Courses Descriptions
"Master Data Science with R: Learn data analysis, visualization, machine learning, and statistical modeling using R for real-world applications."
Overview of Data Science and Its Applications
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Introduction to R and RStudio Environment
0:00:00
Installing R Packages and Libraries
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Basic R Syntax and Data Types
0:00:00
Working with Vectors, Matrices, Lists, and Data Frames
0:00:00
Importing and Exporting Data (CSV, Excel, Databases)
0:00:00
Data Manipulation with dplyr and tidyverse
0:00:00
Handling Missing Values and Data Cleaning
0:00:00
Data Transformation and Summarization
0:00:00
Exploratory Data Analysis (EDA) Techniques
0:00:00
Data Visualization with ggplot2
0:00:00
Working with Large Datasets Efficiently
0:00:00
Descriptive and Inferential Statistics
0:00:00
Probability Distributions and Sampling Methods
0:00:00
Hypothesis Testing (t-tests, Chi-Square, ANOVA)
0:00:00
Correlation and Regression Analysis
0:00:00
Time Series Analysis Basics
0:00:00
Introduction to Supervised and Unsupervised Learning
0:00:00
Regression Models (Linear, Logistic)
0:00:00
Classification Models (Decision Trees, Random Forests, SVM)
0:00:00
Clustering Techniques (k-Means, Hierarchical Clustering)
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Model Evaluation and Performance Metrics
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Introduction to Deep Learning in R
0:00:00
Working on a Real-World Dataset
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Building and Optimizing Machine Learning Models
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Automating Workflows with R Scripts
0:00:00
Introduction to R Shiny for Interactive Dashboards
0:00:00
Final Project: End-to-End Data Science Case Study
0:00:00
Course Recap and Next Steps for Further Learning
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