Big Data and Analytics for Business Users

Course#: BSWA2186 - Next Public Course 20th July 2018

About this Course

Course Type Course Code Duration
Big Data BSWA2186 1 Day

Data is one of the most valuable assets that your organization possesses. Every day you are creating more data and potentially passing up opportunities to harvest that data and use it to accelerate the achievement of your organization’s strategic objectives. Big Data and Analytics represent an emerging trend around harvesting, analyzing, and capitalizing on the wealth of data that is within the grasp of your enterprise.

“For every 100 open Big Data jobs, there are only two qualified candidates” –

This one day primer introduces Cloud Computing, Big Data, and the emerging discipline of Data Analytics. Attention will be given to the three V’s of Big Data: Volume, Velocity, and Variety as well as the fourth V of Value. You’ll learn about these critical elements and the powerful value proposition that these capabilities provide. What are the processes, tools, and personnel that will be needed in order to take advantage of this sea change in information management? This essential course will equip you to understand your customers better and how to deliver more value today.

Why Attend this Course?

Provides a concise introduction to Bid Data for Business Analysts

What Makes this Course Stand Apart?

Hands on course, small groups, latest content.

What you will Learn?

Upon completion of this course, you will be able to:

Cloud Computing Basics
Introduction to Big Data
Understanding Data Analytics
Understanding Predictive Analytics
Basics of Analytical Modeling
Unpacking the Value, Volume, Velocity, and Variety
Organizational Considerations
Recommended Next Steps


Managers, Analysts, Architects, and Team Leads


No Prerequisites are required to attend this course.

Course Outline

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

2. Defining the Cloud

A Bit of History
Wikipedia Entry
Cloud Computing at a Glance
Gartner Research on Cloud
Electrical Power Grid Service Analogy
The NIST Perspective
Five Characteristics
On-demand Self-Service (NIST Characteristic)
Broad Network Access (NIST Characteristic)
Resource Pooling (NIST Characteristic)
Rapid Elasticity (NIST Characteristic)
Measured Service (NIST Characteristic)
The Three Cloud Service Models (NIST)
The Cloud Computing Spectrum: IaaS, PaaS and SaaS
The Four Cloud Deployment Models (NIST)
The NIST Cloud Definition Framework
A Hybrid Cloud Diagram
Cloud Deployment Model Dynamics

3. The Cloud Economics:

Cloud Value Proposition
Coping with Computing Demand the Traditional Way
Coping with Computing Demand the Cloud Way
Cloud economics
You Can Move Your Cloud Apps Closer to Your Clients!
Be Aware of What You Ask For!
Do Clouds Compute?
Total Cost of Ownership (TCO)
Cloud Infrastructure – Vendor Comparison
Select Expected Benefits
You Still Need …
Financial Management and Tracking
Calculate initial, simple return
Calculate Returns for on-going Usage
How to Practically Estimate Your Cloud Bill?
Shop Around (Within the Same Shop)
Discounted Object Storage: Amazon Glacier
Amazon S3 Cost Monitoring
Google Compute Engine Per-Minute Billing

4. What is NOSQL?

Limitations of Relational Databases
Limitations of Relational Databases (Cont’d)
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
Limitations of NoSQL Databases

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

5. 6. 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
Decision Trees (SL)
Naive Bayes Classifier (SL)
Naive Bayesian Probabilistic Model in a Nutshell
Unsupervised Learning Type: Clustering
K-Means Clustering (UL)
K-Means Clustering in a Nutshell
Time-Series Analysis
Decomposing Time-Series
Monte-Carlo Simulation (Method)
Who Uses Monte-Carlo Simulation?
Monte-Carlo Simulation in a Nutshell

7. 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
Hadoop-based Systems for Data Analysis
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
What is Hive?
Business Analytics with Hive

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