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Course Code
DSBA-004
Building Advanced Data - Driven Solutions - Analytics and AI Applications
- Leveraging the basic concepts, skills and knowledge on Big Data, Data Science and AI models, this course covers two major areas: advanced data-driven solutions and architecture and developing real-life end-to-end applications as per the recent industry trends. The course is interactive hands-on practices, programming and development intensive.
- Through series of use cases, advanced data-driven technologies and applications are introduced including working with data ingestion scenarios for streaming and near real-time, complex data modeling, applying workflows and pipelines, multi-engine processing, big graph models and massive dynamic columnar.
- The use cases cover several verticals including online web analysis, financial services, traffic management, network analysis, data warehousing transformation, text analytics and edge AI. Trainees will realize the application of data science methods and AI models on top of advanced Big Data analytics and applications platforms.
Learning Outcomes
Outcomes
Course Contents
- Advanced Big Data Technology Stack: Motivations and Usage
- First Look at Common Use Cases for Advanced Analytics and AI Applications
- Design Patterns and Architecting Solutions in Practice
- Programing Languages and Frameworks for Implementation
- Data Science and AI Models Multiprocessing Implementation
- Complex Data Modeling, Transformation and Ingestion
- Working with Streaming and Near/Real-time Applications
- Data Flow Engines, Pipelines and Event Processing Technologies
- Working with Big Graph Modeling and Processing
- Industry Patterns & Practices for Implementing Data-Driven Solutions
- Use Cases Covered during the Lectures, Demos, Labs, Projects Assignments:
- Clickstream Analysis for Online Services and Web Applications
- Data Warehousing Transformation and Extension
- Predicting Forest Cover with Decision Trees
- Anomaly Detection in Network Traffic with K-means Clustering
- Understanding Wikipedia with Latent Semantic Analysis
- Analyzing Co-occurrence Networks with GraphX
- Geospatial and Temporal Data Analysis on Taxi Trip Data
- Estimating Financial Risk through Monte Carlo Simulation
- Edge AI for Internet of Things in Autonomous Vehicles
- Finalization of the Course Project
- Final Review and Conclusions
Our Methodology
- Make coaching and monitoring innovative and using modern
- Media training also using on the go training by using interactive means and focusing on
- The exercises, practical applications and real situations study
- Live delivery method, instructor-led training
- Experienced consultant, trainers, and professional
- Qualified trainer with high-level experience
Attendance Reports
- Send daily attendance reports to training departments
- Send full attendance report to training dep. by the end of the course
- Attend 100 % from the course days also provide daily
- Issue attendance certificate for participant who attend minimum 80% from the course duration
Pre/Post Reports
- Pre- assessment before starting training
- Post assessment after finish training
- Full report for the deferent between Pre-& Post assessment
Who Should Attend
- Managers, Heads of Departments, and Directors
- Data Scientists and Data Analysts
