The Road to Data Science - A Guide for Non-Programmers
Data science is a broad, exciting, and challenging field. It’s also a lot of work. Once you figure out that data isn’t going anywhere and you have access to a few thousand dollars a month, you’ll know that becoming a data scientist is for you.
But if you’re like many people who are interested in a career change or a new field, you might be wondering where to start.
The road to data science isn’t necessarily a straight shot. There are many possible paths, and each path has its own challenges and rewards.
This article is the first in a series of posts that will take you through the Data Science roadmap step by step. Whether you’re brand new to the field or you want to understand what you can do to break into data science, the Road to Data Science is designed to be a resource that you can come back to time and time again.
What is data science?
Data science is a branch of knowledge that is concerned with collecting, analyzing, and understanding data. Data scientists are in charge of finding patterns in data, informing business operations, improving decision-making, and more.
The field of data science has a lot to offer: it’s fun, creative, and challenging. In addition to the rewarding career prospects, you also get to work with interesting datasets!
Data science is an interdisciplinary field—meaning you need know statistics and programming as well as have some expertise in machine learning or other topics (e.g. natural language processing).
How to become a data scientist
What does it take to become a data scientist? To find out, we spoke with Anne M. O’Leary-Kelly, the founder and CEO of Data Science Dojo.
O’Leary-Kelly says that to become a data scientist, you need to have three things:
Knowledge of programming languages
A deep understanding of statistics
The ability to visualize data
The six principles of data science
Data science is all about asking questions and then finding the answers. People with strong analytical skills combined with an aptitude for coding can make a great data scientist. But there’s more to it than just being an analyst.
To become a data scientist, you need to know how to:
• ask the right questions;
• clean the data;
• analyze the data;
• get insights from your analysis;
• code up solutions to problems;
and share those insights with others.
Data scientist interview questions
Data scientists are an important part of any company’s data-driven project. They’re the ones who make sense of the data and draw conclusions for the team.
It’s not easy to find a great data scientist, so companies will often interview prospective employees before hiring them. A typical interview process might include a personality test and a couple of interviews with different people on the team.
Here are some questions that you can use during your interview process to determine if this is the right role for you:
* What was one of your favorite projects?
What are your goals?
What do you want to contribute to our organization?
Online resources for beginners
If you’re just getting started, it can be easy to feel overwhelmed. Where do you start? What technologies do you need? What languages should you learn?
Luckily, there are a lot of online resources for beginners that can help. Some of my favorite beginner resources include:
* Dataquest - www.dataquest.io
* DataScience Bootcamp - dscbootcamp.com
* Coursera and EdX - coursera.org edx.org
* Kaggle - www.kaggle.com
* Udacity - www.udacity.com
The more time you spend exploring these sources, the more comfortable you’ll become with data science and the more likely you’ll be able to find a path that suits your interests and skillset.
Data scientist career path
Getting into data science can be hard. There are many possible paths, and each path has its own challenges and rewards. If you’re brand new to the field or want to know what it takes to break into data science, the Road to Data Science is designed to be a resource that you can come back to time and time again.
The first step on your journey is understanding what a data scientist does and how they do it. The following posts will take you through what skills you need to have, what kinds of jobs are available, and who hires data scientists.
You’ll also learn about different methods for getting started in data science that don’t require an advanced degree or lots of money: data hacking, working with Kaggle, taking MOOCs (massive open online courses), doing independent study, or teaching yourself data science skills on the side while maintaining another job.
Bottom line
If you want to make the most of your data, you need to start thinking about data science. Data is just about everywhere on the internet and in the world around us. And with more companies relying on data-driven decision making, data science is a field that can’t be ignored.
Whether you want to become a self-starter and teach yourself or get into graduate school for it, data science can open doors for you that never would’ve been possible otherwise. The road to data science might not be easy, but it’s worth it!