The world relies increasingly on massive data sets—from YouTube algorithms that keep interesting video streaming to MRI analysis that identifies life-threatening cancer; it all requires data to function. The discipline that ties all of these different applications together is known as data science, and there are major opportunities for those with knowledge in this field, but do you need to spend years at a university to learn data science?
You can teach yourself data science, although it won’t be easy. Start with a broad understanding of the discipline and work your way through the different coding languages, data analysis, and machine learning tools. You can then become proficient in this cutting edge technological field.
In this article, we will take a look at data science as a discipline and outline why this is essential knowledge for anyone pursuing a career in technology. We will also examine some of the best tools and resources available to help you self-educate in this interesting field. If you have ever desired to learn data science to help expand your career options and don’t know where to start, I’m sure this article will give you insights into data science.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is Data Science?
As we mentioned, there are ever-growing pools of data that are used to organize, manage, and affect almost every aspect of modern life. These massive data sets need to be analyzed for trends, insights, and patterns, with modeling and testing being a key element in the process.
Therefore, a data scientist has to be someone who can see the patterns in data sets and use its curiosity, deductive reasoning, and technology to figure out underlying significance.
A Self-Taught Discipline
There is no doubt that having a university degree in certain fields can help you on your way to understanding data science. Computer Science and Applied Mathematics are two degrees that provide an excellent foundation that can get your foot in the door to a data science department.
However, many employers are more interested in your demonstrable knowledge of the field than a piece of paper. This means that with discipline, patient, and hard work, a career in data science is not outside the realm of possibilities on a self-taught trajectory. There is also an incredible amount of online courses and digital resources, making this a field that is surprisingly welcoming to independent study.
Learn Essential Skill Sets
Most experts in the field consider background in computer science and statistics being the two most valuable determiners of a successful comprehension of data science. This is due to the technological requirements of coding that are part of this discipline, along with the high-level mathematics that underpins the field.
It is not uncommon for a data scientist to have a Master’s in Applied Mathematics with a minor in Computer Science or vice versa.
Because these skill sets are at the heart of data science, they are the perfect place to begin on your self-guided education journey.
Refresh and Sharpen Your Mathematics Skills
Data science is often referred to as a synthesis of computer science and math, specifically statistics. Because of the way these two disciplines interrelate in the data science world, you will need a fairly high degree of mathematics competency to succeed in the field.
More than most disciplines, in math, each concurrent level of complexity is built upon the previous one, so you will need more than just a single course in statistics.
Linear Algebra
Basic algebra is a series of processes that are used to solve equations with unknown variables. This is the stuff you learned as a kid that provides the foundation for a lot of other branches of more advanced mathematics.
Linear algebra is a way to solve multiple unknown variables within a system, and it is used in all sorts of other fields. Fluid dynamics, atomic orbitals, and facial recognition technologies all rely on this advanced branch of mathematics.
There are several resources online that can be used to gain an understanding of linear algebra. However, if you have been out of school for a while and aren’t comfortable jumping into this advanced level, you may very well need to go back to more basic mathematics and work your way up.
Statistics
Statistics is the branch of mathematics that has to do with the collection, organization, and analysis of data. Given that we are discussing data science, it should be no surprise that this data-oriented subset of mathematics is important.
Once again, statistics is based on an assumption of prior knowledge of other branches of mathematics, like linear algebra, probability theory, and calculus. If you have little knowledge of these, you will likely need a refresher course if you stand any chance of continued development.
Take Coding Classes
Coding languages are the underlying script that allows for the functionality of many of the analytic tools used in data science, and becoming familiar with these languages is the next step in your self-education.
Learn Python and SQL With Codecademy
On the computer science side of things, you will need to learn how to code. Languages like R, C++, and Java are all used throughout data science; however, Python is the most common, making it an excellent place to start.
Coding is a complicated discipline, but a data scientist needs to be at least proficient in these common languages, so take the time, persevere, and work your way through some of these free online classes. Codecademy is a great resource that can help introduce you to programming with these popular languages.
On a side note, many new programmers often feel lost when they start learning their first language, so relax and try to absorb the key concepts. Many of these languages have similar functionality, just different syntax, so look for big picture concepts and be patient.
Learn C++ With Codecademy
While Python is considered the most common programming language used in data science, having at least a basic knowledge of C++ is good for anyone considering entering the field. This is because it remains one of the best languages for computing large data sets without a predetermined algorithm. It is also much faster for processing large (petabyte) data sets than other languages like Java or Python.
Once again, it could be possible to enter the data science industry without expertise in C++, but having a working knowledge of how C++ fits into the field is a good idea. For self-educators, there are valuable resources that can help you learn C++ as well.
Learn Java With Codecademy
One of the oldest languages, Java, still has a place in data science, and gaining some experience in this language is a good idea. Its age is one reason for Java’s usefulness: the fact that it has been around as long as it has means that you will undoubtedly come across it at some point in your data science education.
Many Big Data frameworks like Hadoop and Apache Spark were written using Java, so it is probably best that you take some time to become familiar with this coding language.
