Learn data science online
At Springboard, we teach many students, some of them with non-traditional academic backgrounds, the fundamentals of data science. We are very familiar with the pain points of those looking to learn data science. A common question we get from these students concerns their futures in the field. Most of them are convinced that they need a strong academic background to succeed in the field of data science, especially since this is a new field where a lot of cutting-edge practices are coming to the fore now.
To begin with, we should cover what data scientists should know. There are three main data science skill-sets: statistics, programming, and business knowledge.
A data scientist needs to know statistics and math to analyze patterns in data and to manipulate it with different treatments. They need to use programming skills to deal with data at scale that can take up terabytes of space. They need to understand business fundamentals in order to communicate their findings and drive other teams to action based on their insights.
This is a diverse skill set, but one advantage it brings is that most data scientists will have not picked up all of their skills in an academic context. This means a lot of self-learning and a lot of different resources available online rather than locked up in textbooks. There are various communities such as Datatau, the Kaggle Forums and the datascience subreddit that are friendly to learners, and which are filled with useful resources.
It is entirely possible, as a result, to learn data science without a computer science or mathematics background and to learn data science for free online. There’s a roadmap to getting into data science without a data science degree.
It wouldn’t be easy. The majority of data scientists come from traditional STEM degrees, and there is still a strong affinity in the industry for people with degrees. A university education also holds more power than just the knowledge transferred: it can open up new networks for you, and offer social proofing that you are determined to overcome any challenge.
This article doesn’t seek to hide the fact that it will be hard, but it does seek to illuminate that it is possible.
Based on our research at Springboard, placing students like Sara into data science roles with non-technical backgrounds and the research we’ve gotten from our workshop with a data science career guarantee, we’ve traced a curated roadmap forward for you if you were to try to learn data science without a degree.
A path forward
1- Look everywhere for learning resources
Get acquainted with different communities and learning resources, and get a sense for which ones suit your learning style. Get a learning routine and path set based on how you want to learn. Get a sense for where you are, and how much progress you need to achieve. Our guide to data science jobs has a good overview of the skills you’ll need to succeed in data science and a good list of resources to get started if you wanted to learn data science for free, online.
You’ll want to constantly think about how to learn data science. The best data scientists are lifelong learners.
2- Learn a programming language
As a foundation, we suggest learning at least one programming language so you can start playing with data at scale. It’s strongly suggested that budding data scientists embrace one of either R or Python to begin with.
3- Learn the basics of statistics
As a data scientist, you’ll be called upon to use statistical methods to analyze and interpret data. You should be familiar with those methods as well as the overall mentality associated with thinking in probabilities.
4- Learn what data means to a particular industry
Combine your knowledge of data science methods with domain knowledge so you can start unearthing insights about a particular industry. You’ll want to learn something you’re passionate about and then start applying data science methods to it.
5- Combine your knowledge together and build real-world projects
Take the knowledge you have and start building a portfolio of interesting data science projects. Examine different angles and questions and build interesting analyses you can share with others. You’ll want to create a portfolio site using software such as WordPress and a Github account and start applying the skills you’ve learned in theory.
6- Network and get to know the data science community
You’ll want to network and get to know the data science community, whether that’s local events on Meetup or larger events like O’Reilly Strata. It’s important that you start networking and getting to know what opportunities lie in data science, and it’s important to start finding people you can collaborate with and from whom you can learn from. You’ll want to start building relationships with people at hiring companies or who have data science needs: you may even consider freelancing as a data scientist if you can build projects at a professional level.
7- Prepare for the data science interview process and break into a data science career!
After building your network and getting a few portfolio projects going, you’ll want to start tapping people within your network and reaching out for different job opportunities. At this point, you should focus on mastering your skills and passing the data science interview process. Springboard has created a comprehensive guide to data science interviews that can help in this regard.
Once you’ve mastered the data science interview, and accepted your first offer, you’ll be on a career path as a data scientist where you won’t have to look back. Learning data science will have paid off.
A curriculum of learning
For a curated flow into the basics, Springboard has a data analysis learning path that will help you organize your learning. Here are some of the basic fundamentals you should learn for each section.
How Bayes Theorem, Probability, Logic and Data Interact – This short primer on our blog goes into the basics of probability and logic, and how they interact with data.
Think Stats – This book by O’Reilly Media explains statistics from a programmer’s perspective, and is a handy guide to the stats knowledge you’ll need as a data scientist.
What math subjects would you suggest to prepare for data mining and machine learning? – This thread on the Stats Stack Exchange covers a self-made curriculum of math topics you’ll need to cover to succeed in data science.
15 Mathematics MOOCs for Data Science – This blog post covers great options for free online courses that can help you with your math skills. It’s an essential read for those looking to learn data science.
Data science sexiness: Your guide to Python and R, and which one is best – This guide covers the differences between R and Python and how you can learn both.
Datacamp – Datacamp offers both free and paid interactive online courses that can teach you R or Python. It’s a great way to learn data science with R or data science with Python online.
The SQL Tutorial for Data Analysts – This free interactive tutorial to SQL by Mode will take you from learning the basics of SQL to applying it to real-world cases.
W3Schools SQL Introduction – This tutorial from W3schools can help you learn data science with the basics of SQL.
The Data Science Process – Our five-day email course will help you frame your data science insights in a way that will change business outcomes.
Guide to Data Science Jobs – Our guide to data science jobs gives you an outline of what business knowledge you need to be a successful data scientist.
A Dramatic Tour Through Python’s Data Visualization Landscape – This guide walks through the rich data visualization landscape in Python, letting you take a look at a rich suite of tools you can use to visualize your results.
FlowingData – Nathan Yau’s informative blog is one of the web’s leading authorities on data visualization tools and techniques.
For some of you reading, this curriculum might be enough, but for those looking to learn data science in order to break into a new data science career path–read on!
What you have to do to differentiate yourself from technical degree-holders
When you’re in the job interview process, you’ll have to compete with profiles that on paper may be much stronger than yours. In order to get a data science job without a degree, you’d have separate yourself in several ways so that you have a good shot at getting in.
1- Conduct informational interviews and get referrals
You’ll want to conduct informational interviews with data scientists in the field to understand what’s going on in the industry, and to get a handle on how a certain company operates and hires. If you manage to impress a data scientist while out on a coffee with them with tons of thoughtful projects, and researched insights on the company they work for, they can become an advocate for you, and they may even decide to do a referral for you. We’ve seen referrals lead to interviews at an exponentially higher rate than applying through the web.
2- Gain project experience
Look to apply and demonstrate your data science skills in a variety of different contexts. Look for data hackathons, data for social good initiatives like Datakind and platforms like Kaggle where you can work on data science projects competitively, and where you can show your skills off. It’ll be very important to demonstrate how the skills you’ve learned, your voracious desire to learn, and your willingness to get your hands dirty and apply your learnings could translate to great performance in a new data science job.
3- Sharpen your communication skills
The one red flag that will kill strong academic applicants in data science interviews, according to recruiters we’ve talked to is the sense that they don’t have strong communication skills. You’ll want to sharpen your communication skills to ensure you can tell your story as smoothly as possible.
Learning data science without a degree always involves a bit of risk. You may spend time wondering if after all your hard work if you’ll end up with a data science job. Fear not. Follow the steps above, and you’ll be well on your way to a path many have already taken: getting into data science without a data science degree.