Learning Path- R- Real-World Data Mining With R

Learning Path: R: Real-World Data Mining With R, available at $34.99, has an average rating of 3.21, with 78 lectures, based on 7 reviews, and has 134 subscribers.

You will learn about Get to know the basic concepts of R: the data frame and data manipulation Work with complex data sets and understand how to process data sets Explore graphs and the statistical measure in graphs Apply data management steps to handle large datasets Implement various dimension reduction techniques to handle large datasets Create predictive models in order to build a recommendation engine Acquire knowledge about the neural network concept drawn from computer science and its applications in data mining This course is ideal for individuals who are This course is ideal for data analysts from novice to intermediate level. You should have prior knowledge of basic statistics and some programming language experience in any tool or platform. Familiarity with R will be an added advantage. It is particularly useful for This course is ideal for data analysts from novice to intermediate level. You should have prior knowledge of basic statistics and some programming language experience in any tool or platform. Familiarity with R will be an added advantage.

Enroll now: Learning Path: R: Real-World Data Mining With R

Summary

Title: Learning Path: R: Real-World Data Mining With R

Price: $34.99

Average Rating: 3.21

Number of Lectures: 78

Number of Published Lectures: 78

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Get to know the basic concepts of R: the data frame and data manipulation

Work with complex data sets and understand how to process data sets

Explore graphs and the statistical measure in graphs

Apply data management steps to handle large datasets

Implement various dimension reduction techniques to handle large datasets

Create predictive models in order to build a recommendation engine

Acquire knowledge about the neural network concept drawn from computer science and its applications in data mining

Who Should Attend

This course is ideal for data analysts from novice to intermediate level. You should have prior knowledge of basic statistics and some programming language experience in any tool or platform. Familiarity with R will be an added advantage.

Target Audiences

This course is ideal for data analysts from novice to intermediate level. You should have prior knowledge of basic statistics and some programming language experience in any tool or platform. Familiarity with R will be an added advantage.

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?

Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It is very useful for day-to-day data analysis tasks.

Data mining is a very broad topic and takes some time to learn. This Learning Path will help you to understand the mathematical basics quickly, and then you can directly apply what you’ve learned in R. This Learning Path explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields.

This Learning Path is the complete learning process for data-happy people. We begin with a thorough introduction to data mining and how R makes it easy with its many packages. We then move on to exploring data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields using R’s vast set of algorithms.

The goal of this Learning Path is to help you understand the basics of data mining with R and then get you working on real-world datasets and projects.

This Learning Path is authored by some of the best in their fields.

Romeo Kienzler

Romeo Kienzler is the Chief Data Scientist of the IBM Watson IoT Division and working as an Advisory Architect helping client worldwide to solve their data analysis problems.

He holds an M. Sc. of Information System, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology. He works as an Associate Professor for data mining at a Swiss University and his current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J. He also contributes to various open source projects. Additionally, he is currently writing a chapter on Hyperledger for a book on Blockchain technologies.

Pradeepta Mishra

Pradeepta Mishra is a data scientist, predictive modeling expert, deep learning and machine learning practitioner, and econometrician. He currently leads the data science and machine learning practice for Ma Foi Analytics, Bangalore, India. Ma Foi Analytics is an advanced analytics provider for Tomorrow’s Cognitive Insights Ecology, using a combination of cutting-edge artificial intelligence, a proprietary big data platform, and data science expertise. He holds a patent for enhancing the planogram design for the retail industry. Pradeepta has published and presented research papers at IIM Ahmedabad, India. He is a visiting faculty member at various leading B-schools and regularly gives talks on data science and machine learning.

Pradeepta has spent more than 10 years solving various projects relating to classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures, spanning across domains such as healthcare, insurance, retail and e-commerce, manufacturing, and so on.

