A data scientist, also known as a data analyst or a data engineer, is someone who can extract valuable insights from massive amounts of raw data. Though most often associated with the tech industry, there are plenty of fields that benefit from this kind of analysis, including biology, psychology, marketing and education. In this guide on data science for all, we’ll explore how you can use your background in fields like computer science and math to start your own career as a data scientist, no matter what your level of experience.
Data is an enabler. It helps you accomplish your goals faster, better and cheaper than before. Data science is a broad term that encompasses many skills including data analytics, machine learning, artificial intelligence, and much more. This post is meant to serve as a guide for anyone looking to get started in data science using R programming language with hands-on examples. We will also touch on related technologies like Python as well as related fields such as machine learning and artificial intelligence (AI).
Why Data Science?
Knowing how to make use of data science can have a tremendous impact on your life. Companies like Google, Amazon, and Facebook are making huge profits off their ability to read your mind and predict what you want before you even know it. And they’re using algorithms to do it! Being able to use data science will give you a distinct advantage in any field that involves knowing what people want, need, or will be interested in in the future. Many experts also agree that studying math and statistics is one of the best ways to develop critical thinking skills – which is pretty useful when trying to work out how exactly all those algorithms work. As technology continues to advance at such an impressive rate, knowledge of data science will only become more and more important. So why not get ahead by learning it early? Data science won’t go away anytime soon so why not master it now? Before you know it, you’ll be changing lives for good…and making some money while doing so 🙂
What is Data Science?
The term data science was coined by professors at Columbia University in 2008 to describe a discipline that marries skills from across computer science, statistics, and mathematics. What does it mean to be a data scientist? The best way to understand what data scientists do is by thinking about all of those headlines that have inundated us over recent years, like The End of Theory: The Data Deluge Makes the Scientific Method Obsolete or Data Scientist: The Sexiest Job of the 21st Century. In these articles we are told that humans are increasingly useless as information-gathering tools; machines gather a huge amount of raw information through sensors and data streams every day. We’re only beginning to recognize just how much information there is out there waiting to be harnessed, and much of it hasn’t been properly catalogued yet. This brings us back to our definition: A data scientist works with data—raw facts—to create knowledge. Knowledge could take many forms, including insight into how people interact with products or discoveries about new possibilities or avenues for research within your industry.
How to Start Learning Data Science?
If you’re interested in data science, you’re not alone. It is one of today’s most in-demand skills. If you’re like many people, though, your experience with statistics and advanced mathematics stops at high school or college—and that may be keeping you from pursuing a career in data science. To get into data science, what do you need to know? A lot! But don’t let that intimidate you. The good news is there are plenty of resources available if you want to learn more about data science. Here are some steps to take if you want to start learning data science but don’t have any background in statistics and math:
[See Data Scientist Career Path] There are lots of great books on data science. Before diving into deep material, pick up a book like [The Signal And The Noise] . In general, these books will help you get an overview of what goes into creating models, algorithms, and applications from a beginner’s perspective while also providing enough context around how they work so they make sense even if they aren’t something you’re familiar with yet.
Best online courses/tutorials
There are dozens of online learning platforms you can use to learn data science—Coursera, edX, and Udacity to name a few. That being said, it’s also possible to build your own (bad-ass) curriculum. For beginners, we recommend three core courses: How To Think Like A Computer Scientist and Preparing For A Career In Data by MIT OpenCourseWare (taught in Python), and The Nature Of Code by University of Virginia (taught in Processing). You should be able to work through these programs in around 10 hours apiece. By building your own curriculum based on what interests you most, you’ll make sure that data science stays fun and interesting. After all, remember that becoming good at something is as much about practice as it is about passion. If it doesn’t fascinate you, then eventually your interest will wane. We don’t want that! So just have fun with it…while keeping things laser-focused enough so you don’t lose sight of why you’re doing what you’re doing. Doing data science well is like solving a crossword puzzle…you need both breadth and depth in order to come up with solutions easily and quickly (and most importantly…correctly!). Happy coding!
Books to read
If you’re just starting out with data science, check out Python for Data Analysis. (Python is a popular language used in data science and machine learning.) Other books to consider include The Elements of Statistical Learning, which focuses on statistical modeling and analysis and Machine Learning: A Probabilistic Perspective, which is intended for advanced students of probability and statistics. Not ready to plunk down cash on a book? Download Google’s free TensorFlow tutorials or dive into your favorite MOOC (massive open online course). You can also find tons of great videos about machine learning on YouTube or courses on Coursera.
Career opportunities in data science
The U.S. Bureau of Labor Statistics expects that job opportunities for statisticians and data scientists will increase by 18% from 2016 to 2026, faster than average (7). These positions can include working with extremely large datasets and having a background in machine learning. The median salary for these positions is $110,000 (8). Therefore, learning how to do data science can be a rewarding career opportunity as long as you are willing to work hard. If you are thinking about going into business analytics then it would also help to learn statistics and probability, too!
Conclusion – So, let’s start!
So, you’ve mastered basic data science? Great! The next step is to start applying what you’ve learned. There are all sorts of applications out there, and data science can be applied across industries. If you have access to a dataset that isn’t overly sensitive, don’t be afraid to analyze it using a range of analytical tools and techniques. In fact, I encourage it!