Udemy – Data Science, AI, and Machine Learning with R 2024-9

Published on: 2024-09-30 19:53:22

Categories: 28

Description

Data Science, AI, and Machine Learning with R. What is the course? R is a free and open source programming language designed for statistical computing and graphics. This language is widely used by statisticians, data scientists and researchers. R’s key strengths in data science, artificial intelligence, and machine learning:

Extensive ecosystem: R has a rich set of packages (18,000+) provided by the community covering a wide range of data analysis and machine learning tasks.
Data visualization: Powerful R visualization libraries (such as ggplot2) create high-quality charts and graphics that help explore data and communicate insights.
Statistical power: R’s statistical foundation provides a strong foundation for data analysis, hypothesis testing, and modeling.
Reproducibility: With its R Markdown capabilities, R encourages reproducible research and makes it easier to document and share the entire analysis process.

What you will learn in the course

Understanding the main concepts of data science and its applications in various industries
Setting up and effectively navigating the R programming environment
Mastery of R programming basics, including data types, structures, operators, and control flow
Understanding basic statistical and probabilistic concepts for data analysis
Gathering data from various sources (flat files, databases, web, APIs)
Clean, manipulate and pre-process data to ensure its quality and suitability for analysis
Perform exploratory data analysis to discover patterns and insights using visualizations
Effective data analysis and interpretation using R’s powerful statistical and visualization tools
Building and evaluating different machine learning models for: prediction (regression), classification, clustering, extracting association rules
Using dimensionality reduction methods such as PCA and LDA
Using ensemble methods (bagging and boosting) to improve model performance
Building and deploying machine learning models using R to solve real-world problems
Critical thinking about data and applying data science techniques in different contexts
Completion of a comprehensive final project to consolidate learning and demonstrate practical skills in data science and machine learning using R

Who is this course suitable for?

Anyone looking for a career in data science, machine learning, and artificial intelligence.
Data analysts looking to expand their skills and move into data science roles.
Data scientists migrating to R from other tools or languages.
Machine learning engineers looking to strengthen their foundation in data science and statistics using R.
AI engineers looking to exploit R’s capabilities for data analysis and research.
Business analysts looking to leverage data for more advanced insights and decision making.
Statisticians who want to learn how to apply their machine learning expertise.
Software developers or engineers interested in data-driven applications and artificial intelligence development.
Researchers from various fields (such as social sciences, biology) who intend to use data science and machine learning techniques in their work.
Anyone with a slightly strong background looking to transition into a data science career.
Undergraduate or graduate students studying statistics, computer science, mathematics, or related fields.
Researchers and academics interested in integrating data science and machine learning in their research.