Course Curriculum

Chapter 1: Learning Data Mining with R

Lecture 1: The Course Overview

Lecture 2: Getting Started with R

Lecture 3: Data Preparation and Data Cleansing

Lecture 4: The Basic Concepts of R

Lecture 5: Data Frames and Data Manipulation

Lecture 6: Data Points and Distances in a Multidimensional Vector Space

Lecture 7: An Algorithmic Approach to Find Hidden Patterns in Data

Lecture 8: A Real-world Life Science Example

Lecture 9: Example – Using a Single Line of Code in R

Lecture 10: R Data Types

Lecture 11: R Functions and Indexing

Lecture 12: S3 Versus S4 – Object-oriented Programming in R

Lecture 13: Market Basket Analysis

Lecture 14: Introduction to Graphs

Lecture 15: Different Association Types

Lecture 16: The Apriori Algorithm

Lecture 17: The Eclat Algorithm

Lecture 18: The FP-Growth Algorithm

Lecture 19: Mathematical Foundations

Lecture 20: The Naive Bayes Classifier

Lecture 21: Spam Classification with Na?ve Bayes

Lecture 22: Support Vector Machines

Lecture 23: K-nearest Neighbors

Lecture 24: Hierarchical Clustering

Lecture 25: Distribution-based Clustering

Lecture 26: Density-based Clustering

Lecture 27: Using DBSCAN to Cluster Flowers Based on Spatial Properties

Lecture 28: Introduction to Neural Networks and Deep Learning

Lecture 29: Using the H2O Deep Learning Framework

Lecture 30: Real-time Cloud Based IoT Sensor Data Analysis

Chapter 2: R Data Mining Projects

Lecture 1: The Course Overview

Lecture 2: What Is Data Mining?

Lecture 3: Introduction to the R Programming Language

Lecture 4: Data Type Conversion

Lecture 5: Sorting, Merging, Indexing, and Subsetting Dataframes

Lecture 6: Date and Time Formatting

Lecture 7: Types of Functions

Lecture 8: Loop Concepts

Lecture 9: Applying Concepts

Lecture 10: String Manipulation

Lecture 11: NA and Missing Value Management and Imputation Techniques

Lecture 12: Univariate Data Analysis

Lecture 13: Bivariate Analysis

Lecture 14: Multivariate Analysis

Lecture 15: Understanding Distributions and Transformation

Lecture 16: Interpreting Distributions and Variable Binning

Lecture 17: Contingency Tables, Bivariate Statistics, and Checking for Data Normality

Lecture 18: Hypothesis Testing

Lecture 19: Non-Parametric Methods

Lecture 20: Introduction to Data Visualization

Lecture 21: Visualizing Charts, and Geo Mapping

Lecture 22: Visualizing Scatterplot, Word Cloud and More

Lecture 23: Using plotly

Lecture 24: Creating Geo Mapping

Lecture 25: Introduction about Regression

Lecture 26: Linear Regression

Lecture 27: Stepwise Regression Method for Variable Selection

Lecture 28: Logistic Regression

Lecture 29: Cubic Regression

Lecture 30: Introduction to Market Basket Analysis

Lecture 31: Practical project

Chapter 3: Advanced Data Mining projects with R

Lecture 1: The Course Overview

Lecture 2: Understanding Customer Segmentation

Lecture 3: Clustering Methods & K means and Hierarchical

Lecture 4: Clustering Methods & Model Based, Other and Comparison

Lecture 5: What Is Recommendation?

Lecture 6: Application of Methods and Limitations of Collaborative Filtering

Lecture 7: Practical Project

Lecture 8: Why Dimensionality Reduction?

Lecture 9: Practical Project around Dimensionality Reduction

Lecture 10: Parametric Approach to Dimension Reduction

Lecture 11: Introduction to Neural Networks

Lecture 12: Understanding the Math Behind the Neural Network

Lecture 13: Neural Network Implementation in R

Lecture 14: Neural Networks for Prediction

Lecture 15: Neural Networks for Classification

Lecture 16: Neural Networks for Forecasting

Lecture 17: Merits and Demerits of Neural Networks

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

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2 stars: 2 votes

3 stars: 1 votes

4 stars: 1 votes

5 stars: 2 votes

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