting line of work.

So What Actually is Data Science?

A quick definition of data science might be articulated as an interdisciplinary field that primarily uses statistics and computer programming to derive insights from and base decisions from a collection of information represented as numerical figures. The “science” part in data science is quite apt because data science very much follows a scientific process that involves formulating a hypothesis and using a specific toolset to confirm or dispel that hypothesis. At the end of the day, data science is about turning a problem into a question and a question into an answer and/or solution.

Tackling the meaning of data science also means interrogating the meaning of data. Data can be easily described as “information encoded as numbers” but that doesn’t tell us why it’s important. The value of data stems from the notion that data is a tangible manifestation of the intangible. Data provides solid support to aid our interpretations of the world. For example, a weather app can tell you it’s cold outside but telling you that the temperature is 38 degrees fahrenheit provides you with a stronger and specific understanding of the weather.

Data comes in two forms: qualitative and quantitative.

Qualitative data is categorical data that does not naturally come in the form of numbers, such as demographic labels that you can select on a census form to indicate gender, state, and ethnicity. 

Quantitative data is numerical data that can be processed through mathematical functions; for example stock prices, sports stats, and biometric information.

Quantitative can be subdivided into smaller categories such as ordinal, discrete, and continuous.

Ordinal: A sort of qualitative and quantitative hybrid variable in which the values have a hierarchical ranking. Any sort of star rating system of reviews is a perfect example of this; we know that a four-star review is greater than a three-star review, but can’t say for sure that a four- star review is twice as good as a two-star review.

Discrete: These are countable and finite values that often appear in the form of integers. Examples include number of franchises owned by a company and number of votes cast in an election. It’s important to remember discrete variables have a finite range of numbers and can never be negative.

Continuous: Unlike discrete variables, continuous can appear in decimal form and have an infinite range of possibilities. Things like company profit, temperature, and weight can all be described as continuous. 

What Does Data Science Look Like?

Now that we’ve established a base understanding of data science, it’s time to delve into what data science actually looks like. To answer this question, we need to go over the data science workflow, which encapsulates what a data science project looks like from start to finish. We’ll touch on typical questions at the heart of data science projects and then examine an example data science workflow to see how data science was used to achieve success.

The Data Science Checklist

A good data science project is one that satisfies the following criteria:

Specificity: Derive a hypothesis and/or question that’s specific and to the point. Having a vague approach can often lead to a waste of time with no end product.

Attainability: Can your questions be answered? Do you have access to the required data? It’s easy to come up with an interesting question but if it can’t be answered then it has no value. The same goes for data, which is only useful if you can get your hands on it.

Measurability: Can what you’re applying data science to be quantified? Can the problem you’re addressing be represented in numerical form? Are there quantifiable benchmarks for success? 

As previously mentioned, a core aspect of data science is the process of deriving a question, especially one that is specific and achievable. Typical data science questions ask things like, does X predict Y and what are the distinct groups in our data? To get a sense of data science questions, let’s take a look at some business-world-appropriate ones:

  • What is the likelihood that a customer will buy this product?
  • Did we observe an increase in sales after implementing a new policy?
  • Is this a good or bad review?
  • How much demand will there be for my service tomorrow?
  • Is this the cheapest way to deliver our goods?
  • Is there a better way to segment our marketing strategies?
  • What groups of products are customers purchasing together?
  • Can we automate this simple yes/no decision?

All eight of these questions are excellent examples of how businesses use data science to advance themselves. Each question addresses a problem or issue in a way that can be answered using data science.

The Data Science Workflow

Once we’ve established our hypothesis and questions, we can now move onto what I like to call the data science workflow, a step-by-step description of a typical data science project process.

