How to Data Science without a DegreeThoughts and advice from a data scientistJason JungBlockedUnblockFollowFollowingOct 5Me at GoDaddy officeINTRODUCTIONHey there.I want to show you how to become a Data Scientist without a degree (or for free)..Before we get into the details, let’s find out what data science is about.WHAT DO YOU DO AS A DATA SCIENTIST?Skip this section if you already know this.Well, from my experience working as a data scientist at a few companies like GoDaddy, HERE, and GoGo, data scientists solve problems by applying machine learning on big data..Some examples are: predicting customers’ probability to cancel subscription, identifying data anomalies, computing ad-hoc analysis on gigabytes or terabytes of data, clustering customers into meaningful groups, text analytics to find topics in customer chat transcripts, calculating revenue projection, and the list does not end.As a data scientist, you get thrown a lot of different types of problems..At the end, you often need to present the results and techniques to the executives and less-technical audience.Also, as a data scientist, you need to continue to learn and adapt..To learn the skills I mentioned above online, I recommend these:Math: Multivariable calculus, differential equation, linear algebra from Khan Academy.Statistics: Statistics in R and Intro to data science: Data Science Specialization by Johns Hopkins University on Coursera.Python: CodeAcademy.com for general programming in Python.To see examples of what data science can do, check out Kaggle.com where people learn and compete data science projects..Also, check out DataCamp.com which provides hands on tutorials on various data science topics in both R and Python.By the end of phase 1, you should be comfortable with performing simple machine learning techniques like logistic/linear regression and decision trees on either R or Python..For example, DeepLearning.ai on Coursera gives you a very good and practical side of deep learning where as Stanford’s CS231n Computer Vision course delves much deeper.In this phase, take the following:Machine Learning: Andrew Ng’s Machine Learning Course on Coursera..But still very useful for understanding fundamentals of machine learning.Machine Learning: Stanford CS229 Machine Learning Course..Since it’s very affordable, I would recommend you pay for it.Computer Vision: Stanford CS231n Convolutional Neural Networks for Visual Recognition Course.Natural Language Processing: Stanford CS224n Natural Language Processing with Deep Learning Course.Again, there are other resources like DataCamp, Udacity, edX, and fast.ai that you can check out to learn various topics.PHASE 3: INDEPENDENCEDuring this phase, you should prepare for interviews and continue to learn new and deeper topics..If you are in a data science program, most classes make you complete machine learning projects, which are very good to practice your skills and to show the employers what you have done..Check out Leetcode.com to practice your SQL and programming skills.Finally, by this stage, you should have enough knowledge to explore different machine learning topics and learn deeper..I tried to focus on specific classes you should take instead of specific tools or Python/R packages you need to learn because the classes will teach those things.If you want to see example codes of machine learning, check out my Github repository which I constantly update with new things I learn.. More details