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Home > Training > Big Data > Applied Data Science and Big Data Analytics Boot Camp for Business Analysts Training

Applied Data Science and Big Data Analytics Boot Camp for Business Analysts Training

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Course#: WA2262

Business success in the information age is predicated on the ability of organizations to convert massive amount of raw data coming from various sources into high-grade business information.

Many organizations are overwhelmed by the sheer volume of information they have to process in order to stay competitive.  Traditional database systems may become either prohibitively expensive to handle the exponential growth of data volumes or found unsuitable for the job.  Data Science and Big Data Analytics represent an emerging discipline that helps get a handle on the situation and capitalize on the wealth of information assets within your organization.


This intensive training course provides theoretical and technical aspects of Data Science and Business Analytics.  The course covers the fundamental and advanced concepts and methods of deriving business insights from Big Data.  The course is supplemented by hands-on labs that help attendees reinforce their theoretical knowledge of the learned material.


  • NoSQL and Big Data Systems Overview
  • Big Data Business Intelligence and Analytics
  • Applied Data Science and Business Analytics
  • Algorithms, Techniques and Common Analytical Methods
  • Machine Learning
  • Visualizing and Reporting Processed Results
  • Data Analysis with R


Business Analysts, IT Architects and Managers


Participants should have the general knowledge of statistics and programming


3 Days

Outline of WA2262 Applied Data Science and Big Data Analytics Boot Camp for Business Analysts Training

Chapter 1. Defining Big Data

  • Transforming Data into Business Information
  • Quality of Data
  • Gartner's Definition of Big Data
  • More Definitions of Big Data
  • Processing Big Data
  • Challenges Posed by Big Data
  • The Cloud and Big Data
  • The Business Value of Big Data
  • Big Data: Hype or Reality?
  • Big Data Quiz
  • Big Data Quiz Answers
  • Summary

Chapter 2. What is NoSQL?

  • Limitations of Relational Databases
  • Limitations of Relational Databases (Con't)
  • Defining NoSQL
  • What are NoSQL (Not Only SQL) Databases?
  • The Past and Present of the NoSQL World
  • NoSQL Database Properties
  • NoSQL Benefits
  • NoSQL Database Storage Types
  • The CAP Theorem
  • Mechanisms to Guarantee a Single CAP Property
  • Limitations of NoSQL Databases
  • Big Data Sharding
  • Sharding Example
  • Quiz
  • Quiz Answers
  • Summary

Chapter 3. NoSQL Systems Overview

  • MongoDB
  • MongoDB Features (Cont'd)
  • MongoDB Operational Intelligence
  • MongoDB Use Cases
  • Amazon S3
  • Amazon Storage SLAs
  • Amazon Glacier
  • Amazon S3 Security
  • Data Lifecycle Management with Amazon S3
  • Amazon S3 Cost Monitoring
  • OpenStack
  • Object Store (Swift)
  • Components of Swift
  • Google BigTable
  • BigTable-based Applications
  • BigTable Design
  • Google Cloud Storage
  • Hadoop
  • Hadoop Clusters
  • Hadoop's Core Components
  • Hadoop Distributed File System
  • Accessing HDFS
  • Communication inside HDFS
  • HBase
  • HBase Design
  • HBase Design
  • MemcacheDB
  • Using MemcacheDB instead of memcached
  • Apache Cassandra
  • Apache Cassandra Design
  • Cassandra's Main Features and Qualities of Service
  • Summary

