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Data Science, R, Mahout Training - Combo Course -iPartner

Data Science, R, Mahout Training - Combo Course

  1. Data Science, R, Mahout Training - Combo Course
41 hr / 626*
(* including all taxes.)

Key Features

Course Agenda

  • Data Science Overview
  • Reasons to use Data Science
  • Project Lifecycle
  • Data Acquirement
  • Evaluation of Input Data
  • Transforming Data
  • Statistical and analytical methods to work with data
  • Machine Learning basics
  • Introduction to Recommender systems
  • Apache Mahout Overview
  • What is Data Science?
  • What Kind of Problems can you solve?
  • Data Science Project Life Cycle
  • Data Science-Basic Principles
  • Data Acquisition
  • Data Collection
  • Understanding Data- Attributes in a Data, Different types of Variables
  • Build the Variable type Hierarchy
  • Two Dimensional Problem
  • Co-relation b/w the Variables- explain using Paint Tool
  • Outliers, Outlier Treatment
  • Boxplot, How to Draw a Boxplot
  • Discussion on Boxplot- also Explain
  • Example to understand variable Distributions
  • What is Percentile? – Example using Rstudio tool
  • How do we identify outliers?
  • How do we handle outliers?
  • Outlier Treatment: Using Capping/Flooring General Method
  • Distribution- What is Normal Distribution?
  • Why Normal Distribution is so popular?
  • Uniform Distribution
  • Skewed Distribution
  • Transformation
  • Discussion about Boxplot and Outlier
  • Goal: Increase Profits of a Store
  • Areas of increasing the efficiency
  • Data Request
  • Business Problem: To maximize shop Profits
  • What are Interlinked variables
  • What is Strategy
  • Interaction b/w the Variables
  • Univariate analysis
  • Multivariate analysis
  • Bivariate analysis
  • Relation b/w Variables
  • Standardize Variables
  • What is Hypothesis?
  • Interpret the Correlation
  • Negative Correlation
  • Machine Learning
  • Correlation b/w Nominal Variables
  • Contingency Table
  • What is Expected Value?
  • What is Mean?
  • How Expected Value is differ from Mean
  • Experiment – Controlled Experiment, Uncontrolled Experiment
  • Degree of Freedom
  • Dependency b/w Nominal Variable & Continuous Variable
  • Linear Regression
  • Extrapolation and Interpolation
  • Univariate Analysis for Linear Regression
  • Building Model for Linear Regression
  • Pattern of Data means?
  • Data Processing Operation
  • What is sampling?
  • Sampling Distribution
  • Stratified Sampling Technique
  • Disproportionate Sampling Technique
  • Balanced Allocation-part of Disproportionate Sampling
  • Systematic Sampling
  • Cluster Sampling
  • 2 angels of Data Science-Statistical Learning, Machine Learning

  • Multi variable analysis
  • linear regration
  • Simple linear regration
  • Hypothesis testing
  • Speculation vs. claim(Query)
  • Sample
  • Step to test your hypothesis
  • performance measure
  • Generate null hypothesis
  • alternative hypothesis
  • Testing the hypothesis
  • Threshold value
  • Hypothesis testing explanation by example
  • Null Hypothesis
  • Alternative Hypothesis
  • Probability
  • Histogram of mean value
  • Revisit CHI-SQUARE independence test
  • Correlation between Nominal Variable
  • Machine Learning
  • Importance of Algorithms
  • Supervised and Unsupervised Learning
  • Various Algorithms on Business
  • Simple approaches to Prediction
  • Predict Algorithms
  • Population data
  • sampling
  • Disproportionate Sampling
  • Steps in Model Building
  • Sample the data
  • What is K?
  • Training Data
  • Test Data
  • Validation data
  • Model Building
  • Find the accuracy
  • Rules
  • Iteration
  • Deploy the model
  • Linear regression
  • Clustering
  • Cluster and Clustering with Example
  • Data Points, Grouping Data Points
  • Manual Profiling
  • Horizontal & Vertical Slicing
  • Clustering Algorithm
  • Criteria for take into Consideration before doing Clustering
  • Graphical Example
  • Clustering & Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy Clustering
  • Simple Approaches to Prediction
  • Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity
  • Clustering Algorithm in Mahout
  • Probabilistic Clustering
  • Pattern Learning
  • Nearest Neighbor Prediction
  • Nearest Neighbor Analysis
  • R introduction
  • How R is typically used
  • Features of R
  • Introduction to Big data
  • R+Hadoop
  • Ways to connect with R and Hadoop
  • Products
  • Case Study
  • Architecture
  • Steps for Installing RIMPALA
  • How to create IMPALA packages
  • Classification and Recommendation
  • Clustering in Mahout
  • Pattern Mining
  • Understanding machine Learning
  • Using Model diagram to decide the approach
  • Data flow
  • Supervised and Unsupervised learning

