Lillian Pierson - Data Science For Dummies

Over on the companion site to this book ( https://businessgrowth.ai/ ), you can find free access to a fun, 45-second quiz about data career paths. It helps you uncover your own inner data superhero type. Take the quiz to receive personalized data career recommendations that directly align with your unique combination of data skills, personality, and passions.

For now, let’s take a look at the three main data superhero archetypes that I’ve seen evolving and developing over the past decade.

Some data science professionals were simply born to be implementers. If that’s you, then your secret superpower is building data and artificial intelligence (AI) solutions. You have a meticulous attention to detail that naturally helps you in coding up innovative solutions that deliver reliable and accurate results — almost every time. When you’re facing a technical challenge, you can be more than a little stubborn. You’re able to accomplish the task, no matter how complex.

Without implementers, none of today’s groundbreaking technologies would even exist. Their unparalleled discipline and inquisitiveness keep them in the problem-solving game all the way until project completion. They usually start off a project with a simple request and some messy data, but through sheer perseverance and brainpower, they're able to turn them into clear and accurate predictive data insights — or a data system, if they prefer to implement data engineering rather than data science tasks. If you’re a data implementer, math and coding are your bread-and-butter, so to speak.

Part 2of this book are dedicated to showing you the basics of data science and the skills you need to take on to get started in a career in data science implementation. You may also be interested in how your work in this area is applied to improve a business’s profitability. You can read all about this topic in Part 3.

Other data science professionals naturally gravitate more toward business, strategy, and product. They take their data science expertise and apply it to lead profit-forming data science projects and products. If you’re a natural data leader, then you’re gifted at leading teams and project stakeholders through the process of building successful data solutions. You’re a meticulous planner and organizer, which empowers you to show up at the right place and the right time, and hopefully keep your team members moving forward without delay.

Data leaders love data science just as much as data implementers and data entrepreneurs — you can read about them in the later section “ The data entrepreneur.” The difference between most data implementers and data leaders is that leaders generally love data science for the incredible outcomes that it makes possible. They have a deep passion for using their data science expertise and leadership skills to create tangible results. Data leaders love to collaborate with smart people across the company to get the job done right. With teamwork, and some input from the data implementation team, they form brilliant plans for accomplishing any task, no matter how complex. They harness manpower, data science savvy, and serious business acumen to produce some of the most innovative technologies on the planet.

Chapters 7through 9and Chapters 15through 17in this book are dedicated to showing you the basics of the data science leadership-and-strategy skills you need in order to nail down a job as a data science leader.

That said, to lead data science projects, you should know what’s involved in implementing them — you’ll lead a team of data implementers, after all. See Part 2— it covers all the basics on data science implementation. You also need to know prominent data science use cases, which you can explore over in Part 3.

The third data superhero archetype that has evolved over the past decade is the data entrepreneur. If you’re a data entrepreneur, your secret superpower is building up businesses by delivering exceptional data science services and products.

You have the same type of focus and drive as the data implementer, but you apply it toward bringing your business vision to reality. But, like the data leader, your love for data science is inspired mostly by the incredible outcomes that it makes possible. A data entrepreneur has many overlapping traits and a greater affinity for either the data implementer or the data leader, but with one important difference:

Data entrepreneurs crave the creative freedom that comes with being a founder.

Data entrepreneurs are more risk-tolerant than their data implementer or data leader counterparts. This risk tolerance and desire for freedom allows them to do what they do — which is to create a vision for a business and then use their data science expertise to guide the business to turn that vision into reality.

For more information on how to transform data science expertise into a profitable product or business, jump over to Part 3.

Using my own data science career to illustrate what this framework looks like in action, (as mentioned earlier in this chapter) I started off as a data science implementer, and quickly turned into a data entrepreneur. Within my data business, however, my focus has been on data science training services, data strategy services, and mentoring data entrepreneurs to build world-class businesses. I’ve helped educate more than a million data professionals on data science and helped grow existing data science communities to more than 650,000 data professionals — and counting. Stepping back, you could say that although I call myself a data entrepreneur, the work I do has a higher degree of affinity to data leadership than data implementation.

I encourage you to go to the companion site to this book at https://businessgrowth.ai/ and take that career path quiz I mention earlier in this section. The quiz can give you a head-start in determining where you best fit within the spectrum of data science superhero archetypes.

Chapter 2

IN THIS CHAPTER

Unraveling the big data story

Looking at important data sources

Differentiating data science from data engineering

Storing data on-premise or in a cloud

Exploring other data engineering solutions

Though data and artificial intelligence (AI) are extremely interesting topics in the eyes of the public, most laypeople aren’t aware of what data really is or how it’s used to improve people’s lives. This chapter tells the full story about big data, explains where big data comes from and how it’s used, and then outlines the roles that machine learning engineers, data engineers, and data scientists play in the modern data ecosystem. In this chapter, I introduce the fundamental concepts related to storing and processing data for data science so that this information can serve as the basis for laying out your plans for leveraging data science to improve business performance.