Chapter 4. Big Data Business Intelligence and Analytics

  • Traditional Business Intelligence and Analytics
  • OLAP Tasks
  • Data Mining Tasks
  • Big Data / NoSQL Solutions
  • NoSQL Data Querying and Processing
  • MapReduce Defined
  • MapReduce Explained
  • Example of Map & Reduce Operations using JavaScript
  • Hadoop
  • Hadoop-based Systems for Data Analysis
  • Hadoop's MapReduce
  • Hadoop's Streaming MapReduce
  • Streaming Use Cases
  • Setting up Java Classpath for Streaming Support
  • Making things simpler with Hadoop Pig Latin
  • Pig Latin Script Example
  • SQL Equivalent
  • Amazon Elastic MapReduce
  • Big Data with Google App Engine (GAE)
  • GAE Dashboard
  • Example of Google AppEngine Java Datastore API
  • MongoDB Data Model
  • MongoDB Query Language (QL)
  • The
  • find
  • and
  • findOne
  • Methods
  • The
  • find
  • and
  • findOne
  • Methods
  • A MongoDB QL Example
  • What is Hive?
  • Hive Architecture
  • Interfacing with Hive
  • Hive Data Definition Language
  • Business Analytics with Hive
  • The UnQL Specification
  • Quiz
  • Quiz Answers
  • Summary

Chapter 5. Applied Data Science

  • What is Data Science?
  • Data Science Ecosystem
  • Data Mining vs. Data Science
  • Business Analytics vs. Data Science
  • Who is a Data Scientist?
  • Data Science Skill Sets Venn Diagram
  • Data Scientists at Work
  • Examples of Data Science Projects
  • An Example of a Data Product
  • Applied Data Science at Google
  • Data Science Gotchas
  • Summary

Chapter 6. Data Analytics Life-cycle Phases

  • Big Data Analytics Pipeline
  • Data Discovery Phase
  • Data Harvesting Phase
  • Data Priming Phase
  • Model Planning Phase
  • Model Building Phase
  • Communicating the Results
  • Production Roll-out
  • Summary

Chapter 7. Getting Started with R

  • Introduction
  • Positioning of R in the Data Science Arena
  • R Integrated Development Environments
  • Running R
  • Ending the Current R Session
  • Getting Help
  • Getting System Information
  • General Notes on R Commands and Statements
  • R Data Structures
  • R Objects and Workspace
  • Assignment Operators
  • Assignment Example
  • Arithmetic Operators
  • Logical Operators
  • System Date and Time
  • Operations
  • User-defined Functions
  • User-defined Function Example
  • R Code Example
  • Type Conversion (Coercion)
  • Control Statements
  • Conditional Execution
  • Repetitive Execution
  • Repetitive execution
  • Built-in Functions
  • Reading Data from Files into Vectors
  • Example of Reading Data from a File
  • Writing Data to a File
  • Example of Writing Data to a File
  • Logical Vectors
  • Character Vectors
  • Matrix Data Structure
  • Creating Matrices
  • Working with Data Frames
  • Matrices vs Data Frames
  • A Data Frame Sample
  • Accessing Data Cells
  • Getting Info About a Data Frame
  • Selecting Columns in Data Frames
  • Selecting Rows in Data Frames
  • Getting a Subset of a Data Frame
  • Sorting (ordering) Data in Data Frames by Attribute(s)
  • Applying Functions to Matrices and Data Frames
  • Using the apply() Function
  • Example of Using apply()
  • Executing External R commands
  • Listing Objects in Workspace
  • Removing Objects in Workspace
  • Saving Your Workspace
  • Loading Your Workspace
  • Getting and Setting the Working Directory
  • Getting the List of Files in a Directory
  • Diverting Output to a File
  • Batch (Unattended) Processing
  • Importing Data into R
  • Exporting Data from R
  • Standard R Packages
  • Extending R
  • CRAN Page
  • Summary

Chapter 8. R Statistical Computing Features

  • Statistical Computing Features
  • Descriptive Statistics
  • Basic Statistical Functions
  • Examples of Using Basic Statistical Functions
  • Non-uniformity of a Probability Distribution
  • Writing Your Own skew and kurtosis Functions
  • Generating Normally Distributed Random Numbers
  • Generating Uniformly Distributed Random Numbers
  • Using the summary() Function
  • Math Functions Used in Data Analysis
  • Examples of Using Math Functions
  • Correlations
  • Correlation Example
  • Testing Correlation Coefficient for Significance
  • The cor.test() Function
  • The cor.test() Example
  • Regression Analysis
  • Types of Regression
  • Simple Linear Regression Model
  • Least-Squares Method (LSM)
  • LSM Assumptions
  • Fitting Linear Regression Models in R
  • Example of Using lm()
  • Confidence Intervals for Model Parameters
  • Example of Using lm() with a Data Frame
  • Regression Models in Excel
  • Multiple Regression Analysis
  • Finding the Best-Fitting Regression Model
  • Comparing Regression Models
  • Summary

