As the decade comes to a close, one of the most dynamic relationships we’ve seen evolve throughout the years is the population’s interaction with the world of big data. With programs like Apache Spark and Hadoop paving the way for data driven decision making for finance and business giants, deep learning and data mining once seemed valuable only to the likes of a data scientist.
The predictive powers of data, however, have revolutionized the way we carry out even mundane, daily tasks. From the algorithms involved in our YouTube suggestion bar to the data visualizations that turn unstructured data into digestible information about the economy, the amounts of data we use and create each day is immeasurable and has become an integral part of our lives.
While the job of data analysts has become much more democratized in the 2010s, thanks in part to the mini computers the majority of the population carry in their pockets, many governments around the world have been waking up to the importance of user data. Even China, who has been using its citizens’ data for public services for years now, has unveiled a new law requiring each person with a sim card to allow their device to send facial recognition data to the government.
Gone are the days raw data was used simply for business intelligence - data will continue to play an even more ubiquitous role in all aspects of life. Whether you’re interested in learning about predictive analytics because you want to make your career in advanced analytics or you simply want to become a more informed, data-creating individual, there are many ways to start diving into the world of bit data analytics. This guide will walk you through the main branches of data analysis and where you can learn more about the subject.
What is Data Analysis
If you’re thinking about learning new skills in business analytics, data management and data visualization, the best way to jump-start your journey into the world of data analysis is to understand the origins of the field and its various specializations.
The aspect of data analysis that trips most people during their first look into the subject is the difference between data analysis and data science. While both fields focus on turning unstructured, complex data into valuable information, there are many key differences between the two fields.
A data analyst will have a mathematical statistics background and can often come from the fields of mathematical statistics, business, biology and more. The core skills involved in this profession are data processing, learning models, creating predictive models from a variety of different data sources. The most common technical skills you’ll need to master generally involve work in Excel.
A data scientist on the other hand is expected to have a background in computer science, along with other relevant statistics fields. They can have a specialization in any different subjects, such as machine learning, artificial intelligence, software development and more. This type of profession often requires knowledge of many different programming languages, such as R programming.
A data science job can be very similar to one in data analysis, as many employers still aren’t very aware of the subtle differences between the two fields. This is definitely fair, as there is much cross-over between them and is something you should be aware of as you search for programs and professional experiences in the field.
Another stark difference between the two subjects is the fact that the history of data analysis goes back centuries while data science has been made possible thanks to the technological innovations of the 21st century. Both fields involve the investigation of large amounts of data in order to find valuable patterns, so if you love data you should explore your options within both broad disciplines.
Where to Learn Data Analytics Courses
Searching for a data wrangling bootcamp or fundamentals of statistics course but don’t have the room in your budget for a major price tag? One of the best places to start learning algorithms or the basics of exploratory data analysis is by taking online courses.
The first step towards taking an online data analysis or data science course is to determine which analysis or data science skills you’d like to learn or improve. While this can be overwhelming at first, it’s easier if you start by identifying the fields you are most interested in. If you’re interested in business, for example, you can improve your analytic skills by solving business problems with data from an online course.
The next step you should take is to understand what your preferred learning style is. Because there is an endless number of data analysis resources online, you can choose from:
- Online courses
- Video tutorials
- Private tutoring
It is also important to understand that there are many different data products on the market for statisticians and that you won’t need or be able to learn all of them. These involve software and programs like Hadoop, R, Python, Julia, SPSS, Stata and more.
If you’re interested in learning a specific programming language with applications in statistics, there are plenty of tutorials and courses online for you to take. One great example is Code Academy’s Python 3 course, which will give you an introduction to basic syntax and functions. In contrast to Code Academy, Google has a Python course that you can access free of charge.
Determine what you want to accomplish by the end of the course, whether that be knowing how to analyse all data types or simply to gather more experience for your Capstone course. If you want to know more about learning statistics online, check out this guide for an exhaustive list of organizations, companies and individuals who offer data analysis courses.
Learn Analytics in University
For those interested in a data science career or a job in data analytics, getting a bachelor or master of science as a statistician can be a wonderful thing to pursue. Whether you’re interested in certification training, a non-technical degree, data engineering or more, there are many different pathways you can choose from.
Before looking into specific programs, get to know what kind of learning style you would like. You can choose everything from unsupervised learning in online courses or an online master to an intensive master involving new data and a capstone project. If you’re the type of person that benefits from the learning techniques involved in a traditional four-year bachelor or one year master’s program, the UK has some of the world’s best universities for statistics.
There are three infamous ranking systems that classify the world’s universities: the Shanghai, Times Higher Education and QS university rankings. Starting with these rankings can give you a clearer idea of what kinds of universities teach statistics programs as well as what they’re most known for. Each of the three rankings allows you to search for the best universities by subject, although only two have a field specifically for statistics. Some of the best universities in the UK for statistics include the University of Cambridge and University of Warwick.
These rankings all score universities using different indicators and weighting systems. While not widely understood, these rankings should only be meant for preliminary research. You should make sure to do your research into each course these universities offer as well as compare them to programs offered by other universities that perhaps don’t figure in these rankings.
Some of the common programs you’ll find related to statistics are:
- Artificial intelligence
By no means an exhaustive list, these fields and subjects can give you an idea of the possible degree programs you’ll find in the UK relating to statistics. If you want to learn more about statistics degrees in the UK, make sure to check out our guide!
If you’ve already conducted your research into a particular university and program, don’t hesitate to apply now!
Resources for Data Scientists
From google cloud and programming skills bootcamps to advice on NoSQL databases, the internet is full of help for statisticians. Whether you’re having trouble with your unsupervised data cleaning or need to analyse data on a particularly difficult data set, here are some of the top resources for analysts.
- Learn what is data science and statistics with the book An Introduction to Statistical Learning
- Try out Stanford’s 10-week course on machine learning in R
- Get tutorials in R with R-bloggers