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Big Data Analytics and Business Intelligence Fundamentals Training

  • Training Duration:
  • 3 Days
  • Training Environment:
  • On-site at the Institution
  • In DVA Classrooms
  • Online
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About the Training

The Big Data Analytics and Business Intelligence Fundamentals Training is highly valuable. Participants will learn the fundamentals of big data analytics and explore business intelligence tools and techniques. This knowledge plays a critical role in improving business processes.

The training focuses on data collection and processing methods. Participants will learn how to collect and analyze data, as well as discover how to extract meaningful insights from it. This process enables businesses to make more informed decisions, creating a competitive advantage.

Business intelligence is a key component of the training. Participants will learn how to use business intelligence software, which simplifies data visualization and reporting. It also helps in identifying trends and patterns, thus supporting strategic planning processes.

The training places great emphasis on practical applications. Participants will work on real-world scenarios, reinforcing the theoretical knowledge they have acquired. At the same time, they will develop their skills in big data analytics and business intelligence, providing a significant advantage in their careers.

The Big Data Analytics and Business Intelligence Fundamentals Training provides participants with a deep understanding, enhancing their ability to think data-driven. The training fosters expertise in data analytics and business intelligence, enabling participants to help businesses develop data-driven strategies.

In conclusion, this training opens the door to the world of big data and business intelligence. Participants will learn the fundamentals of data analytics and business intelligence, which will allow them to work more effectively and efficiently. By the end of the training, participants will be equipped to play significant roles in data-driven decision-making processes. These skills will greatly contribute to their professional development.

What Will You Learn?

  • Big Data Concepts and Technologies: Big data technologies like Hadoop and Spark.
  • Data Analytics: Data mining, exploratory data analysis, and basic statistics.
  • Business Intelligence Tools and Techniques: Usage of BI tools and data visualization.
  • Fundamentals of Machine Learning: Supervised and unsupervised learning, modeling.
  • Visual Reporting: Creating dashboards and visual data presentation.
  • Data Management and Quality: Data cleaning, integration, and transformation.
  • Real-World Applications: Case studies and project-based learning.

Prerequisites

  • Basic programming knowledge (Python, SQL preferred).
  • Fundamental understanding of data structures and algorithms.
  • Familiarity with statistics and probability concepts.

Who Should Attend?

  • Data Analysts and Scientists.
  • Business Intelligence Professionals.
  • Software Developers and Data Engineers.
  • Anyone involved in data-driven decision-making processes.

Outline

Introduction: Big Data and Business Intelligence Concepts
  • The importance and components of big data.
  • Fundamental concepts of business intelligence and data analytics.
Big Data Technologies
  • Big data processing frameworks like Hadoop and Spark.
  • Data storage and processing techniques.
Data Analytics
  • Data mining techniques and tools.
  • Exploratory data analysis and basic statistics.
Business Intelligence Tools and Techniques
  • Usage and applications of BI tools.
  • Effective data visualization and reporting techniques.
Fundamentals of Machine Learning
  • Supervised and unsupervised learning models.
  • Basic machine learning algorithms and applications.
Visual Reporting
  • Dashboard design and creation.
  • Visual data presentation and interactive reports.
Data Management and Quality
  • Data cleaning, integration, and transformation techniques.
  • Best practices in data quality and management.
Practical Examples and Case Studies
  • Hands-on exercises with real-world datasets.
  • Industry-specific case studies and project-based learning.

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