Big Data and Analytics for Business Users – GTBD9
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.
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.
- 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
There are no prerequisites for this course. If you have any queries or are in doubt about your suitability, please contact us and we will assist you.
Suggested Follow on Courses.
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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
Chapter 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
Chapter 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
Chapter 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
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
Chapter 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
Chapter 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