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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, giving them the opportunity to apply and test the methodologies and practical techniques acquired during the course. This enables them to produce a comprehensive big data plan for implementation, which can be used for deploying big data at an enterprise level.

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, generating organizational value through data analytics.
  • Build teams of 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 advantages.

Target Audience

This course is designed for professionals who want to use enterprise data to achieve highly efficient business results and make informed decisions through prediction. This includes data analysis professionals such as database administrators, system administrators, business analysts, business intelligence specialists, and management professionals.

Training Program Content:

  • An overview of big data.
  • What is big data?
  • Big data compared to its predecessors.
  • How big data relates to scientific data and analytics.
  • The big data ecosystem.
  • Roles of big data specialists.
  • How big data benefits companies and industries.
  • Hadoop ecosystem and architecture.
  • Other technologies in the big data landscape.
  • Planning big data projects.
  • Beyond the Hadoop ecosystem.
  • Popular MapR projects.
  • Commercial distributions for Hadoop.
  • Security within Hadoop.
  • Data engineering.
  • Useful programming languages.
  • 4-step process for planning big data.
  • Staying competitive in the field of big data.
  • Advanced analysis methods for problem-solving.
  • Nature of scientific data and analytics.
  • Real-time fraud detection using machine learning.
  • Improving online sales through recommendation engines.
  • Predicting customer behavior and reducing logistic downtime.
  • Selecting the best option using multiple criteria decision making.
  • Stock price prediction using Markov chains.
  • Analyzing price elasticity and its impact on sales volume using simple linear regression.
  • Fundamentals of data science mechanics.
  • Benefits of object-oriented programming.
  • Python programming.
  • R programming for data science.
  • Data sources.
  • Traditional relational database management system (RDBMS) data source.
  • Structured Query Language (SQL) in analytics and scientific data.
  • Added value of geographic information system (GIS) data.
  • Machine learning.
  • Common machine learning algorithms.
  • Free resources for data analysis and finding insights.
  • Free applications for data science and analytics.
  • Contextualizing and measuring using free and open data.
  • Market data discovery online.
  • Different types of data visualization.
  • Three simple steps to build your audience.
  • Data visualizations.
  • Design techniques for impactful messaging.
  • Dashboard design for data analysis.

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