Getting into Data Science is definitely a good option. These days a PhD is not a requirement.
Try some of the micro courses on Kaggle: https://www.kaggle.com/learn/overview.
There are also some great machine learning courses and free code fast.ai.
There are almost too many options available for you to learn principles of data analysis and specifically data science online without a formal degree. Many of them are entirely free (eg, Coursera) and some cost about the same as the tuition for a formal degree (eg, MSc Data Science UC Berkeley).
Scott recommends the following next steps:
Francisco J. Cordero
Great question! A lot of what is taught in the field is very technical and specific. There are online tools that can teach you these things, however, in most cases data scientists are expected to have advance degrees like a Masters or PHd. A great way to learn more about the field is to do some industry research through google.
Francisco J. recommends the following next steps:
check out fastAI classes
There are plenty of free and paid resources online to help you (Codecademy, Udemy, Udacity, Coursera, FastAI, HackerRank, Leetcode, etc). There are also data science bootcamps (General Assembly, Flatiron, Metis, NYC Data Science Academy, etc.) that last usually around 3 months if you prefer an in-person feel.
The most important thing is building a portfolio of projects. Having 2 or 3 good ones to discuss in interviews and on your resume is great. Ask a question you're interested in, then search the web for a dataset and start exploring. Create an output, and if possible share your work on GitHub/LinkedIn/a personal website. Interviewers love seeing who you are as a person & data scientist through your personal projects.
First, to get the knowledge and skill you need, check out FastAI
it is an awesome course for people without PHD to learn AI and it's a great class!
and then in order to get a job without relevant degree or experience, you can build a profile of your data science work, try to do below:
1. Kaggle competition
2. contribute to ML libraries on Github
3. create and open source your own libraries on Github
4. make fun apps, this can be as simple as an app that recognize different types of bear
YES. While having that degree will no-doubt help open doors, in an open-source world there is definitely no reason to wait on credentials or college course completions to get started. When employers hire in this space, having a tangible portfolio that you can speak to in-depth is every bit as powerful as an expensive degree from a major university.
Download a package and a tutorial, find a data set that you are passionate about, explore. For me, I love sports, so pulling down NBA data excites me to spend hours digging in. Find a data set that you love. You can begin to build a resume as you learn.
FREE: Find online courses and dig in. Use resources like Kaggle.com to learn the type of problems that need to be solved. Learn from others http://blog.kaggle.com/category/winners-interviews/ about how to build models.
As you know, "Data Scientist" is a broad bucket, and can mean one skill or a combination of several skills. I'm a fan of first making sure you know WHY you want to be a data scientist, and letting that guide your path. Be clear about the types of problems that you are passionate about solving. If that requires data acquisition skills, start there. If it requires intense statistical analysis, focus your research there. Other work may be more frontend or data visualization, and that would help inform you about where to start too.
Vance recommends the following next steps: