Most people focus on increasing income, rather than minimizing expenses — With the right strategy, everyone can build wealth over time irrespective of their income. The math is pretty simple, if you keep your expenses well below your income, you will have extra money each month to put into a savings or investment account. When it comes to wealth building, most…

Learn the basics of correlation and its use in data science and machine learning — After reading this article, the reader will learn the following: Definition of correlation Positive Correlation Negative Correlation Uncorrelation Mathematical Definition of Correlation Python Implementation of Correlation Coefficient Covariance Matrix Python Implementation of Covariance Matrix To find out more about the use of vectors and matrices in data science and machine…

Basic matrix concepts used in data science and machine learning — After going through this article, the reader should learn the following: Definition of Matrices Matrix multiplication Apply matrix multiplication to linear regression Inverse of a matrix Eigenvalues Eigenvectors To learn more about the use of vectors in data science and machine learning, see the article below: Linear Algebra for Data Science — Vectors Basic vector concepts used in data science and machine learningbenjaminobi.medium.com

Basic vector concepts used in data science and machine learning — After going through this article, the reader should learn the following: Definition of Vectors Perform addition and subtraction of vectors Perform scalar multiplication of vectors Use scalar multiplication for feature engineering Define dot product of vectors Use dot products to calculate correlation coefficient To learn more about the use of…

4 steps for managing a data science project — Key takeaways Executing a data science project requires good planning 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 Benjamin Franklin once said: “By failing to prepare, you are preparing to fail.” This article will discuss…

3 important reasons why teamwork is so crucial in real-world data science projects — Key Takeaways Without teamwork, real-world data science problems would be impossible to solve. Academic training programs should therefore design capstone projects that would help students to collaborate and build their teamwork skills. Andrew Carnegie once said: “ Teamwork is the ability to work together toward a common vision. The ability to direct…

Effective communication will enable you to convey your ideas clearly during virtual meetings — Key Takeaways With remote work becoming the norm, communication skills are crucial in data science As a data scientist, you might have a great portfolio of technical skills, but if you can’t communicate effectively, you won’t be able to convey your ideas clearly during virtual meetings. It is therefore crucial that strong…

Tips on how to collect raw data — Key Takeaways Designing experiments for data collection is important when the data required for analysis isn’t available. The key goal is to design a way to collect the best subset of data quickly and efficiently. Data plays a central role in data science and machine learning. Most often, we assume that the…

Dive deep into core data science concepts using these resources — Key takeaways In today’s modern world of information technology, there are unlimited online resources for learning data science. Most of the data science resources provide superficial knowledge only. To expand your knowledge beyond online courses, you must put in extra effort to gain in-depth knowledge of core data science concepts.