After asking a question, the next steps are:

  1. Get and Understand the Data. We obviously need to acquire data for our project, but sometimes that can be more difficult than expected if you need to scrape for it or if privacy issues are involved. Make sure you understand how the data was sampled and the population it represents. This will be crucial in the interpretation of your results.
  1. Data Cleaning and Exploration. The dirty secret of data science is that data is often quite dirty so you can expect to do significant cleaning which often involves constructing your variables in a way that makes your project doable. Get to know your data through exploratory data analysis. Establish a base understanding of the patterns in your dataset through charts and graphs.
  1. Modeling. This represents the main course of the data science process; it’s where you get to use the fancy powerful tools. In this part, you build a model that can help you answer a question such as can we predict future sales of a product from your dataset.
  1. Presentation. Now it’s time to present the results of your findings. Did you confirm or dispel your hypothesis? What are the answers to the questions you started off with? How do your results advance our understanding of the issue at hand? Articulate your project in a clear and concise manner that makes it digestible for your audience, which could be another team in your company or your company’s executives.

Data Science Workflow Example: Predicting Neonatal Infection

Now let’s parse out an example of how data science can affect meaningful real-world impact, taken from the book Big Data: A Revolution That Will Transform How We Live, Work, and Think.

We start with a problem: Children born prematurely are at high risk of developing infections, many of which are not detected until after a child is sick.

Then we turn that problem into a question: Can we detect patterns in the data that accurately predict infection before it occurs?

Next, we gather relevant data: variables such as heart rate, respiration rate, blood pressure, and more.

Then we decide on the appropriate tool: a machine learning model that uses past data to predict future outcomes.

Finally, what impact do our methods have? The model is able to predict the onset of infection before symptoms appear, thus allowing doctors to administer treatment earlier in the infection process and increasing the chances of survival for patients.

This is a fantastic example of data science in action because every step in the process has a clear and easily understandable function towards a beneficial outcome.

Data Science Tasks

Data scientists are basically Swiss Army knives, in that they possess a wide range of abilities — it’s why they’re so valuable. Let’s go over the specific tasks that data scientists typically perform on the job.

Data acquisition: For data scientists, this usually involves querying databases set up by their companies to provide easy access to reams of data. Data scientists frequently write SQL queries to retrieve data. Outside of querying databases, data scientists can use APIs or web scraping to acquire data.

Data cleaning: We touched on this before, but it can’t be emphasized enough that data cleaning will take up the vast majority of your time. Cleaning oftens means dealing with null values, dropping irrelevant variables, and feature engineering which means transforming data in a way so that it can be processed by a model.

Data visualization: Crafting and presenting visually appealing and understandable charts is a hugely valuable skill. Visualization has an uncanny ability to communicate important bits of information from a mass of data. Good data scientists will use data visualization to help themselves and their audiences better understand what’s going on.

Statistical analysis: Statistical tests are used to confirm and/or dispel a data scientist’s hypothesis. A t-test or chi-square are used to evaluate the existence of certain relationships. A/B testing is a popular use case of statistical analysis; if a team wants to know which of two website designs leads to more clicks, then an A/B test is the right solution.

Machine learning: This is where data scientists use models that make predictions based on past observations. If a bank wants to know which customers are likely to pay back loans, then they can use a machine learning model trained on past loans to answer that question.

Computer science: Data scientists need adequate computer programming skills because many of the tasks they undertake involve writing code. In addition, some data science roles require data scientists to function as software engineers because data scientists have to implement their methodologies into their company’s backend servers.

Communication: You can be a math and computer whiz, but if you can’t explain your work to a novice audience, your talents might as well be useless. A great data scientist can distill digestible insights from complex analyses for a non-technical audience, translating how a p-value or correlation score is relevant to a part of the company’s business. If your company is going to make a potentially costly or lucrative decision based on your data science work, then it’s incumbent on you to make sure they understand your process and results as much as possible.

Conclusion

We hope this article helped to demystify this exciting and increasingly important line of work. It’s pertinent to anyone who’s curious about data science — whether it’s a college student or an executive thinking about hiring a data science team — that they understand what this field is about and what it can and cannot do.

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