  • Concept of Recommendation
  • Recommendations by E-commerce site
  • Comparison between User Recommendations and Item recommendation
  • Define recommenders and Classifiers
  • Process of Collaborative Filtering
  • Explaining Pearson coefficient algorithm
  • Euclidean distance measure
  • Implementing a recommender using map reduce
    • What is statistics
    • How is this useful
    • What is this course for
    • Converting data into useful information
    • Collecting the data
    • Understand the data
    • Finding useful information in the data
    • Interpreting the data
    • Visualizing the data
  • Descriptive statistics
  • Let us understand some terms in statistics
  • Variable
  • Dot Plots
  • Histogram
  • Stemplots
  • Box and whisker plots
  • Outlier detection from box plots and Box and whisker plots
  • What is probability
  • Set & rules of probability
  • Bayes Theorem
  • Probability Distributions
  • Few Examples
  • Student T- Distribution
  • Sampling Distribution
  • Student t- Distribution
  • Poison distribution
  • Stratified Sampling
  • Proportionate Sampling
  • Systematic Sampling
  • P – Value
  • Stratified Sampling
  • Cross Tables
  • Bivariate Analysis
  • Multi variate Analysis
  • Dependence and Independence tests ( Chi-Square )
  • Analysis of Variance
  • Correlation between Nominal variables

Learn & Get

  • Learn the concept of Logistic Regression
  • Master Vector Creation and Assigning Values to Variables
  • Generate Repeats and Factor levels
  • Explore steps to install IMPALA
  • Get familiar with statistics concepts
  • Learn rules of Probability and Bayes Theorem

Payment Method

You need to pay through PayPal. We accept both Debit and Credit Card for transaction.
We subsidize our fees by 10% for military personnel, and college students with exceptional records. To apply for a scholarship, email
In our iPartner self-paced training program, you will receive the training assessments, recorded sessions, course materials, Quizzes, related softwares and assignments. The courses are designed in such a way that you will the get real world exposure; the solid understanding of every concept that allows you to get the most from the online training experience and you will be able to apply the information and skills in the workplace. After the successful completion of your training program, you can take quizzes which enable you to check your level of knowledge and also enables you to clear your relevant certification at higher marks/grade where you will be able to work on the technologies independently.
In Self-paced courses, the learners are able to conduct hands-on exercises and produce learning deliverables entirely on their own at any convenient time without a facilitator whereas in the Online training courses, a facilitator will be available for answering queries at a specific time to be dedicated for learning. During your self-paced learning, you can learn more effectively when you interact with the content that is presented and a great way to facilitate this is through review questions and quizzes that strengthen key concepts. In case if you face any unexpected challenges while learning, we will arrange a live class with our trainer.
All Courses from iPartner are highly interactive to provide good exposure to learners and gives them a real time experience. You can learn only at a time where there are no distractions, which leads to effective learning. The costs of self-paced training are 75% cheaper than the online training. You will offer lifetime access hence you can refer it anytime during your project work or job.
Yes, at the top of the page of course details you can see sample videos.
As soon as you enroll to the course, your LMS (The Learning Management System) Access will be Functional. You will immediately get access to our course content in the form of a complete set of previous class recordings, PPTs, PDFs, assignments and access to our 24*7 support team. You can start learning right away.
24/7 access to video tutorials and Email Support along with online interactive session support with trainer for issue resolving.
Yes, You can pay difference amount between Online training and Self-paced course and you can be enrolled in next online training batch.
Please send an email. You can join our Live chat for instant solution.
We will provide you the links of the software to download which are open source and for proprietary tools, we will provide you the trail version if available.
You will have to work on a training project towards the end of the course. This will help you understand how the different components of courses are related to each other.
Classes are conducted via LIVE Video Streaming, where you get a chance to meet the instructor by speaking, chatting and sharing your screen. You will always have the access to videos and PPT. This would give you a clear insight about how the classes are conducted, quality of instructors and the level of Interaction in the class.
Yes, we do keep launching multiple offers that best suits your needs. Please email us at: and we will get back to you with exciting offers.
We will help you with the issue and doubts regarding the course. You can attempt the quiz again.
Sure! Your feedbacks are greatly appreciated. Please connect with us on the email support -