Description
This book teaches you to draw insights from your data. In today's data-driven business landscape, making informed decisions requires effective data analysis. This book guides you through the steps to import, structure, and visualize your data in R, and apply statistical and ML algorithms to drive better insights.This book offers a thorough introduction to data science, starting with R programming basics and advancing to ML and data visualization. Learn to clean, explore, and transform data using tools like dplyr. Key statistical concepts like probability, hypothesis testing, and modeling are covered, providing a foundation for data-driven decisions. Discover supervised and unsupervised ML techniques, feature engineering, and model evaluation. The book also provides career guidance in data science, including skill-building tips and job search strategies, equipping you to excel in this growing field.By the end of this book, you will be able to confidently use R to prepare data for analysis and apply ML algorithms to make predictions and drive business decisions.
Key Features
? Master R for effective data analysis and ML.
? Analyze data, identify patterns, and drive informed decision-making.
? Learn by doing hands-on R codes and applying ML techniques.
What you will learn
? Use R to clean, analyze, and visualize data effectively.
? Apply statistical techniques to find patterns and trends in data.
? Understand and implement key ML algorithms step-by-step.
? Data visualization techniques using ggplot2 to create informative visualizations.
? Strong foundation in statistical concepts, including probability theory, hypothesis testing, and statistical modeling.
Who this book is for
This book is ideal for individuals with a basic understanding of programming and statistics who aspire to enter the field of data science. Professionals such as data analysts, software engineers, and researchers will find this book particularly valuable as it provides a practical approach to leveraging data for informed decision-making.
Table of Contents
1. The Data Science Landscape
2. R Basics
3. Exploring Data
4. Wrangling Data
5. Working with Dates
6. Manipulating Strings
7. Visualizing Dat
8. Feature Engineering
9. Statistics and Probability
10. Introducing ML
11. Training Machine Learning Models
12. Building a Career in Data Science