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Course Description
The Data Science with R course is designed to provide participants with a thorough understanding of data science concepts, techniques, and applications using R, a powerful programming language specifically developed for statistical computing and data analysis. This course covers the full data science workflow, from data collection and cleaning to advanced analysis and visualization, enabling learners to extract valuable insights from data.
What you'll learn in this course?
At the end of Data Science with R training course, participants will
• Get acquainted with various analysis and visualization tools such as Ggplot and plotly
• Understand the behavior of data; build significant models to understand Statistics Fundamentals
• Learn about the various R libraries like Dplyr, Data.table used to manipulate data
• Use R libraries and work on data manipulation, data preparation and data explorations
• Use of R graphics libraries like Ggvis, Plotly etc.
• Learn Supervised and Unsupervised Machine Learning Algorithms
Prerequisites
There is no specific pre-requisite for the course however exposure to core Java and statistics will be beneficial.
Course Curriculum
• What is Data Science?
• Analytics Landscape
• Life Cycle of a Data Science Project
• Data Science Tools & Technologies
• Intro to R Programming
• Installing and Loading Libraries
• Data Structures in R
• Control & Loop Statements in R
• Functions in R
• Loop Functions in R
• String Manipulation & Regular Expression in R
• Working with Data in R
• Data Visualization in R
• Case Study
• Measures of Central Tendency
• Measures of Dispersion
• Descriptive Statistics
• Probability Basics
• Marginal Probability
• Bayes Theorem
• Probability Distributions
• Hypothesis Testing
• ANOVA
• Linear Regression (OLS)
• Case Study: Linear Regression
• Principal Component Analysis
• Factor Analysis
• Case Study: PCA/FA
• Logistic Regression
• Case Study: Logistic Regression
• K-Nearest Neighbor Algorithm
• Case Study: K-Nearest Neighbor Algorithm
• Decision Tree
• Case Study: Decision Tree
• Understand Time Series Data
• Visualizing TIme Series Components
• Exponential Smoothing
• Holt’s Model
• Holt-Winter’s Model
• ARIMA
• Case Study: Time Series Modeling on Stock Price