Learn R With Coursera
This is another programming language that can be useful in a variety of data science applications. One reason, it is a good idea to get some experience with R is the fact that it remains a common programming language despite the plethora of other options. There are certain functions where R is better suited than other languages as well as some R tools that simply don’t exist in other languages.
Becoming a well-rounded programmer is an essential part of pursuing an education in data science, and that means gaining some experience with R.
Learn Python for Data Science and Machine Learning Boot Camp
Once you get your head wrapped around some basics of these programming languages, the next step will be to learn about how these languages are used in data science. This online course is designed for people with a basic understanding of programming as well as for experienced developers that are looking to transition into data science.
This course will teach you how to use Machine Learning with Python, as well as how to produce data visualizations. It is an excellent, in-depth introduction to many of the concepts and processes that are essential to your understanding of data science.
Introduction to Machine Learning for Data Science
Machine learning is a growing part of data science and having a working knowledge of these technologies is essential if you plan on making a career in the field. Within data science, machine learning refers to a method of analysis that relies on automation for building analytical models.
Given the size and amount of data sets that are regularly used in data science, the emphasis on machine learning is understandable, and becoming familiar with these tools is a great way to progress on your data science education.
Make Use of Open Source Libraries and Resources
Once you have a bit of an understanding of some of these different programming languages–again, Python is a good first language–the only real way to get better is to write code. Luckily, several different open-source libraries allow users to contribute and learn at the same time.
This active learning method is particularly useful for gaining an understanding of machine learning tools, so here are some of the top open-source libraries with this technology as the focus.
TensorFlow
Developed by Google, TensorFlow is an open-source framework that allows you to build models, specifically neural network models. This allows you to take advantage of the prior work done by other programmers and data science professionals and help you understand a variety of different concepts through active learning.
It also uses Python, making it one of the more popular open-source libraries for machine learning algorithms.
Scikit Learn
Like TensorFlow, Scikit Learn is another valuable resource that will help you learn about machine learning algorithms by actively writing code. It also uses Python, so spending time working through the supervised and unsupervised learning algorithms is a great way to improve your knowledge of this common programming language.
While TensorFlow was developed to easily produce neural networks, Scikit Learn is a bit broader in its functionality. Regression, classification, clustering, modeling selection, and preprocessing are all available on this platform.
Keras
Keras is an Application Programming Interface (API) that uses Python that can be run on top of TensorFlow and some other platforms. Keras is designed for testing deep learning models, and neural networks, with its main advantage being speed. It can process large amounts of data and test complex models without sacrificing speed, thanks to its support for data-parallelism.
While it is by no means an industry standard, many data science professionals find it useful for a variety of functions and it is worth spending some time with Keras to understand deep learning models.
Pytorch
This is another open-source library with a wealth of tools designed for deep learning development. It is similar to TensorFlow, although many professionals and researchers prefer Pytorch for it’s on the go deployment of dynamic graphs, which can reduce the time between development and release of applications. It uses a C++ front end with a Python interface, which will allow you to practice some C++.
Theano
Like TensorFlow, Theano is another open-source library for Python that is designed for building and testing neural networks. It can function on both CPU and GPU and is often favored for its ability to optimize and evaluate multi-dimensional arrays. There is an ongoing debate over which is better, TensorFlow or Theano, but since both frameworks are commonly used by data science firms, it is a good idea to be familiar with both platforms.
DeepLearning4J
Unlike some of the other platforms we have listed, DeepLearning4J or DL4J is a Java-based library that has become more popular in commercial applications. Much of the machine learning and deep learning research is done using Python, so much of the cutting edge data science is focused around this language.
However, DL4J’s use in commercial and business settings means that for someone looking to make a career out of data science, having a bit of experience with this library is a good choice.
Learn Other Analytical Tools
Once you are comfortable with Python, SQL, and the other languages we examined, you can move on to learning about some of the other common tools utilized across the data science industry. These other tools are used to manage and analyze data, some being more useful than others.
However, for someone just entering the field, it is a good idea to have at least a general understanding of all of these tools.
Excel
At this point, most people are at least vaguely familiar with Excel, and it does have some applications in the field of data science. While this program is certainly not as useful or robust in terms of functionality as other tools, you will most likely be using Excel at some point, so being familiar with some of these functions is a good idea.
That being said, don’t devote too much time to Excel, as it is fairly limited in terms of what it can do in the day to day world of a data scientist.
MapReduce
One tool that is worth becoming familiar with is MapReduce. It is used for analyzing and processing massive data sets by using the combined processing power of multiple computers. Data that is stored at other locations are run through MapReduce, and certain parameters are designated by the user to reorganize the data into smaller, more manageable chunks.
A top-ranking response on Reddit summed it up quite succinctly:
A “map” is when you process each piece of data. “reduce” is when you combine it back up into groups, which you do by telling the computers “anything that looks like group A should be sent to computer 1, anything that looks like group B should be sent to computer 2”, and so on.
When dealing with the endless stream of data that is typical of the field, it is easy to see why a tool like this would be so useful.
Hadoop
Hadoop is a management program for a MapReduce cluster of computers. Instead of having to break up the data and copying it to a series of computers and managing the results as they are completed, you write Java code describing the desired actions, and Hadoop takes care of the rest. Once again, when considering the amount of data that is constantly being produced, the ability to code functions like this becomes instantly apparent.
