Data Science with R

Equip yourself with the statistical power of R to transform raw data into actionable insights.

Duration

2 Days

Language

English

Delivery Mode

Online/Hybrid/Face-to-Face

Course Overview

Learning Objectives

  • Apply fundamental R programming concepts, including data structures and control logic, to solve computational problems.
  • Perform comprehensive data manipulation and cleaning to prepare messy, real-world datasets for analysis.
  • Execute exploratory data analysis (EDA) to uncover hidden patterns, trends, and relationships within data.
  • Implement statistical testing and hypothesis validation to ensure the accuracy of data-driven conclusions.
  • Develop and evaluate machine learning models for classification, regression, and clustering tasks.
  • Design professional-grade visualizations and interactive dashboards to communicate complex insights effectively.

Course Outline

Day 01
  • R Programming Essentials: Introduction to the environment, syntax, and the extensive package ecosystem.
  • Data Wrangling and Preprocessing: Techniques for handling missing values, transforming variables, and merging diverse datasets.
  • Advanced Statistical Methods: Applying hypothesis testing, regression analysis, and statistical modeling.
  • Supervised and Unsupervised Machine Learning: Training algorithms for predictive accuracy and pattern recognition.
  • Visual Storytelling with ggplot2: Creating impactful plots and interactive visualizations for executive reporting.
  • Applied Data Projects: Hands-on case studies focused on solving industry-specific data challenges.

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