Courses
Difficulty Level: BEGINNER
Age group: 15-39
data science - Data Science with R Data Science with R - Introduction to R Programming - Analysis of Variance (ANOVA) - R-Packages - Sorting of Data Frame - Matrices and Vectors in R - Reading Data from External Files - Generating Plots - K-Means Clustering Introduction to R Programming - Features of R - Introduction to RStudio - Familiarity with Different Data Types and Functions - Learning to Deploy them in Various Scenarios - Use SQL to apply ‘join’ Function - Components of RStudio like Code Editor, - Visualization and Debugging Tools and Learn about R-bind - Statistical Packages R-Packages - R Functions - Code Compilation - R Package Structure - Package Metadata and Testing - CRAN (Comprehensive R Archive Network) - Vector Creation and Variables Sorting of Data Frame - R Functionality - Rep Function - Generating Repeats - Sorting and Generating Factor Levels - Transpose and Stack Function Matrices and Vectors in R - Introduction to Matrix - Vector in R - Understanding various functions like Merge, Strsplit - Matrix Manipulation, RowSums, RowMeans,ColMeans,ColSums - Sequencing, Repetition, Indexing and Other Functions Reading Data from External Files - Understanding Subscripts in Plots in R - How to Obtain Parts of Vectors - Using Subscripts with Arrays - Lists and Understanding How to Read Data from External Files Generating Plots - Generate Plots in R - Graphs - Bar Plots - Line Plot - Histograms and Components of a Pie Chart Analysis of Variance (ANOVA) - Working with Pie Charts and Histograms - Deploying ANOVA with R - One-way ANOVA, and Two-Way ANOVA - Understanding Analysis of Variance - Statistical Technique K-Means Clustering - K-Means Clustering for Cluster and Affinity Analysis - Cluster Algorithm - Cohesive Subset of Items - Solving Clustering Issues - Working with Large Datasets - Association Rule Mining Affinity Analysis for Data Mining - Learning Co-Occurrence Relationships Assignment - Assignment
Knowledge of Programming Language R
Enthuziasm to learn
Introduction to Machine Learning: Get started with basic machine learning techniques in R, including supervised and unsupervised learning methods like linear regression and clustering.
Data Visualization: Learn to create informative plots and charts using ggplot2 to explore and present data visually.
Statistical Analysis: Gain foundational knowledge of statistical concepts and methods such as descriptive statistics, hypothesis testing, and regression analysis.
Data Manipulation: Understand how to import, clean, and manipulate data using R packages like dplyr and tidyr.
Introduction to R Programming: Learn the basics of R syntax, data types, variables, and functions.
Knowledge of R Studio with Functionality and Tools
Understanding of Statistical Modeling Techniques
Skill of Deploying ANOVA with R
Ability to K-Means Clustering
Mastery in handling, storing, and analyzing the data
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