I published my own website, was posted in a major online data science publication, and was given scholarships to a competitive computer science graduate program.In the following article, I give guidelines and advice so you can make your own data science curriculum..So they can begin to work towards a more passionate career in data science.When I say “data science”, I am referring to the collection of tools that turn data into real-world actions..You can find the full book online in pdf form(free), or get a physical copy from Amazon ($27).These are just a few of the free resources that provide a detailed learning path for data science..This is intended to be high-level, and not just a list of courses to take or books to read.Data Science Curriculum GuidelineProgramming is a fundamental skill of data scientists..(Jupyter notebook vs. command line vs IDE)I took about a month to review the Python docs, the Hitchhiker’s Guide to Python, and coding challenges on CodeSignal.Hint: Keep an ear out for common problem-solving techniques used by programmers.(pronounced “algorithms”)A prerequisite for machine learning and data analysis..There are better resources out there, but these are what I used.Remember, the only way you will learn these libraries is by using them!Learn the theory and application of machine learning algorithms..Then apply the concepts you learn to real-world data that you care about.Most beginners start by working with toy data-sets from the UCI ML Repository..You can listen to it on your commute or while working out.Getting a job means being able to take real-world data and turn it into action.To do this you will need to learn how to use a business’ computational resources to get, transform, and process data.Amazon Web Services, Google Cloud, Microsoft AzureThis is the most under-taught part of the data science curriculum..This way you can save material for later, and focus on the topic that is relevant to you at the moment.My current Chrome Bookmarks BarIf you do this right, you can make an ordered learning path that shows you what you should be focused on..But it is important to stay grounded and remember that you are learning so you can make an impact in the world.When it comes down to it, skepticism is one of the biggest adversities you will face when learning data science.This may come from others, or it may come from yourself.Your portfolio is your way of showing the world that you are capable and confident in your own skills.Because of this, building a portfolio is the single most important thing you can do while studying data science..Some data scientists build computer vision systems to diagnose medical images, others traverse billions of data entries to find patterns in website user preferences.The applications of data science are endless, that’s why it is important to find what applications excite you.If you find topics that you are passionate about, you will be more willing to put in the work to make a great project.. More details