Businesses have more data available to them at any point in history, but simply storing a lot of data doesn’t benefit a business. To reap the rewards of data, businesses need data scientists to help them turn it into insights that can be applied to decision-making.
Data science has never been more in demand. Skilled data scientists are a rare commodity, and companies are willing to pay a premium for their work.
Even if you don’t want to be a data scientist, you still could benefit from a basic understanding of the tools and techniques they use. After all, there’s almost no business that couldn’t benefit from a better understanding of the world, and that requires data.
So, how do you go about learning data science and data analysis?
First, you’re going to need some familiarity with a programming language. R and Python are the most popular languages for data science. Python is my personal preference, but that’s likely because I was familiar with Python before I became interested in data science. Python provides dozens of great tools and libraries for handling and analyzing data, including NumPy, SciPy, scikit-learn, PySpark, Pandas, and matplotlib, among many others. Jupyter Notebook — which works with R too — and iPython are powerful tools for interactive data analysis.
Learning Data Science
My aim here is to present a selection of the best data science learning resources, regardless of whether they are free or paid. I’ve tried to include as many free resources as possible, and I’ll indicate when a resource is available for free.
Data Science From Coursera
Coursera’s Data Science Specialization from Johns Hopkins University is an excellent — if not inexpensive — introduction to data science. The course is based on the R programming language, and consists of 10 individual modules that start by introducing learners to data science tools like R, RStudio, and GitHub, before diving deep into R programming, data cleaning, data analysis, statistics, and machine learning.
Data Science And Analytics In Context from edX
This course from edX and Colombia is a five week introduction to the fundamentals of data science. It covers the history of data science, statistics and probability, machine learning, and data visualization. You don’t have to take the whole bundle of courses, and are free to choose the areas that you are most interested in learning about.
edX also includes a couple of excellent free introductory courses from Microsoft that focus on introducing Python and R for data science. They’re a great place to start if you’re not already familiar with a language used in data science.
Finally, edX offers several other data science related courses tailored to practical applications, including Data Analysis For Your Business, which focuses on using Excel in a business setting.
Kaggle is most famous for its data science competitions, but before we get to that, you should check out Kaggle’s list of data science educational resources, which are an excellent complement to our list.
Kaggle’s data science competitions offer prizes for teams and individuals who produce the best answers for pre-defined data science projects using real world datasets. The competitions include guidance about the goal — what should be learned from the data — and provide large datasets drawn from businesses. Even if you aren’t an expert in data science, it’s well worth entering the competitions: they’re a great way to learn with the sort of datasets you’re likely to face when you work in the field
DataQuest is an online education platform dedicated to teaching data science. Learners can choose from a series of tracks, including those aimed at data scientists, data analysts, and data engineers.
DataQuest isn’t free, but their excellent blog has a lot of introductory data science information.
If you’re interested in entering the data science field, the resources we’ve discussed here are more than enough to get you started. Good luck and happy learning!