course | Big Data Analytics for Managers & Business Leaders

What is data, and how does it come into existence?
 

• Data lifecycle – From cradle to grave
• From data generation, data processing, and data analysis to data-driven insights
• Translating data insights into business insights
• Classification of data
• Structured vs. semi-structured vs. unstructured data
• Continuous vs. categorical data
• Nominal vs. ordinal data
• Demystifying data terminology
• Big Data -- Data Analytics -- Data Science -- Data Intelligence -- Data Insights

Big Data project overview
 

• Different stakeholders in a typical Big Data project
• Role of each stakeholder in the data landscape – Data Analysts vs. Data Scientists vs. Big Data Engineers
• Overview of Big Data and its proliferation over the last decade
• Big Data landscape overview
• Different tools and technologies that have come into existence for addressing Big Data
• Four dimensions of Big Data – Volume, Velocity, Variety, Veracity
• Functional nuances of the four V’s of Big Data
• Apache Software Foundation – The cornerstone of the modern open source software ecosystem
• Apache Foundation tools and technologies for Big Data

Introduction to the Hadoop ecosystem
 

• Overview of Hadoop, MapReduce, YARN, and Spark, and some of the other key components of the Big Data world · Different computing frameworks in the Big Data computing world
• Cluster computing vs. parallel computing vs. distributed computing
• Utility of each of the frameworks and application scenarios
• Building an analytics layer for Big Data problem statements

Data analytics for Big Data
 

• Analytics to explore and prepare data models
• Identify the type of analytics problem to apply the appropriate set of techniques
• Scenarios and Use Cases – Big Data with analytics and data science
• Delivering business benefit from Big Data
• Establishing the business importance of Big Data
• Data governance and cybersecurity

Application of Big data for analytical decision-making
 

• Data flow management and data pipelines
• How to design data pipelines for effective data flow
• Master data management
• Governance, policies, processes, standards, and tools
• Big Data modelling and management systems
• Why is a Big Data infrastructure plan needed
• Different types of databases – Relational Database Management System (RDBMS), data warehouse, data lakes, SQL vs. No-SQL
• Real-time streaming data vs. batch data - Cloud computing vs. on-premise computing
• Differences between a traditional database management system and a Big Data management system