CONTEXT
Since I've been working on data science, I was exposed to problem-solving strategies so as to move from real problems to real solutions. Somehow, this was a natural complement to one of the things I'm most passionate about: the way we perceive our surrounding world and how our mind works.
After reading many books, attending many courses and doing a bunch of data science projects, I felt the need to define how I should move from real-world problems to real-world solutions in a structured way.
So, the purpose of this brief material is to share my initial summary of how to structure a problem-solving strategy. I emphasize that it is just my initial MVP about this subject. In other words, it is not supposed to be a definitive solution, not even to replace any already tested framework!
In fact, this is just an initial attempt to structure problem-solving strategies holistically, taking into account the planning stage as well as general guidelines to monitor the project and product development so as to achieve meaningful business results consistently, especially through data science tools.
This initial template must evolve over time, especially with learning from practical experience, to help solve the problems of the ever-changing world!
My first idea was to learn these tactics deeply by thinking about them and combining them. The way I use this material is to, once in a while or if I get lost at some point in the project, get back here, check where I am in the problem-solving track and remember some key points about this step. I'm sharing this compilation so anyone interested in this topic can learn or remember something relevant to solve some real problem: if this happens somehow, I would be very happy! By the way, contact me for feedback about this material by checking the "contacts" section ;)
As an important note, I didn't create any of these problem-solving tactics! I'm just a curious guy that loves to study and wants to improve my professional solutions as a data scientist. So, please take at least a brief look at the resources section to understand where this information came from.
COMPILATION MATERIAL
MAIN REFERENCES
Here is a list of resources, books and courses, that were important to make this compilation possible. Note that these resources are not in order of importance; they are more or less in the order I had first contact with the given resource. Besides, I may even forget some specific resources once the process of compiling all this information was non-linear, non-centralized and have evolved throughout time.
book: Lateral Thinking: Creativity Step by Step by Edward de Bono
book: Visual Intelligence: Sharpen Your Perception, Change Your Life by Amy E. Herman
comunity: Comunidade DS
book: Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll & Benjamin Yoskovitz
bootcamp: Le Wagon
work experience: A3Data
course: Become a Product Manager - Learn the Skills & Get the Job by Cole Mercer & Evan Kimbrell
course: Advanced Product Management: Vision, Strategy & Metrics by Cole Mercer & Evan Kimbrell
MY CONTACT
Feel free to contact me in case of questions, suggestions, discussions and any other reason you think is relevant.
