Benjamin Obi Tayo, Ph.D. is a Physicist, Data Science Educator, and Writer, as well as the Owner of DataScienceHub. Previously, Benjamin was teaching Engineering and Physics at U. of Central Oklahoma, Grand Canyon U., and Pittsburgh State U.
Good planning and preparation will not only improve productivity, but it will help avoid potential pitfalls and roadblocks that could be encountered during project execution.
Even though data is now produced at an unprecedented amount, data must be collected, processed, transformed, and analyzed to harness its power. Read more about the 3 main stages involved.
This article discusses 2 levels of data science learning, and the amount of time that will need to go into each. From 6 months to 4 years, this write-up covers a number of skills and how long it takes to acquire them.
Surfing the professional career wave in data science is a hot prospect for many looking to get their start in the world. The digital revolution continues to create many exciting new opportunities. But, jumping in too fast without fully establishing your foundational skills can be detrimental to your success, as is suggested by this advice for data science newbies from Peter Norvig, the Director of Research at Google.
Understanding the most important features to use is crucial for developing a model that performs well. Knowing which features to consider requires experimentation, and proper visualization of your data can help clarify your initial selections. The scatter pairplot is a great place to start.
Linear algebra is foundational in data science and machine learning. Beginners starting out along their learning journey in data science--as well as established practitioners--must develop a strong familiarity with the essential concepts in linear algebra.
Maintaining proper organization of all your data science projects will increase your productivity, minimize errors, and increase your development efficiency. This tutorial will guide you through a framework on how to keep everything in order on your local machine and in the cloud.