Udemy – Data Science With R 2024-6

Published on: 2024-08-09 17:57:38

Categories: 28

Description

The Data Science With R course takes you into the fascinating world of machine learning and helps you become an expert in this field using the powerful R programming language. This course covers a wide range of topics including regression, classification, clustering and dimensionality reduction techniques. You will gain hands-on experience that will allow you to confidently apply machine learning methods in real-world scenarios. The hands-on approach of this course ensures that you not only understand the theoretical concepts, but also learn how to effectively implement them using R. By completing this course, you will acquire the necessary knowledge and skills to succeed in the field of machine learning.

What you will learn

Data Interpretation: Master the interpretation of graphs and summary statistics to extract meaningful insights from data.
Data exploratory skills: understanding data structures and data summarization using R.
Data cleaning and preprocessing: handling missing values, detecting outliers, and applying appropriate techniques to improve data quality in R.
Mastering Data Visualization: Visualizing Data Using R Libraries.
Statistical analysis: performing statistical tests and building statistical models using R.
Feature engineering: Developing skills in creating and selecting features to improve model performance.

Who is this course suitable for:

Data analysts
Specialists in related fields
University students and researchers
Business professionals
Entrepreneurs and innovators
People who want to change jobs
Anyone interested in data analysis

Specifications of the Data Science With R course

Publisher: Udemy
Instructor: GreyCampus Edutech
Training level: beginner to advanced
Training duration: 8 hours and 24 minutes
Number of courses: 7

Course headings

Prerequisites of the Data Science With R course

Fundamental computer skills and comfort with installing and using software.
Understanding of key concepts in data handling, such as loading data from different sources (CSV, Excel, databases) and basic data cleaning
Basic understanding of programming concepts