Let’s face it: while learning data science sounds like an exciting proposition for data nerds the world over, the field still remains as alienating as it was at its inception. The word data science itself calls forth images of computing and solving complex algorithms or mind-numbing lectures. Quite frankly, we are living an interesting paradox, where data science is at once considered an exciting but unapproachable subject, due to its association with mathematics and computer science.
The Advantages of Learning Data Science
At the heart of the matter, however, is the simple fact that data science now permeates every facet of our daily lives. The information gained form either learning more about the subject, or deciding to take up a career in it, can improve your grasp on what’s new in current affairs, technology, medicine, et cetera.
At the base of data science, however, is a discipline that we’ve all become experts in – statistics. Analytical and critical thinking are skills that are required of us throughout our lives, and while statistics might be something that initially sounds complicated, it’s actually quite simple. Statistics is the recording of information today to make decisions tomorrow – and this act is something we repeat everyday, multiple times a day.
Some of the benefits of learning or mastering data science include:
- Job security
- Improving your business
- Innovative collaboration
- Understanding fiscal policies
While this list is definitely not exhaustive, it serves to underscore the importance of data science both as a profession and the role it plays in our daily lives.
The Ways You Use Data Science Everyday
Learning more about the way statistics has evolved as a discipline and how its used today can greatly improve your life. While this can sound dubious at first, it’s an easier fact to accept once you interpret some facts for yourself.
Statistics is a profession that effects our daily lives, from providing us with digestible financial data to helping get new medical drugs on the market. Statistics can also help you improve yourself on an individual level, providing the foundation for many health, fitness and financial apps.
Today, statistics is used to collect passenger information in metros and train services to help improve your daily commutes, in universities to identify weak points in their scholarship systems, and grocery stores to ensure your favourite cereal is always in stock. The list can go on, but these main examples serve to highlight our dependency on the collection, storage and analysis of what is, in its core, information about ourselves.
Some of the skills you can gain from learning about or perfecting the art of statistical analysis are:
- Improving business practices
- Making sounder financial decisions
- Forecasting trends in your economy
- Managing your own business
- Communication and analytical skills
Are Data Science and Data Analysis That Different?
Now that we’ve covered why taking a data science course and statistics course is important to all of us, we have no excuse to go ahead and do so! Getting started can be frustrating – the amount of search results when typing in data science can be enough to intimidate even the most seasoned data scientist. However, the first part is understanding that data science and statistics are, while linked, separate disciplines.
While this distinction might just seem like another attempt to deter you from entering the world of data, it can actually bring you more insight. To understand the difference between what data scientists and statisticians is a good way to start preparing you for your journey into data.
Statistics is the mother of all subjects that are making headlines today: AI, data mining, machine learning, data engineering, and, of course, data science. Mentioned already is what statistics is, what that leaves room for is how it penetrates today’s schools and job markets. There are three main titles one can take on with a degree in statistics or a statistics-adjacent subject:
- Mathematical statistician
- Data analyst
Whereas mathematical statisticians tend to stick to jobs in academia – namely, teaching or researching – statisticians and data analysts dominate professions in banks, government, and businesses. In other words, we’re everywhere!
Data science, on the other hand, is the offspring of both statistics and computer science. That is to say, data science is like a statistician that is also deeply interested and a master in computer programming and engineering. Depending on what your title is as a data scientist, the degree to which you are a skilled statistician or not will vary. A general rule that seems to hold is that the more involved a data scientist is with the production of statistical software, the more they have to know about both statistics and computer science. Someone heading an IT department will, on the other hand, need to know less about statistics. The main titles data scientists take on are:
- Data engineer
- Data scientist
- Computer scientist and engineer
Studying Data Science Online
Classroom settings aren’t for everyone, and this applies whether you’re exploring extra help on data science or looking to pursue a degree in data science. Thankfully, the web provides a number of interactive resources, from global institutes to online-only webinars, that help you in data science, applied statistics, and other interdisciplinary branches of data science.
From data processing to predictive modelling, data analysis can be tricky. If you’re looking for a regular lecture setting, the best option for you is to find an a university that hosts lectures online on a large scale. While there is a vast array of universities that do this, if you’re looking to get a degree in data science and not just extra help, look for an accredited university that will engage your interests. Studying data online can have a competitive advantage, as many people who do this choose to also become part of the workforce at the same time, gaining skill in the field while studying it.
Applying to these types of lectures should normally involve both paying a tuition and also the ability to receive a graduate certificate at the completion of your online program. Another advantage of studying under this framework means you will also get access to a vast network of alumni that can either help you find a job after graduating, or offer you advice about the job market. If you prefer to be taught at your own pace, and would like to tailor your program to you, taking a statistics course online is something to research further.
If you’re not looking to receive a bachelors or masters in data science and simply want some extra help, webinars are an excellent example of how to use online courses to your advantage. Admission into these webinars depends only on whether they are free or require payment. Webinars span everything from exploratory analysis, statistical methods, analyzing data, r programming and data science strategies. The diversity in the subjects taught online can mean that you have access to advising in an area by qualified instructors or people who are also students.
If you would like to attend a webinar, the first things to evaluate are whether or not you are able to pay, what type of help you need, and what kind of qualifications you would like your instructor to have. If you simply need some online resources that provide raw data, explanations on data analysis tools and analytics tools like SPSS, R and Hadoop – stick to data science blogs and websites.
Studying Data Science in the UK and Around the World
If you’re just starting to apply to data science and engineering programs, or yearning to spend a semester abroad, the good news is that data science programs can be found anywhere in the world. Deciding where to study can be difficult, as programs differ in global recognition, types of bachelors and masters they provide, and courses offered.
If you’re looking for a four-year program in the UK, Europe, or everywhere in the world, your options are unlimited depending on what you would like to obtain as a specialization. Different institutions offer different degrees of expertise in data science, AI, computer science and engineering, and business intelligence. The first problem you would need to solve on your applications and admissions journey is to decide what types of data you would like to be working with, and what kinds of quantitative analysis you would like to perform. This will help you decide best what type of program in data science you would like to be part of.
The second step is to decide where you would like to study. If you’re a student who needs a scholarship in order to start studying, this can be the deciding factor when it comes to choosing a program. If you would like to become a researcher or attain a PhD, many schools will offer fellowship programs at the graduate level that provide you with both a scholarship and a monthly or yearly stipend for your studies. If that doesn’t sound like the right path for you, it is important to create an outline of the types of scholarships the programs you’re looking at are offering, what type of limitations they have, and of course the deadlines.
Studying data science can be complex, but that doesn’t mean it can’t be fun. Many universities offer opportunities to complete an internship through a partnership with established businesses, insight into how to get hired in the industry, or even opportunities to study abroad!