Data Science With R
Data Science With R
Updated on Jul, 2024
Language - English
Development,Data Science,R Programming
Lectures -7
Duration -8 hours
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Course Description
Welcome to Data Science with R! This comprehensive course guides you through the fascinating realm of machine learning, leveraging the powerful R programming language. You'll begin with fundamental concepts and gradually advance to implementing sophisticated algorithms, equipping you with the expertise needed to excel in the field of machine learning.
The course covers a wide range of topics, including regression, classification, clustering, and dimensionality reduction techniques. You will gain practical experience, empowering you to confidently apply machine learning methods in real world scenarios. The practical approach ensures that you not only understand theoretical concepts but also learn how to implement them effectively using R.
Geared towards data scientists, aspiring machine learning engineers, students, academic researchers, and anyone interested in data analysis, this course aims to develop your proficiency in using R for machine learning tasks. By the end of the course, you will be well-equipped to tackle complex data challenges, enhance your analytical skills, and contribute effectively to data-driven decision-making processes within your organization.
Embark on this educational journey with us and master the art of machine learning using R. Throughout this course, you will gain the confidence and expertise to apply advanced techniques to tackle real-world challenges effectively.
Who this course is for:
- Data analysts
- Professionals in related fields
- Students and academic researchers
- Business professionals
- Entrepreneurs and innovators
- Career Changers
- Anyone Interested in Data Analysis
Goals
Data Interpretation: Master interpreting visualizations and summary statistics to derive meaningful insights from data.
Data Exploration Skills: Understanding data structures and data summarization using R
Data Cleaning and Preprocessing: Handling missing values, outlier detection, and applying appropriate techniques to improve data quality in R
Data Visualization Proficiency: Visual representation 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
Prerequisites
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
Curriculum
Check out the detailed breakdown of what’s inside the course
Data Exploration
3 Lectures
-
Data Exploration - 1
01:00:59
01:00:59
-
Data Exploration - 2
01:27:23
01:27:23
-
Data Exploration - 3
49:10
49:10
Data Manipulation
2 Lectures
Data Visualization
2 Lectures
Instructor Details
GreyCampus Inc.
About me
GreyCampus helps people power their careers through skills and certifications. We believe continuous upskilling and certifications is key to sustained success in your career. While older skills are fast becoming less relevant, need for newer in-demand skills is growing exponentially. We believe if you stay skilled, you will stay ahead.
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