In today's digital age, organizations need to leverage sharp and timely data insights across all areas of their operations to maintain a competitive edge. Big data has emerged as a transformative force that disrupts traditional decision-making approaches. When utilized effectively, big data enables the creation of accurate business models and forecasts, empowering leaders to make informed decisions across all aspects of their organization.

This course is designed to equip participants with the necessary data literacy skills to enhance their efficiency, effectiveness, and ability to stay ahead of the curve. Participants will learn the why, where, and how of deploying technologies and methodologies such as big data, Hadoop, data analytics, and data science. By understanding these tools and techniques, participants will be able to harness the power of data to drive better decision-making and gain a competitive advantage in their respective fields.

Course Objectives:

By the end of the training, participants will be able to:

Illustrate the benefits, functionality, and ecosystem of big data
Lead a big data initiative within their organization and generate organizational value by adopting data analytics
Create well-rounded big data analytics teams by identifying the essential data professional roles
Apply advanced analytics methodologies to optimize their line of business and solve complex business problems
Leverage free, open-source applications and open data to deliver insights that generate an organizational competitive advantage

Who Should Attend?

This course is intended for working professionals who seek to use enterprise data to achieve better, more efficient business results and/or to make improved decisions through forecasting. This includes experienced data professionals such as Database Administrators, System Administrators, Business Analysts or Business Intelligence Specialists, as well as less technically-inclined management and administrative professionals.

Course Outlines:

The Big Data Landscape Introduction & Overview

What is Big Data?
Big data vs. its predecessors
How big data relates to data analytics and data science
The big data paradigm
Big data professional roles
How big data projects benefit businesses and industries
The Hadoop ecosystem and architecture
Other technologies in the big data paradigm

Big Data Project Planning

Beyond the Hadoop ecosystem
Other popular projects by MapR
Commercial distributions of Hadoop
Security within Hadoop
Data engineering
Useful programming languages
The 4-step big data planning process
Staying competitive as a big data professional

Advanced Analytical Methods for Problem-Solving

The nature of data science and analytics
Fraud prevention in real-time using machine learning
Online sales improvement through recommendation engines
Customer churn prediction and reduction through logistic regression
Best option selection using multi-criteria decision making
Stock price predictions using Markov Chains
Analyzing how price changes impact sales volumes using simple linear regression

Basic Data Science Mechanics

The benefits of object-oriented programming
Programming Python
R programming for data science
Where is your data coming from?
The traditional relational database management system (RDBMS – DSFD) source
Structured Query Language (SQL) in analytics and data science
Making the value of location data with Geographic Information System (GIS)
Machine learning
Popular machine learning algorithms

Free Resources to Analyze Data and Communicate Findings

Free applications for data science and analytics
Context and benchmarking using free and open data
Scraping the web for market data
The different types of data visualization
Three simple steps to design for your audience
Data graphics
Design styles to convey powerful messages
Design data analytics dashboards

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