Practical Recommender Systems For Business Applications in R

Implementing Data Science Driven Recommender Systems For Business Applications With R

Descripción

• Are you interested in learning how the Big Tech giants like Amazon and Netflix recommend products and services to you?
• Do you want to learn how data science is hacking the multibillion e-commerce space through recommender systems?
• Do you want to implement your own recommender systems using real-life data?
• Do you want to develop cutting edge analytics and visualisations to support business decisions?
• Are you interested in deploying machine learning and natural language processing for making recommendations based on prior choices and/or user profiles?

You Can Gain An Edge Over Other Data Scientists If You Can Apply R Data Analysis Skills For Making Data-Driven Recommendations Based On User Preferences

• By enhancing the value of your company or business through the extraction of actionable insights from commonly used structured and unstructured data commonly found in the retail and e-commerce space
• Stand out from a pool of other data analysts by gaining proficiency in the most important pillars of developing practical recommender systems

MY COURSE IS A HANDS-ON TRAINING WITH REAL RECOMMENDATION RELATED PROBLEMS- You will learn to use important R data science techniques to derive information and insights from both structured data (such as those obtained in typical retail and/or business context) and unstructured text data

My course provides a foundation to carry out PRACTICAL, real-life recommender systems tasks using Python. By taking this course, you are taking an important step forward in your data science journey to become an expert in deploying the R Programming data science techniques for answering practical retail and e-commerce questions (e.g. what kind of products to recommend based on their previous purchases or their user profile).

Why Should You Take My Course?

I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science intense PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real-life data from different sources and producing publications for international peer-reviewed journals.

This course will help you gain fluency in deploying data science-based recommended systems in R to inform business decisions. Specifically, you will:

• Learn the main aspects of implementing data science techniques in the R Programming Language
• Learn what recommender systems are and why they are so vital to the retail space
• Learn to implement the common data science principles needed for building recommender systems
• Use visualisations to underpin your glean insights from structured and unstructured data
• Implement different recommender systems in the R Programming Language
• Use common natural language processing (NLP) techniques to recommend products and services based on descriptions and/or titles

Lo que aprenderás

• Learn what recommender systems are and their importance for business intelligence
• Learn the main aspects of implementing data science technique within the R Programming Language
• Implement practical recommender systems using R Programming Language
• Learn about the theoretical and practical aspects of recommender systems

¿Para quién es este curso?

• People Wanting To Master The R Programming Language For Data Science
• Students Interested In Developing Powerful Data Visualisations
• Learning to Make Product and Service Recommendations Based on Prior Choices
• Identify the Best Recommender System For Your Problem

Requisitos

• Be Able To Operate & Install Software On A Computer
• Prior Exposure to R Programming Concepts Will be Helpful
• Prior Exposure to the R Studio Environment
• An Interest in Learning About Practical Recommender Systems

Más cursos del mismo autor: Minerva Singh

Frequent Questions

• Is the price in dollars? A: Yes, it is value is in dollars (USD)
• In what language is it? A: The course is in English

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