It's an understatement to say that the internet is packed with a mind-boggling amount of data. From predictive modeling to content personalization, companies are investing significant time and budget in quantitative strategies. That's because data directly impacts the choices we make about business.
Data and analytics are foundational to helping companies of all sizes run as efficiently as possible. Entrepreneurs should take a lesson from the Fortune 500 to create the right analytical infrastructure for optimizing initiatives. It starts with brushing up on your stats.
Regardless of whether you specialized in math, business, or English in college, these 5 resources will get you the data skills you need!
1. Udacity's Introduction to Statistics Class: This open source class will teach you the fundamentals of statistical analysis including data interpretation, visualization, probability, and regression. The syllabus includes 7 short lessons. Watch them all, or focus on the topics that you may not remember from high school or college. This course is ideal for a range of audiences – it only requires a basic understanding of algebra.
2. Statistics One: This open source class is taught by Princeton University's Andrew Conway and is ideal for learners who want an in-depth, structured class. There are 24 lectures and 12 labs to help students develop hands-on experience with t-tests, ANOVA, and binary logistic regression. These topics will help you understand the foundations of data analysis online.
3. Learn to Program: the Fundamentals: Data analysis and computer science go hand-in-hand. To effectively analyze your numbers, you need working knowledge of at least one programming language. Python is ideal for beginners because it's powerful, logical, and pretty straightforward. This free class is taught by the University of Toronto's Jennifer Campbell and Paul Gries and is designed for learners who have never programmed before.
4. Machine Learning: Stanford Professor Andrew Ng has developed a free course to teach concepts in machine learning, the science of working with computers to independently process information. This technique has been critical to the field of big data. This course is relatively advanced and provides an introduction to data mining, machine learning, and statistical pattern recognition. A programming and statistics background will be helpful for following this course.
5. The Chicago Guide to Writing about Numbers: If you're conducting data analysis, you will probably need to write about your insights and results. The process of writing about data is a discipline where art meets science. To convey maximum impact from your analysis, follow the best practices outlined in this book. It's a quick read and is available for under $20.
Don't Be Afraid to Take It a Step Further
As you develop your new skills, look to apply them immediately. Data analysis requires quite a bit of 'learning on the fly.' If you run into a roadblock, do a quick Google search to see what solutions the quantitative community has found. And most importantly, have fun!
Have you taught yourself any data skills? Know of any easy ways to take advantage of data? Please share!