Big Data

Big Data refers to the massive volume of structured and unstructured data. The data is generated and collected at a high speed and demands advanced technologies for its storage, processing, and analysis.

Big Data enables organizations to gain insights and make informed decisions by leveraging the data to identify patterns and correlations.

This training program is a comprehensive educational program that teaches individuals technologies, and tools used in managing and analyzing large and complex data sets. It is a structured program designed to teach individuals the concepts and techniques of data science.

This course typically cover topics such as statistics, data analysis, machine learning, and data visualization. This course is available in various formats such as online classes, in-person classes, or self-paced learning.

This skill can apply in a wide range of fields and industries, including business, health care, and academia. It can be beneficial for individuals who want to pursue careers in data science, data engineering, or business intelligence, or for organizations looking to upskill their employees in technologies.

Goal & Target Audience:

• The goal of this course is to identify patterns, trends, and correlations in the data. The information is of use to make informed decisions and optimize business processes. The goal is to enable organizations to turn large and complex data sets into actionable insights that drive business value.

The target audience for this course includes:

• Data scientists and data analysts
• Data engineers
• Business intelligence professionals
• IT and technical professionals
• Overall, it is relevant to organizations and individuals who are looking to derive value from large and complex data sets.

Eligibility Criteria for Big Data:

• The eligibility criteria for this training program may vary but in general, most programs require the following:
• Educational Qualification: A minimum of a Bachelor’s degree in computer science, engineering, mathematics, statistics, or a related field, but not necessary.
• Programming Knowledge: Basic knowledge of programming languages such as Python, Java, and SQL is usually required, but not necessary.
• Familiarity with Databases: Knowledge of database concepts, such as normalization and schema design, is beneficial when working with large datasets.
• Mathematics and Statistics: Familiarity with mathematical and statistical concepts is helpful when working with data analysis and machine learning algorithms.
• Problem Solving and Logical Reasoning Skills: The ability to think logically and solve complex problems is essential when working with large and complex datasets.