The prevalence of Hadoop is another incentive for learning codes besides Python.
Apache Spark
Building on the layered framework of Hadoop managing your data on a cluster of computers, Apache Spark–usually referred just as Spark–is a framework for managing datasets. While you don’t necessarily need to run Spark on a Hadoop cluster, there are benefits of this method, and the built-in libraries for machine learning and other analytics tools available on Spark make it an invaluable platform to understand.
Spark is also better adapted for smaller data sets and can perform more complex functions, often much faster than Hadoop. Another reason that Spark is gaining popularity in data science circles is that it is partially language agnostic, meaning that you can use Python, Java, or SQL when writing code.
RapidMiner
This is a data science platform that allows you to perform several functions like data mining, machine learning, and data visualizations. The general opinion in data science circles is that RapidMiner’s main appeal is that it doesn’t require programming, but it may be unfit for larger and more complex datasets. However, having a general understanding of the platform isn’t a bad idea.
Knime
This is a data analysis tool developed at the University of Konstanz that some people feel is useful in the world of data science. Like RapidMiner, Knime is a tool whose largest appeal is that it doesn’t require knowledge of programming languages or experience coding.
While we know that it is essential to be proficient in several programming languages to succeed in data science, Knime, and its built-in algorithms and data visualization capabilities make it useful in certain situations. Once again, any familiarity with this tool is positive when entering the field.
Splunk
Splunk is an analytical tool that is used to examine large data sets, specifically log files, and certain data can be prioritized, pulled from different files, and visualized using graphs in real-time. It is mostly used in IT departments as a way to manage the massive amounts of data that are generated by dozens of servers and is a useful tool for identifying errors and anomalies.
Splunk may have a more specific functionality than some of the other analytic tools we have looked at, but it is an interesting tool worth examining.
Improve Your Communication
This is an often-overlooked aspect of a career in data science that many employers consider essential. As a data scientist, you will be part of a team that not only has to interpret massive data sets, build models, test for functionality, but also tell a story.
All of the mathematical knowledge and programming skills are incomplete if you cannot produce a clear and concise explanation for bosses, many of whom will not be versed in the jargon or intricacies of data science. That’s why it is important to work on your communication skills and your ability to translate complex patterns and trends into useful language that demonstrates the problem and how you have solved it.
Unlike math or coding, this is a difficult characteristic to improve for many people since it is not as simple as x + y = z. However, if you feel that you lack in the communication department, there are some resources you can use to try to improve your interpersonal and professional communication skills.
Further Reading: The 6 Best Resources to Learn Data Science
Several great books have been published on data science, as well as its growing role in business. These are some good texts that can help you and provide insights for your path to learning data science.
Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking
Written by Foster Provost and Tom Fawcett, this book has become a must-read for anyone diving into the world of data science as a professional. It is particularly helpful in explaining the reasoning behind using particular models and why they work.
Importantly, it focuses on training you to think analytically, which is a key element in professional settings that is not covered when learning the nuts and bolts of the mathematics and coding languages that allow for the functionality of data science.
Confident Data Skills: Master the Fundamentals of Working With Data and Supercharge Your Career
This is another book that will help prepare you for a business environment that utilizes your analytical data science skills. Written by Kirill Eremenko, Confident Data Skills provides examples and case studies of the role of data science in a variety of commercial settings, including Netflix, Linked-In, and Goodreads.
It also gives solid advice on how to deal with real-world business scenarios, like formulating questions, cleaning data, and communicating the process and results to other team members.
Superforecasting: The Art and Science of Prediction
Not specifically focused on data science, this book–written by Phillip E. Tetlock and Dan Gardner–instead focuses on the way humans make decisions. As a data scientist, you will be analyzing data with a specific goal in mind, and deciding the correct course of action is an essential part of the job.
This book shines a light on how the decision-making process is based on a variety of factors and how understanding your prediction methods is important for improving them. Part psychology, part statistics, this is a great book that every data scientist should read.
Applied Predictive Modeling
This book serves as a great introduction to predictive models and how they can be applied. Max Khun and Kjell Johnson do a great job of providing intuitive explanations to these techniques as well as using real data for examples in problem-solving. The math that you have been studying will come in handy during their discussion of statistical principles.
Hands-on Machine Learning With Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
In a more functional direction, Aurelien Geron’s book provides concrete examples of the different ways to build intelligent systems that can be directly applied to existing platforms. It has been referred to as “the Bible” by some in the data science field, and the real-world examples and insights it provides make it an invaluable resource.
Machine Learning Yearning
Another functional read, this book was written by a living legend in the data science world, Andrew Ng. The author of several excellent online courses, a well-respected programmer and adjunct professor at Stanford, Ng outlines several scenarios that you will undoubtedly run into when working on machine learning and provides valuable insights that everyone interested in data science should have.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Data science is a new and exciting field with a growing value that is continually emerging. Learning data science on your own can be a difficult but worthwhile endeavor, and the path and resources we have outlined here provide a framework for anyone to start their journey to an understanding of the field.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.
