Statistics and Mathematics for Data Science and Data Analytics: Explore statistics and probability to extensively understand data science and business analysis!

If you aim for a career in data science or data analytics, this course will equip you with the practical knowledge needed to master basic statistics. You need good statistics and probability theory knowledge to become a data scientist or analyst. The course begins with an introduction to descriptive statistics and explains the basics, including the mean, median, mode, and skewness. You will then learn more about ranges, interquartile range (IQR), samples and populations, variance, and standard deviation. The following section will explain distributions in detail, including normal distribution and Z-scores. Then, you will explore probability in detail, go over the Bayes theorem, the Central Limit theorem, the law of large numbers, and finally, Poisson’s distribution. Next, you will comprehensively explore linear regression and the coefficients of regression, mean square error, mean absolute error, and root mean square error. You will also explore hypothesis testing and type I and II errors in more detail and then learn comprehensively about the analysis of variance (ANOVA). After completing this course, you will comprehensively acquire knowledge about statistical fundamentals, data analysis methods, decision-making processes, and machine learning concepts with examples. All resources are available at: https://github.com/PacktPublishing/Statistics-Mathematics-for-Data-Science-Data-Analytics

This beginner-level course has been niched to cater to an individual looking to master statistics and probability for data science and analysis, an individual looking to pursue a career in data science, or professionals and students wanting to understand statistics for data analysis. The prerequisites for this course include absolutely no previous experience required and an eagerness and motivation to learn.