Big Data Analytics Training

 

Big Data Analytics Training Overview

This Big Data Analytics training includes managing, storing and processing of Big Data, as well as analytics layer. For analyzing and processing the Data at maximum speed. Big Data Analytics course will give the necessary skills to deploy different analytical tools and techniques to work on Big Data and Hadoop.

Objectives of the Course

• Complete understanding of the Big Data Anayltics Concepts.
• Understand the different Data Processing skills.
• Real-Time Analysis on Large Data.

Pre Requisites

• System analyst and Data Analysts.
• Business Intelligence and Business Professionals.
• No Need of Programming Experience.
• Any one wants to learn BigData Analytics.

Big Data Analytics Course Highlights

• Weekly Two Tasks (20 Tasks)
• Exam after Each Session (4 Exams)
• Mini Project for each Data Mining Topic (12 Mini Projects)
• Power point Presentation, R, SAS Program with Data on each Day
• Real-time Project on Data Analytics

Big Data Analytics Course Content

Session 1

• Introduction to Data Science
• Analytical Terminology, Analytical Methodology
• Introduction to SAS, R, R-studio interface
• Data Collection, Creating Datasets
• Reading Data From External Files (.Txt, .Xls, .Csv) — Tasks
• Data Exploration: Proc Print, Proc Contents —Tasks
• Data Exploration: ProcGchart, ProcGplot —- Tasks
• Data Exploration: Statistical Terminology
• Data Exploration: Understanding Probability
• Data Exploration : Analyzing Categorical Data (ProcFreq) — Tasks
• Data Exploration : Hypothesis, Types of Errors
• Data Preparation: Arranging the data: Proc Sort, Proc Format-Tasks
• Data Preparation: Keeping, Dropping, Renaming, Transposing
• Data Preparation: Using SAS Functions  — Tasks
• Data Preparation: Conditional Processing, By group Processing -Tasks
• Data Preparation: Combining Data sets — Tasks
• Data Preparation: Do-Loops, Arrays
• Statistics: ProcFreq
• Statistics: ProcTtest, ProcAnova
• Proc Npar1way

Session 2

• Data Mining: Introduction
• Introduction to Regression: ProcCorr, ProcReg
• Dimensionality Reduction Techniques: Proc Factor
• Dimensionality Reduction Techniques: ProcPrincomp
• Dimensionality Reduction Techniques: ProcDiscrim
• Clustering: Introduction
• Clustering case study — Task
• Association Rules — Introduction
• Association Rules — Case study: Task
• Density Estimation: Proc KDE

Session 3

• ProcReg: Case study — Task
• ProcReg: Model Diagnostics  — Task
• Introduction to Logistic Regression
• Proc Logistic  -Case study, — Task
• Introduction to Decision Trees
• ProcDtree, Case study — Task
• Introduction to SVM, Naive Bayes, Case study
• Introduction to Neural nets,
• Neural Nets – the Case study
• Introduction to KNN, Case study  — Task
• Introduction to Bagging and Boosting
• Ensemble methods Case study
• Reinforcement Learning

Session 4

• Introduction to Time series
• Proc Arima — Case study — Task
• Introduction to Text Analytics
• Sentiment Analysis in R — Case study
• Introduction to Optimization
• Optimization — Case study