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 Naïve 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
- 1 stars: 1 votes
- 2 stars: 2 votes
- 3 stars: 1 votes
- 4 stars: 1 votes
- 5 stars: 2 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024