Learning Data Science: Our Favorite Data Science Books - ByContrary to what some of data scientists may like to believe, we can never reduce the world to mere numbers and algorithms. When it comes down to it, decisions are made by humans, and being an effective data scientist means understanding both people and data. Consider the following real-life example:. When OPower, a software company, wanted to get people to use less energy , they provided customers with plenty of stats about their electricity usage and cost. However, the data alone were not enough to get people to change. In addition, OPower needed to take advantage of behavioral science, namely, studies showing people were driven to reduce energy when they received smiley emoticons on their bills showing how they compare to their neighbors!
Learning Data Science: Our Favorite Data Science Books
Whether you are just breaking into data science, or you are looking to improve your data science skills. Books are one great method to get a base level understanding of specific topics. In data science, there are many topics to cover, so we wanted to focused on several specific topics. This post will cover books on python, R programming, big data, SQL and just some generally good reads for data scientists. Heads Up!
Where do you start? Instead of trying to figure it out on your own, use this list of free data science textbooks. Bonus : Download a free summary sheet with all 12 textbooks to start learning immediately. This includes everything from the basics of Python and R , to advanced techniques in machine learning, data mining, and statistics. The best way to do that is by building small projects. Building projects is an effective strategy for the following two reasons:.
Why You Need To Read Data Science Books
I was hooked once I realized how much more powerful coding is than spreadsheets. So was the, not to subtle, message for engineers; if you want to get a well paying job, go into finance and become a quant, much like data science today. The concept of using math directly in business operations is intriguing, not just for decision support but to make real-time decisions. However, the financial crisis also laid bare the inadequacy of even the most sophisticated models to cope with the lion of chaos that is the real world. At the core of the financial crisis, many believe, is a Nobel winning differential equation; The Black-Scholes options pricing model. This model was used, without understanding its inherent limitations and implicit assumptions, to gauge risk for an enormous amount of investments.