Learning Data Mining with R

This hands-on tutorial covers topics in three dimensions: the mathematical foundations, the actual implementation in R, and practical examples Get to know the basic concepts of R: the data frame and data manipulation. Gain a good level of knowledge and an understanding of the data mining disciplines to solve real-world challenges in R.

  • Comprehensive training through 30 video sessions.
  • Explore graphs and the statistical measure in graphs
  • Gain insights into the different association types
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    Self-Paced

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    About Learning Data Mining with R

    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 can be used for day-to-day data analysis tasks.

    Data mining is a very broad topic and takes some time to learn. This course will help you to understand the mathematical basics quickly, and then you can directly apply what you’ve learned in R. This course covers each and every aspect of data mining in order to prepare you for real-world problems. You'll come to understand the different disciplines in data mining. In every discipline, there exist a variety of different algorithms. At least one algorithm of the various classes of algorithms will be covered to give you a foundation to further apply your knowledge to dive deeper into the different flavors of algorithms.

    Course Objectives
    • Get to know the basic concepts of R: the data frame and data manipulation
    • Discover the powerful tools at hand for data preparation and data cleansing
    • Visually find patterns in data
    • Work with complex data sets and understand how to process data sets
    • Get to know how object-oriented programming is done in R
    • Explore graphs and the statistical measure in graphs
    • Gain insights into the different association types
    • Decide what algorithms actually should be used and what the desired and possible outcomes of the analysis should be
    • Grasp the discipline of classification, the mathematical foundation that will help you understand the bayes theorem and the naïve bayes classifier
    • Delve into various algorithms for classification such as KNN and see how they are applied in R
    • Evaluate k-Means, Connectivity, Distribution, and Density based clustering
    Curriculum
    Module 1:

    Getting Started – A Motivating Example

    • The Course Overview
    • Getting Started with R
    • Data Preparation and Data Cleansing
    • The Basic Concepts of R
    Module 2:

    Clustering – A Dating App For Your Data Points

    • Data Points and Distances in a Multidimensional Vector Space
    • An Algorithmic Approach to Find Hidden Patterns in Data
    • A Real-world Life Science Example
    Module 3:

    R Deep Dive, Why Is R Really Cool?

    • Example – Using a Single Line of Code in R
    • R Data Types
    • R Functions and Indexing
    • S3 Versus S4 – Object-oriented Programming in R
    Module 4:

    Association Rule Mining

    • Market Basket Analysis
    • Introduction to Graphs
    • Different Association Types
    Module 5:

    Classification

    • Mathematical Foundations
    • The Naive Bayes Classifier
    • Spam Classification with Naïve Bayes
    Module 6:

    Clustering

    • Hierarchical Clustering
    • Distribution-based Clustering
    • Density-based Clustering
    Module 7:

    Cognitive Computing And Artificial Intelligence In Data Mining

    • Introduction to Neural Networks and Deep Learning
    • Using the H2O Deep Learning Framework
    • Real-time Cloud Based IoT Sensor Data Analysis
    Instructor
    <

    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.

    https://www.linkedin.com/in/romeo-kienzler-089b4557

    https://www.packtpub.com/big-data-and-business-intelligence/learning-data-mining-r-video

    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.

    http://dataconomy.com/where-life-science-and-data-science-meet-interview-with-romeo-kienzler-of-ibm/ Romeo has spoken at the O'Reilly's Velocity conference.

    http://conferences.oreilly.com/velocity/devops-web-performance-eu-2015/public/schedule/speaker/219260

    http://www.meetup.com/Big-Data-Developers-in-Berlin/events/227744512/

    Certification

    A test will be conducted at the end of the course. On completion of the test with a minimum of 70% marks, training.com will issue a certificate of successful completion from NIIT.

    Five re-attempts will be provided in case the candidate scores less than 70%.

    A Participation certificate will be issued if the candidate does not score 70% after five attempts.

    Pre-requisites

    Basic knowledge of data analysitcs and basic programming background with Math or Science

    FAQs

    Who should go for this Course?

    Individuals aspring to become Data Science professional involving data analytics and data mining processes. This course is useful for professionals working on Data Mining Projects. using R Language.

    Where can I find my session schedule?

    The session schedule will be available in the training.com Student portal - Learning Plan section. You can login to your training.com account to view the same.

    What is your refund policy?

    Upon registering for the course, if for some reason you are unable or unwilling to participate in the course further, you can apply for a refund. You can initiate the refund any time before start of the second session of the course by sending an email to support@training.com , with your enrolment details and bank account details (where you want the amount to be transferred). Once you initiate a refund request, you will receive the amount within 21 days after confirmation and verification by our team. This is provided if you have not downloaded any courseware after registration.

    Why is it called Self Paced course?

    Self Paced courses are comprised of several learning videos into a course structure broken down into Learning Modules and Sessions. The learner is required to go through the videos topic-wise in the structure sequence of the course to learn the concepts. Being Self Paced, there is no intervention of any external faculty or additional mentor in learning.

    Being a self paced course, how will my attendance be tracked and marked?

    you login into your training.com account to watch the videos, attendance for it will be marked automatically.

    How will the assessment be conducted for my certification?

    After each module, a multiple choice questions type online assessment will be conducted. 5 Attempts will be allowed for the assessment to be completed. The minimum pass percentage for each assessment is 70%. On successfully clearing the assessment, a verified certificate from NIIT shall be awarded otherwise the certificate of participation will be issued.

    What are the minimum system requirements to attend the course?

      Minimum system requirements for accessing the courses are:

    • Personal computer or Laptop with web camera
    • Headphone with Mic
    • Minimum 4 Mbps broadband connection

    Is there an official support desk for technical guidance during the training program?

    Yes.For immediate technical support during the live online classroom sessions, you can call 91-9717992809 or 0124-4917203 between 9:00 AM and 8:00 PM IST. You can write to support@training.com for all other queries and our team will be happy to help you.

    Course Features

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