Certified Big Data and Data Analytics Practitioner (CBDDAP)

Certified Big Data and Data Analytics Practitioner (CBDDAP)

Course Description

Introduction

Welcome to the Certified Big Data and Data Analytics Practitioner (CBDDAP) training course, meticulously crafted by Cambridge for Global Training. In today's data-driven world, organizations are increasingly turning to big data and analytics to gain insights, make informed decisions, and drive innovation. This course is designed to equip participants with the knowledge and skills necessary to excel as practitioners in the field of big data and data analytics. Through hands-on learning and practical applications, participants will learn how to leverage big data technologies, apply advanced analytics techniques, and extract valuable insights from large datasets.

Course Objectives

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

• Master big data technologies and tools for data processing and analysis.
• Apply advanced analytics techniques to extract insights and patterns from large datasets.
• Develop proficiency in machine learning and predictive analytics.
• Utilize data visualization tools to communicate findings effectively.
• Implement big data solutions in real-world scenarios to solve business problems.
• Earn certification as a Certified Big Data and Data Analytics Practitioner upon successful completion of the course and examination.

Who Should Attend

• Data Scientists
• Data Analysts
• Business Intelligence Professionals
• IT Professionals
• Managers and Executives involved in decision-making
• Anyone interested in advancing their skills in big data and data analytics.

Course Outline

Unit 1: Introduction to Big Data and Data Analytics

• Understanding big data and its significance
• Overview of big data technologies (e.g., Hadoop, Spark)
• Introduction to data analytics and its applications
• Hands-on exercises in processing and analyzing big data

Unit 2: Big Data Technologies and Tools

• Working with distributed file systems (e.g., HDFS)
• Processing and analyzing big data with MapReduce and Spark
• Using SQL and NoSQL databases for big data storage and retrieval
• Introduction to cloud-based big data platforms (e.g., AWS, Azure)
• Hands-on projects in setting up and running big data processing jobs

Unit 3: Advanced Analytics Techniques

• Predictive analytics and machine learning fundamentals
• Supervised, unsupervised, and semi-supervised learning algorithms
• Model evaluation and validation techniques
• Feature engineering and selection
• Case studies on applying advanced analytics techniques to real-world datasets

Unit 4: Data Visualization and Interpretation

• Principles of data visualization and storytelling
• Using libraries like Matplotlib, Seaborn, and Plotly for data visualization
• Creating interactive dashboards with tools like Tableau and Power BI
• Designing effective data visualizations for different audiences
• Communicating insights and findings to stakeholders

Unit 5: Big Data Applications and Use Cases

• Case studies on big data applications in various industries (e.g., finance, healthcare, retail)
• Analyzing social media data for sentiment analysis and customer insights
• Predictive maintenance in manufacturing using IoT data
• Fraud detection and risk management in financial services
• Hands-on projects on building big data solutions for specific use cases