Chapter 9. Data Science Algorithms and Analytical Methods

  • Supervised vs Unsupervised Machine Learning
  • Supervised Machine Learning Algorithms
  • Unsupervised Machine Learning Algorithms
  • Choose the Right Algorithm
  • Life-cycles of Machine Learning Development
  • Classifying with k-Nearest Neighbors (SL)
  • k-Nearest Neighbors Algorithm
  • k-Nearest Neighbors Algorithm
  • The Error Rate
  • Decision Trees (SL)
  • Decision Tree Terminology
  • Decision Trees in Pictures
  • Decision Tree Classification in Context of Information Theory
  • Information Entropy Defined
  • The Shannon Entropy Formula
  • The Simplified Decision Tree Algorithm
  • Using Decision Trees
  • Naive Bayes Classifier (SL)
  • Naive Bayesian Probabilistic Model in a Nutshell
  • Bayes Formula
  • Classification of Documents with Naive Bayes
  • Unsupervised Learning Type: Clustering
  • K-Means Clustering (UL)
  • K-Means Clustering in a Nutshell
  • Regression Analysis
  • Simple Linear Regression Model
  • Linear vs Non-Linear Regression
  • Linear Regression Illustration
  • Major Underlying Assumptions for Regression Analysis
  • Least-Squares Method (LSM)
  • Locally Weighted Linear Regression
  • Regression Models in Excel
  • Multiple Regression Analysis
  • Regression vs Classification
  • Time-Series Analysis
  • Decomposing Time-Series
  • Monte-Carlo Simulation (Method)
  • Who Uses Monte-Carlo Simulation?
  • Monte-Carlo Simulation in a Nutshell
  • Monte-Carlo Simulation Example
  • Monte-Carlo Simulation Example
  • Summary

Chapter 10. Visualizing and Reporting Processed Results

  • Data Visualization
  • Data Visualization in R
  • The ggplot2 Data Visualization Package
  • Creating Bar Plots in R
  • Creating Horizontal Bar Plots
  • Using barplot() with Matrices
  • Using barplot() with Matrices Example
  • Customizing Plots
  • Histograms in R
  • Building Histograms with hist()
  • Example of using hist()
  • Pie Charts in R
  • Examples of using pie()
  • Generic X-Y Plotting
  • Examples of the plot() function
  • Dot Plots in R
  • Saving Your Work
  • Supported Export Options
  • Plots in RStudio
  • Saving a Plot as an Image
  • The BIRT Project
  • Visualization with D3 JavaScript Library
  • Examples of D3 Visualization
  • JavaFX
  • Data Visualization with JavaFX
  • Summary

Chapter 11. Apache Mahout

  • What is Apache Mahout?
  • Main Use Cases
  • Supported Algorithms in Classification
  • Supported Algorithms in Clustering
  • The Stable Set of Algorithms
  • Running Mahout on Amazon
  • Summary

Chapter 12. Machine Learning with BigML

  • What is BigML?
  • How BigML Service Works
  • Data Files
  • Data Sets
  • Data Sets Example
  • Models
  • Predictions
  • The Prediction UI Form
  • Text Analysis in BigML
  • Summary

Chapter 13. The Semantic Web

  • Defining the Term "Semantic"
  • Metadata in HTML Pages
  • Defining the Semantic Web
  • The Original Web Proposal
  • W3C and the Semantic Web
  • The Semantic Web as Web 3.0
  • The Semantic Web Stack
  • Ontology and OWL
  • The Smart Data Continuum
  • Resource Description Framework
  • RDF Model
  • An RDF Example
  • SPARQL Example
  • Microformat
  • Example of the hCard Microformat
  • Summary
Address Start Date End Date
Instructor Led Virtual 06/12/2017 06/14/2017
Instructor Led Virtual 09/18/2017 09/20/2017
Instructor Led Virtual 11/13/2017 11/15/2017
We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.
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