Big Data Program Curriculum

The Big Data Certificate Program meets the same high academic standards of Cal State East Bay executive programs, with a challenging interdisciplinary curriculum designed to prepare you to manage using data and analytics. Roll up your sleeves to solve real-world challenges and use practices developed by top companies

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Module 1

THE FOUNDATION OF LEARNING BIG DATA

Learn why Big Data is critical in today’s economy from a competitive standpoint. Focus on the market outlook and core characteristics of what to sell the C-suite to gain support for a certain strategy solution.​

• Discover the characteristics of Big Data and the four V’s (Volume, Velocity, Variety, Veracity).​
  
• Understand the future of Big Data with machine learning and artificial intelligence. 
  
• Identify implementation challenges; infrastructure, analytics platforms, data security, and qualified resources. 
  
• Learn about stakeholder buy-in and compliance, and how to get the C-Suite to agree with a strategy.

Module 2

PART 1: BIG DATA INFRASTRUCTURE OPTIONS

Develop a solid understanding of infrastructure to create solutions with a well-constructed architecture, offering either an on-premise, hosted, or cloud-based strategy.

• Uncover the five key components; collection, storage, processing power, analysis, visualization/output.
• Analyze the advantages of on-premise vs. hosted vs. cloud solutions.
• Review a case study in infrastructure options – how the world’s largest tech provider creates Big Data solutions.

PART 2: IDENTIFYING & SELECTING THE RIGHT RESOURCES

Create a plan to implement infrastructure, analysis strategy and security while developing the right team. Learn how one company builds their Big Data operation around some of the best resources available in the market.

• Examine the difference between management training vs. technical training.
• Learn how critical it is to have the “right” team to manage your solution and the roles needed to do it.
• Identify resource requirements for a successful solution, spot the fakers, and prevent possible disasters.
• Review a case study in resource selection – how one company selects the best resources for their operation.

Module 3

DATA ANALYTIC PLATFORMS: TOOLS OF THE INDUSTRY & HOW TO USE THEM

Develop a strategy to analyze the structured and unstructured data to produce business value. Discover how to analyze the data and what analytical models and commercial solutions are available for leverage.

• Understand the difference between Big Data analysis and business intelligence.
• Learn the types of tools needed including Hadoop, NoSQL and SQL.
• Identify analytical models including collection, analysis, visualization, integration, and development.
• Uncover analytics solutions including Qlik, Tableau, Splunk and Microsoft BI.

Module 4

MACHINE LEARNING, ARTIFICIAL INTELLIGENCE & BIG DATA ANALYTICS FOR FUTURE OPERATIONS​

Review predictive analytical models. Discover how artificial intelligence and machine learning will be critical to sustain a competitive advantage by analyzing data faster and leveraging the data for a decision support system.

• Expose the future operations of machine learning, artificial intelligence and Big Data analytics.
• Discover what artificial intelligence and machine learning are and how they are constructed.
• Learn how artificial intelligence and machine learning are used and what their future holds.

Module 5

CODING WORKSHIP & VISUALIZATION

Upon completion of the analytics and machine learning modules, you’ll take a hands-on approach and dive deeper into various coding languages that support Big Data. Review various applications to write, compile and test the code based on different case studies.​​​

Module 6

BIG DATA SECURITY

Analysts estimate data breaches will cost companies a global annual cost of more than $2.5 trillion by 2022. Learn why protecting your data and preventing data breaches is critical for corporate survival.

• Understand the role of data analytics in projecting staffing needs of an organization.
• Identify critical financial models using time series regression techniques and statistical classifiers.
• Discover the current challenges in security, the models for the challenges, and the future.
• Review a case study in securing a global company – prevention attempts and the aftermath of a breach.

Module 7

ETL METHODOLOGIES & STRATEGY

Uncover the best practices for reviewing system architectures, defining the relevant data between systems, creating a process mapping with keys, combining the data into a common platform, and visualization strategies.

• Review a custom case to identify system landscape.
• Create a process map and define data keys to build relationship.
• Identify aggregation models and visualization strategies.​

Module 8

STRATEGY

Develop the tactics to communicate persuasively and convince the C-suite of the value proposition of a well-structured Big Data solution.

• Build a Big Data proposal.
• Understand cost implications for a Big Data solution, the cost for not implementing, and valuating the ROI.
• Discover how long it takes to implement a Big Data solution and when to expect results.
• Sell it to the CEO – show them the pot of gold.

Capstone Project

The Big Data Portfolio

Illustrate your success at Cal State East Bay with a personalized, detailed action plan including your organization's goals and key performance indicators to develop an ROI strategy.

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