The world of the data analysts and scientists involves turning unstructured data into valuable data visualizations and predictive information with techniques such as data mining, deep learning and modelling. This type of work is not only important to many different disciplines, but it's applications in other fields are required in today's modern landscape in order to produce valuable information about the future. From predicting weather patterns to creating new pharmaceuticals, data analysis penetrates many different subjects and professions.
Whether you already have a data science career or you're just starting on your journey to learn more data science skills, mastering the skills involved in turning large amounts of data into a creative data visualization or insightful report can be difficult. This is in part due to the fact that data management and predictive analytics are involved in more than just the private sector. Raw data such as your cell phone’s location data, can be used by governments and companies alike to produce information valuable to their policies, laws, businesses.
The internet can be your greatest tool when it comes to refining your data analyst skills and learning more advanced analytics skills. This guide is complete with a list of the most important resources for statistics at your disposal. Starting with some of the basics of statistics, you'll be able to also learn where to take data science courses either online or at university.
The History of Analysis and Machine Learning
For statisticians, the world is made up of unstructured data. Everything you do on a daily basis has the potential to be quantified for business intelligence, informing government policy, and used to create innovative products, medicines and ideas.
While many people tend to think of big data and the programs that analyse it, such as Apache Spark, when they think of modern data analysis, the history of data and data processing has much more ancient and humble roots. From farmers in some of the world's earliest civilizations gathering data on rainfall and harvests to Florence Nightingale’s revolutionary visualizations of mortality data - the field of statistics has evolved tremendously.
Today, machine learning is being used worldwide in a number of different areas. This includes scientists using machine learning to determine the population of elephants with audio recordings to estimating crop growth with satellite imagery.
Resources for Data Driven People
It has been said that the internet is the best tool for those who seek to learn. Becoming a master in subjects like data engineering, non-technical analysis and programming skills is no different. If you thrive in an unsupervised learning environment, are looking for an online master, or simply want to practice data cleaning and wrangling, the web is the best place to turn to for some help.
From google cloud learning techniques to unsupervised programming tutorials, here are some of the best tools and resources you can use if you’re interested in learning data analysis and data science skills.
Learn more about becoming a data scientist with our guide!
Online Data Science Courses
Whether you’re searching for ideas for your capstone project, need data sources in order to aid in learning models, or simply want to get an informal specialization in more programming languages, online courses can be a great place to start.
While most of the free online courses here are taught in R programming language, there are plenty of other free courses online that can be used in other languages such as Python, Stata and more. One great course you can look into if you’re interested in an informal, online Master of Science is The Open Source Data Science Masters.
With the ability to be viewed in GitHub, downloaded as a zip file and outlining the powerful skills learning data science can give you, this online course has drafted its syllabus based off many free online data science websites.
If you’re interested in more statistics focused courses, here are more that you’ll be able to benefit from:
- Coursera's Data Science Specialization: 9-course set, taught in R
- Springboard's Introduction to Data Science: 3-month program, taught in R
- Stanford's Statistical Learning: 10-week course specializing in machine learning, taught in R
While capstone projects analysing new data or a Master of Science with certification training can be extremely important and exciting, data processing and analysis can get confusing and overwhelming.
Luckily, there are plenty of blogs online that deal with many of the common obstacles one can encounter while exploring the world of complex data or big data analytics. Whether you’re a seasoned statistician or in your first year of an undergraduate degree, blogs can be a great resource for those looking for advice from people who’ve been stuck in your position before.
Blogs can also be a great source of entertainment. If you have a passion for data, you can also benefit from keeping yourself up to date with the innovations and complexities arising from data analysis and software. One example of this is Towards Data Science, which is a blog dedicated towards helping people understand the latest in all things data science. Here are some more blogs you should check out:
- FiveThirtyEight: Data-journalism
- R-bloggers: News and tutorials related to R
- No Free Hunch: The data science and competition blog for Kaggle
- Data Science 101: Many interesting posts on the world of data
If you want to improve your technical skills in particular data products that can process all data types, like Python, the internet is a marvellous place to start. Whether you want to analyse data to solve business problems or conduct exploratory analysis on large amounts of data, Python is one of the best languages to have in your arsenal.
Here are some of the best online courses for Python online:
- Codecademy's Python 3 course
- Google's Python Class
- Python for Everybody or PY4E
From using predictive algorithms on big data with software like Hadoop to using data analytics to transform a small business, there are many applications of statistics you can use in a vast array of domains. This is why understanding what to do with data sets doesn’t necessarily have to all into the job of a data scientist. Regardless of why you want to learn statistics, however, everybody has to start somewhere.
If you learn better through self-teaching methods, you can try learning through free online textbooks. Here are some that can help you gain a foundation in statistical methods:
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman
More Online Resources for Statisticians
Accessing data science and statistics resources can be a great way to reinforce your knowledge, whether you're still a student or already have a data science job. Learning algorithms and applying analytic skills are hard! Here are some more resources to help you through the rough patches:
- Elite Data Science's list of free data science resources for beginners
- Help with programming: Stack Overflow
- Help with code and concepts: Stack Exchange's Cross Validated
Where to Take Analytics Courses
If you're interested in taking data science and data analysis bootcamps or courses online, there are plenty of options out there for all kinds of goals and budgets. If you're interested in taking courses, start by determining what you'd like to focus on, which can be:
- learning a programming language
- how to use a database management language such as SQL
- understand the basics of statistical maths
- learn what is data mining and its applications
Read our complete guide on learning data analysis skills with online courses for more!
Data Management and Analytics at University
Interested in attaining a degree in data science but aren't exactly sure where to start applying? The best place to start is by determining what fields you might be interested in. Keep in mind that statistics and data science are broad fields, not often taught in an interdisciplinary manner. The common programs you will come across will likely be:
- Statistical mathematics
- Mathematical statistics
- Computer science and engineering
- Data science
- Artificial intelligence
- Machine learning
- Business Analytics
These are some of the more popular fields but not at all a complete list of all the applications of statistics. Whether you're interested in a NoSQL bootcamp or want an academic degree in a particular specialization, don't hesitate to apply now as soon as you find a program you're passionate about.
For a guide to statistics programs in the UK, check out our guide!