BIG DATA USAGE AND BUSINESS PRACTICES
Available Dates & Locations
COURSE OVERVIEW
The Introduction to Big Data Analytics course is designed for individuals seeking a career transition into the field of data science and analytics. This comprehensive course provides a solid foundation in the principles, techniques, and tools used in big data analytics. Participants will gain hands-on experience with industry-standard tools and technologies to process, analyze, and derive insights from large and complex datasets. By the end of the course, participants will be equipped with the essential knowledge and skills to embark on a successful journey as a data scientist.
COURSE OBJECTIVES
By completely attending this course, participants will be able to:
- Understand the fundamental concepts of big data analytics and its significance in various industries.
- Utilize essential tools and technologies for data collection, storage, and preprocessing.
- Perform exploratory data analysis to uncover patterns, trends, and outliers within datasets.
- Apply statistical methods and machine learning techniques for predictive and prescriptive analysis.
- Communicate data-driven insights effectively through data visualization and storytelling.
- Collaborate in a team-based environment to solve real-world data challenges.
- Develop critical thinking and problem-solving skills specific to big data scenarios.
TARGET COMPETENCIES
- Data Collection and Preprocessing
- Exploratory Data Analysis
- Statistical Analysis
- Machine Learning Fundamentals
- Predictive Analytics
- Data Visualization
- Big Data Technologies
- Ethics and Privacy
This course is ideal for individuals who are looking to transition their careers into the field of data science and analytics. It is suitable for professionals from non-technical backgrounds interested in exploring data science as a career. Business analysts aiming to enhance their analytical capabilities. IT professionals interested in expanding their skill set to include big data technologies.
The course will be delivered through a combination of instructor-led lectures and demonstrations. Hands-on lab sessions to practice concepts using real-world datasets. Group discussions and case studies for practical application. Assignments and projects to reinforce learning.
