Guide For Learning Data Science by Yourself

ABOUT ME I was never good at math or economics in high school, but I understand the potential of automating and perceiving patterns outside of human observation. In my personal life, I am a growth hacker, meaning I automate many processes at the moment with Phantombuster and Duxsoup. But I am reaching the limits of these platforms. So I am learning data science to set my parameters, access more data, and make better visual representations. I have written this guide because most guides I see are written by people who were born to code and love conceptual language. Ordinary people like you and me need analogies to understand the framework of coding.  MINDSET A) If you are a self-learner, this is something for you. Suppose you aren't, don't start with coding as your first self-learning project. I have been a self-learner my entire life, and coding is by FAR the most challenging thing I have undertaken. Of course, if you are a math or physics genius, you can try it. B) Coding is about editing, not writing. After learning the basics, you understand that most coders copy and paste from Github or their older projects. Most mistakes, in the beginning, are wrong tracebacks, meaning you have misspelled, used the wrong symbol, or forgot to add a space or tab. C) It will take much longer than expected unless you are a math wizard. If you see the hour requirements, multiply it by three, and you know what the investment is. #Make it a habit of making detailed notes from the get-go. Plus, don't be like 98/100 programmers who don't consider that others would like to use their code one day. If you make notes, you turn the coding into sizeable chunks, which you can work on in your own time without losing the overview. D) Always repeat the mantra, when you make a mistake, the computer is SOOOOOOOO dumb, that I need to be correct on everything I do. If humanity were like a computer, nothing would get done in society. E) If you don't like the teacher, platform, or method, switch! I have tried to learn code at 13 different websites, everything from gamified coding to those Coursera-kind websites. Ultimately, I picked Udemy because it works for me, but I know many friends who use different platforms. LEARNING JOURNEY 📅 Before you start, you need to have a clear goal in mind. Ask yourself the following questions: What can I do better in my profession when I can run my data experiments? 2. How much time and when during the day can I best devote my time to this course? 3. What are small-scale projects that I can do as self-created milestones? 4. Am I willing to pay money for a course to be more incentivized? Also, schedule a coding hour in your Google Calendar. Let your habits take you where you want to go, not your enthusiasm or interests. PROJECTS A good project is a challenge, but it is not too hard or easy. Every course is divided into chapters; most studies already have projects, but in my personal experience, projects, especially job related, give you a grid when you have to go through obstacles. Find an experienced colleague in your company who can help you when you can figure it out yourself. Your last resort is Stackoverflow, but sometimes you must wait before that community helps out. HELP? If you like this guide, you could check out my latest Producthunt launch and hunt, upvote, and subscribe to the BETA if you like what you see. https://bit.ly/3M5dTtr