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Big Data Analytics with R Introduction
This Big data analytics & hadoop training program extensively covers big data and predictive analytics techniques using R and Hadoop. With big data analytics, data scientists and others can analyze huge volumes of data that conventional analytics and business intelligence solutions can’t touch. Consider this; it’s possible that your organization could accumulate (if it hasn’t already) billions of rows of data with hundreds of millions of data combinations in multiple data stores and abundant formats. High-performance analytics is necessary to process that much data in order to figure out what’s important and what isn’t. Enter big data analytics.
Big Data Analytics with R Online Course
- Scenario Oriented Training
- Materials and Certification Guidance
- Access For Hands-On
- Live-Support During Sessions Hours
Our Trainers
- More than 8 Years of experience in Big Data Analytics with R Technologies
- Has worked on multiple realtime Big Data Analytics with R projects
- Working in a top MNC company
- Trained 2000+ Students so far.
- Strong Theoretical & Practical Knowledge
- Industry certified Professionals
Big Data Analytics with R Info
- Introduction to Big Data
- Logistics
- Analysis through DataVisualization
- Understanding the “business case” and defining a solution framework
- An introduction to R programming language and environment
- Techniques of Pre-processing data (Binning, Normalizing, Filling missing values, removing noise)
- Data Pre-processing—continued
- Traps and Errors
- Confusion matrix, Analyze False positives and False Negatives from a problem perspective
- Different error measures used in Forecasting
- Model Selection
- K-fold validation
- Introduction to Decision Trees and their structure
- Construction of Decision Trees through simplified examples
- Choosing the “best” attribute at each non-leaf node
- Entropy
- Information Gain
- Generalizing Decision Trees
- Information Content and Gain Ratio
- Dealing with numerical variables other measures of randomness
- Inductive learning from a 500-ft view
- Issues in inductive learning like curse of dimensionality
- Over fitting
- Bias-Variance tradeoff
- Pruning a Decision Tree
- Cost as a consideration
- Unwrapping Trees as rules
- A mathematical model for association analysis
- Large item sets and Association Rules
- Apriori
- Constructs large itemsets with minisup by iterations
- Interestingness of discovered association rules
- Application examples
- Association analysis vs. Classification
- Using Association Rules to compare stores
- Dissociation Rules
- Sequential Analysis Using
- Association Rules
- Data visualization and Story-telling
- Anatomy of a graph
- Animated graphs, BI dashboards and the latest trends in data visualization
- An end-to-end case study in R involving understanding the data
- Filling the missing values
- Applying and assessing models and reporting the results.
- Working professionals, managers and recent graduates are eligible for the program. We do not specify any academic background requirements.
- Elementary programming skills.
Online
- It is a 12 days program and extends up to 2hrs each.
- The format is 20% theory, 80% Hands-on.
- Instructor-Led Regular Online (Limited Persons Per Group) Training.
- Instructor-Led Online On Demand Training ( 1-1 or Corporate Training ).
Corporate
- It is a 3 days program and extends up to 8hrs each.
- The format is 20% theory, 80% Hands-on.
Classroom
- Private Classroom arranged on request and minimum attendees for batch is 4.
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