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-Scie…
Master basic statistics, descriptive statistics, and probability theory
Explore ML methods, including decision trees and decision forests
Learn probability distributions normal and Poisson distributions
Explore hypothesis testing, p-values, types I and II error handling
Master logistic regression, linear regression, and regression trees
Learn correlation, R-Square, RMSE, MAE, and coefficient of determination