Published on: 2024-07-18 23:10:32
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Complete Data Science using R Programming course. This comprehensive course teaches you data science using the R programming language. The course is divided into 4 main sections and each section is divided into 6 to 8 sub-sections that cover each topic thoroughly with practical exercises. My aim in this course is to explain the theoretical concepts in such a way that even a beginner can understand them. Learning is then reinforced with at least one practical exercise on each of these topics. The main parts of the course:
Basic Statistics: In this section, you will learn statistics essential for building models and basic knowledge of data science. You will learn topics such as data, measurement scales, population and sample, measures of central tendency, measures of position, measures of dispersion, covariance, correlation, outliers, noise, and standard error.
R Programming: The best way to learn programming is to do it. In this section, you will learn the basic concepts of R and solve exercises in R. You will learn topics such as the principles of R, data structures and its types, and then working with R through exercises.
Modeling: In this section, you will learn the basics of modeling and you will get to know different algorithms such as linear regression, logistic regression, decision tree, Naive Bayes algorithm, resampling methods. You will then do exercises on each of these topics in R.
Advanced Modeling: In this section, you will delve deeper into modeling and learn about advanced algorithms such as discriminant analysis, principal component analysis, support vector machines, clustering, basket/association analysis, neural networks, and time series. You will then do exercises on each of these topics in R.
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