Recently I enrolled in Flatirons Data Science Bootcamp. My learning goals for the program are simple: Data Alchemy: The magical process of transforming data into insights.
My background is in Corporate America, C.P.G Marketing, with a heavy focus on leading teams in New Product Innovation. I have my M.B.A in Marketing and have had several analysts related responsibilities.
Why Data Science?
Innovation! Specifically, using data to drive innovation! Data and Innovation are two words that are rarely combined, but to me this combination is the next frontier in innovation. How do companies and or inventors take the overwhelming amounts of available data and build a structure that fuels the consistent transformation of data it into an insight that leads to Disruptive Innovation. This is my goal! Transforming data to drive disruptive innovation, design thinking and strategic advantage.
Data Science and Innovation
Now, I am only in my third week of the program, so I realize my naivete. Perhaps my expectations will change as my coursework unfolds, yet I remain optimistic as I am seeing the evidence of great potential in Data Science. More specifically, I am seeing an overlap in the core tenants taught in Data Science to those required in Innovation. I have learned during my several years of leading cross functional teams in New Product development, that great innovation hinges on great insights; those unspoken, unrecognized but game changing truths that if discovered and capitalized on can lead to disruptive ideas.
For example, several years ago an innovator noticed people laboriously carrying suitcases around the airport. Crazy right? Then he or she took that pattern in consumer behavior (the data) and changed the way we looked at suitcases by adding wheels…. Brilliant! In innovation the insight is the key pillar to success. In Data Science this also seems to be true, taking mostly ubiquitous data and making new and unique connections to transform recommendations from basic to innovative. This is my hope for how I will leverage Data Science in the future!
Hope Is Not A Strategy
While hope is not a strategy, at least not in the world of business, my hope for the curriculum will be to help me gain confidence and proficiency in a few key areas which I believe will allow me to achieve data alchemy:
1. Learning to Learn Data Science
The first area I am looking to master is learning to learn in the field of Data Science, both from a technical perspective and a statistical methods perspective. Early in my career as a software engineer, I saw how fast technology changes. From C++, to Java, to C#, etc., and despite the shift in languages, my core programing knowledge was strong, so I was able to quickly learn the next best language. Despite the new syntax the core tenants of programing remained the same, a “for loop” remained a “for loop”, OOP principles remained the same. Essentially once a strong foundation is developed that foundation can be applied to aid in learning the next language. I am assuming the same calculus can be said within Data Science where Python, R, Tableau, Power Bi, dominate today and mastery now will form the foundation to learning future technologies. Similarly, I am hoping the same can be said about the statistical methods we will learn in bootcamp. Where advancements in AI and machine learning will certainly occur, as these advancements unfold, I am hoping the statistical foundations we learn in bootcamp will allow me to quickly learn new methods. Overall, my goal here is to become confident in my core knowledge of the fundamentals so as advancements happen, I can quickly incorporate into my working knowledge allowing me to grow within the field.
2. Learning a Repeatable Methodology for Transforming Data into Insights
The second area I am looking to master is the scientific approach to repeatedly turning data into insights. Here I mean methodology. Throughout my many years of leading cross functional teams in New Product Innovation, employing a framework to breakdown complex problems into subproblems and then gathering data around the identified sub-areas is key to eating the elephant one bite at a time. While it is still early in our coursework, my ancillary reading suggestions Data Scientists employ a scientific framework to make data driven recommendations. The prescribed methodology includes: outlining question(s), stating a hypothesis, then rapidly capturing, and analyzing data against the stated hypothesis. This approach is not unique to Data Science and overlaps with other insight capturing processes associated with new product innovation. For example, in Corporate NPD, the cross-functional teams employ Innovation frameworks such as Debono, Design Thinking and or Agile to create the structure needed to ensure all resources are in sync with responsibilities throughout the process. I found that clear methodology is key to effectively organizing workstreams. I am hoping to learn and be able to apply a Data Science methodology within Corporate America as I look to lead teams in driving data drive decision making
My Next Stop — DATA ALCHEMY!
Ultimately my aim is a renewing my approach to problem-solving, transforming myself from an Innovator with strengths in analytics, to Innovator with Data Alchemist abilities. I look forward to future posts throughout my journey! Checkout my 3 part post on introducing you to dataframes!