You don’t have to spend a fortune and study for years to start working with big data, analytics, and artificial intelligence. Demand for “armchair data scientists” – those without formal qualifications in the subject but with the skills and knowledge to analyze data in their everyday work, is predicted to outstrip demand for traditionally qualified data scientists in the coming years.
This means that practically anyone can upgrade their employability and career prospects by learning the fundamental theory and practical skills needed for data science. And luckily, there’s a tonne of resources online to help you do just that.
Here’s my overview of some of the best. Some of these might require payment at the end of the course if you want official certification or accreditation of completing the course, but the learning material is freely available to anyone who wants to level up their data knowledge and skills.
Data Science Crash Course, John Hopkins University (Coursera)
Designed to give a “fluff-free” overview of what data science is, how it works, and what it can be used to do. This course offers an introduction to the technical side of data science but is particularly aimed at understanding the “big picture” for those who need to manage data scientists or data science work.
It’s a relatively short course consisting of just one module that can be completed in under a week and serves as a great introduction for those who want to learn the terminology and understand how to build a data science strategy, without necessarily needing detailed instructions on using the technical tools involved.
Introduction to Data Science (Revised) – Alison
A completely free course that breaks down the core topics of the data science process and an introduction to machine learning into three modules, each designed to take around three hours to complete, and concluding with an assessment. Once you’ve worked through that, you can choose from several other similarly bite-sized tutorials covering data programming languages, visualization tools, and techniques such as building clustering and regression models.
Data Science and Machine Learning Essentials – Microsoft (EdX)
This course, aimed at those wanting to improve their career prospects with a mix of practical and theoretical knowledge, walks you through core concepts and terminology, statistical techniques such as regression, clustering, and classification, and the practical steps needed to build and evaluate models.
As it is a Microsoft course, its cloud-based components focus on the company’s Azure framework, but the concepts that are taught are equally applicable in organizations that are tied to competing cloud frameworks such as AWS. It assumes a basic understanding of R or Python, the two most frequently used programming languages in data science, so it may be useful to look at one of the courses covering those that are mentioned below, first.
Learn Data Science – Dataquest
Although primarily a paid-for platform offering proprietary content, Dataquest offers a number of free introductory modules to anyone who signs up, covering essential topics such as working with data, visualizing data, data mining and constructing algorithms in Python and R. If you want the full, ad-free experience and certification there are monthly subscription options, but there’s more than enough information to get started free of charge.
Data Science – Harvard
All of the class materials and lectures for Harvard’s data science course are made freely available online, so they can be studied at your own pace. You may not end up with a degree from one of the world’s most prestigious universities, but the course is detailed and technical enough to make an expert of you by the end. The course is part of a data science degree and constructed for students who have prior knowledge of, or are also studying, core fields such as programming, maths, and statistics. However, there are enough free resources out there on those subjects to make this a viable option for those outside of academia, if you are dedicated enough.
Introduction to Data Science in Python – University of Michigan (Coursera)
Those wanting to get their hands dirty with some actual coding will soon find out that Python is one of the most commonly used programming languages in the field, and for good reason. It’s relatively simple to learn the basics and can be combined with a number of free, open-source libraries to perform hugely powerful data science operations.
This course serves as a first step along the road, introducing Python functions that are used to prepare and manipulate big datasets as well as the proven techniques for extracting insights from data. It is intended to be completed by spending between three and six hours per week studying or working on exercises, over four weeks.
Learn Data Science with R – Ram Reddy (Coursera)
This course led by an established expert in R and data analytics is the first in an in-depth, ten-part tutorial on expert R programming, but also stands on its own as an introduction to the language and a primer on the basics as they relate to data science.
Like Python, R is a totally free and open-source language and environment that has become an accepted standard among data scientists due to its power and flexibility.
This course consists of 10 lectures delivered across eight hours of video, and is completely free to follow.
Introduction to Data Science Using Python – Rakesh Gopalakrishnan (Udemy)
This is one of the most highly-rated of Udemy’s introductory courses on the subjects of data science and coding in Python. It does not require any previous knowledge or experience as it starts right from the basics. However, unlike some other very entry-level courses, it does progress to some actual practical instruction in Python and, particularly usefully, its Sci-Kit Learn framework, a very popular tool for academic and enterprise-level data exploration and mining.
I Heart Stats: Learning to Love Statistics – University of Notre Dame (EdX)
Along with maths and computer science, statistics is one of the fundamental academic disciplines invoked by those working on projects involving data science and analytics. If you are completely new to the subject, this course offers a non-technical grounding covering basic and some advanced principles and techniques that will certainly help anyone trying to get their head around the wider field of data science.
If you want to truly understand data science then at some point you are going to come up against the field of statistics and probability, which can certainly be baffling for newcomers, particularly if your formal education days ended some time ago and what you did learn about the subject at school is a dim memory. This course explains how the statistical approach is used to make sense of the information that’s everywhere in the world around us.
You might also be interested in Bernard Marr’s latest book: Artificial Intelligence in practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems
And his video outlining the 7 biggest technology trends of 2020: