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Certificate in Big Data and Analytics

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Certificate in Big Data and Analytics


  • Participants undergo a series of practical exercises to apply and test acquired methodologies and practical approaches throughout the course.
  • Participants will be able to produce a comprehensive big data plan for implementation, which can be used for enterprise-wide big data deployment.

Course Objectives:

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

  • Explain the benefits, functions, and ecosystem of big data.
  • Initiate and lead big data initiatives within the organization and generate organizational value through data analytics.
  • Build close-knit teams of big data analysts by identifying key roles for data specialists.
  • Apply advanced analytical methodologies to improve business performance and solve complex business problems.
  • Utilize free applications, resources, and open data to generate organizational competitive advantage.

Target Audience:

This course is designed for professionals who want to leverage enterprise data to achieve highly efficient business outcomes and make improvement decisions through forecasting. It includes professionals experienced in data analysis, such as database managers, system administrators, business analysts, business intelligence specialists, and management professionals.

Program Content:

  • Overview of big data.
  • What is big data?
  • Big data compared to previous data paradigms.
  • The connection between big data, data science, and scientific data.
  • Big data architecture.
  • Roles of big data specialists.
  • How big data benefits companies and industries.
  • Hadoop ecosystem and architectural engineering.
  • Other technologies in the big data landscape.

Planning Big Data Projects:

  • Beyond the Hadoop ecosystem.
  • Popular MapR projects.
  • Commercial distributions of Hadoop.
  • Security within Hadoop.
  • Data engineering.
  • Useful programming languages.
  • 4-step process for planning big data.
  • Staying competitive in the big data field.

Advanced Analysis Methods for Problem Solving:

  • Nature of scientific data and analytics.
  • Real-time fraud prevention using machine learning.
  • Improving online sales through recommendation engines.
  • Predicting customer behavior and reducing logistics costs.
  • Choosing the best option using multiple criteria decision-making.
  • Stock price forecasting using Markov chains.
  • Analyzing price changes and their impact on sales using simple linear regression.

Mechanics of Core Data Science:

  • Benefits of object-oriented programming.
  • Python programming.
  • R programming for data science.
  • Data sources.
  • Traditional relational database management system (RDBMS) in analytics and scientific data.
  • Value of geographic information system (GIS) data.
  • Machine learning.
  • Common machine learning algorithms.

Free Resources for Data Analysis and Insights:

  • Free applications for data science and analytics.
  • Contextualization and measurement using open and free data.
  • Online market data discovery.
  • Different types of visualization data.
  • Three simple steps to build your audience.
  • Data visualization.
  • Design methods for impactful messaging.
  • Designing dashboards for data analysis.

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