Learning Path: R: Master Data Mining Techniques with R

Training

Virtual

$ 100.001-250.000

Descripción

  • Tipología

    Training

  • Metodología

    Virtual

  • Horas lectivas

    7h

  • Inicio

    Fechas disponibles

"The world is emitting data at a very high pace and everyone wants to gain insights from the huge number of data coming their way. Data mining provides a way of finding these insights and R has become the go-to-tool for it among the data analysts and data scientists. If you're looking forward to working on complex data mining projects and gaining deeper insights of data, then go for this Learning Path.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The highlights of this Learning Path are: Practical projects on real-world data mining use cases presented in a very easy to understand manner, One stop solution to perform spatial data mining, text mining, social media mining, and web mining
Let’s get on this data mining journey together! This Learning Path starts with a brief introduction to R and setting up the development environment. This Learning Path will then teach you various data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields.
After completing this Learning Path, you will have a solid understanding of all data mining techniques and how to implement them using R, in any real-world scenario.
We have combined the best authors to ensure that your learning journey is smooth:
Dr. Samik Sen is a theoretical physicist and loves thinking about hard problems. After his PH.D. in developing computational methods to solve problems for which no solutions existed, he began thinking about how to tackle math problems while lecturing.
Pradeepta Mishra is a data scientist, predictive modeling expert, deep learning and machine learning practitioner, and an econometrician."

Información importante

¿Qué objetivos tiene esta formación?: "Make use of statistics and programming to understand data mining concepts and their application
Explore various libraries available in R for data mining
Apply data management steps to handle large datasets
Get to know various data visualization libraries available in R to represent data
Create predictive models to build a recommendation engine
Implement various dimension reduction techniques to handle large datasets
Acquire knowledge about the neural network concept drawn from computer science and its applications in data mining"

¿Esta formación es para mí?: This Learning Path is aimed at aspiring or professional data analysts or data scientists who want to gain deeper insights of data.

Requisitos: "Basic programming knowledge of R Basic knowledge of Math and Statistics"

Sedes y fechas disponibles

Ubicación

Inicio

Online

Inicio

Fechas disponibles Inscripciones abiertas

Materias

  • Data Mining
  • Networks
  • Project
  • Spatial

Programa académico

"Speaking ‘R’ - The Language of Data Science The Course Overview What Is R? Getting and Setting Up R/Rstudio Using RStudio Packages A Lot Is the Same Familiar Building Programming Blocks Putting It All Together Core R Types Some Useful Operations More Useful Operations Titanic Tennis It's Mostly Cleaning Up The Most Widely Used Statistical Package Distributions Time to Get Graphical Plotting to Another Dimension Facets Test Your Knowledge R Data Mining Projects The Course Overview What Is Data Mining? Introduction to the R Programming Language Data Type Conversion Sorting, Merging, Indexing, and Subsetting Dataframes Date and Time Formatting Types of Functions Loop Concepts Applying Concepts String Manipulation NA and Missing Value Management and Imputation Techniques Univariate Data Analysis Bivariate Analysis Multivariate Analysis Understanding Distributions and Transformation Interpreting Distributions and Variable Binning Contingency Tables, Bivariate Statistics, and Checking for Data Normality Hypothesis Testing Non-Parametric Methods Introduction to Data Visualization Visualizing Charts, and Geo Mapping Visualizing Scatterplot, Word Cloud and More Using plotly Creating Geo Mapping Introduction about Regression Linear Regression Stepwise Regression Method for Variable Selection Logistic Regression Cubic Regression Introduction to Market Basket Analysis Practical project Test Your Knowledge Advanced Data Mining projects with R The Course Overview Understanding Customer Segmentation Clustering Methods – K means and Hierarchical Clustering Methods – Model Based, Other and Comparison What Is Recommendation? Application of Methods and Limitations of Collaborative Filtering Practical Project Why Dimensionality Reduction? Practical Project around Dimensionality Reduction Parametric Approach to Dimension Reduction 4.1 Introduction to Neural Networks Understanding the Math Behind the Neural Network Neural Network Implementation in R Neural Networks for Prediction Neural Networks for Classification Neural Networks for Forecasting Merits and Demerits of Neural Networks Test Your Knowledge"

Learning Path: R: Master Data Mining Techniques with R

$ 100.001-250.000