Digital Data Maturity

Roadmap 8: Data Systems and AI Integration Specialist

This roadmap is created to guide you through the challenging process of applying advanced data systems and artificial intelligence (AI) technologies in a range of operational settings. For this role, it is essential to have practical experience with data structures, machine learning models, and their applications in streamlining corporate operations and decision-making. Students will research the principles of data management, methods for applying AI, and the most recent technological advancements that have the potential to transform data into insightful understandings.

The journey is structured to build progressively, starting from foundational knowledge in databases and moving toward advanced topics like AI, ML, and cloud technologies. Through this plan, you can trace your technological evolution, gaining the skills and insights necessary to innovate and excel in the integration of technology within business strategies.

As you proceed through this roadmap, you acquire the skills necessary to deal with large datasets, develop AI-driven solutions, and ensure that these technologies align and complement the organization’s strategic goals with the aid of this comprehensive lesson plan. It guarantees a comprehensive grasp of the theoretical and applied aspects of modern artificial intelligence and data systems. The basics of data infrastructure and complex AI integration strategies are all addressed. The goal is to provide students with the skills they need to effectively use these resources to foster innovation and productivity in a hectic work environment.

Module 1: Foundations of Data and Technology

  • Objective: Gain a foundational understanding of data types, structures, and the basic principles of technology that drive modern data platforms.
  • Topics:
    • Introduction to Big Data and Data Explosion
    • Understanding Data: Types, Structures, and Significance
    • Fundamentals of Information Retrieval
    • Basics of Cloud Computing and Deployment Models
    • Introduction to APIs and System Architecture
  • Activities: Creating data flow diagrams and entity-relationship diagrams for applications

Module 2: Advancing to Big Data and Analytics

  • Objective: Understand the concepts of Big Data and how analytics can be used to derive actionable insights for business strategies.
  • Topics:
    • Introduction to Big Data
    • Overview of Big Data technologies (Hadoop, Apache Spark)
    • Data analytics fundamentals
  • Activities: Analyzing a dataset using Big Data tools to identify sales trends and opportunities.

Module 3: Introduction to Programming for Data Manipulation

  • Objective: Develop basic programming skills necessary for data manipulation and analysis.
  • Topics :
    • Basics of programming with a focus on Java, Python for data science
    • Integration of programming skills in data analysis.
  • Activities: Building a simple data analysis tool using Python.

Module 4: Leveraging Cloud Technologies in Business

  • Objective: Explore cloud computing concepts and how cloud technologies can be utilized for business scalability and agility.
  • Topics:
    • Cloud computing fundamentals,
    • AWS Cloud Practitioner essentials
    • Cloud services for business operations
  • Activities: Deploying a basic web application on AWS.

Module 5: Business Enablement Technologies

  • Objective: Understand the role of technology in business processes and how to select and implement the right tools.
  • Topics:
    • Sales and Marketing: Overview of sales enablement platforms, CRM systems, content management systems, and analytics tools. Overview of marketing engagement and lead generation platforms, content creation, and collaboration/proof tools.
    • Operations: Overview of Enterprise Resource Planning (ERP) systems: Understand the integrative role of ERP systems in managing core business processes, including procurement, manufacturing, service delivery, and inventory management. Explore technologies that optimize supply chain visibility, efficiency, and logistics, including warehouse management systems (WMS) and transportation management systems (TMS).
    • Business Intelligence (BI) and Analytics: An overview of BI tools for data analysis and decision support systems that help visualize trends and business metrics for strategic planning.
  • Activities: Comparing different sales enablement platforms and developing a technology adoption plan.

Module 6: Artificial Intelligence and Machine Learning

  • Objective: Dive into AI and ML technologies, focusing on their application in sales and marketing.
  • Topics:
    • AI fundamentals
    • introduction to machine learning
    • NLP integration
    • Using Hugging Face Transformers, BERT, text tokenization.
  • Activities: Machine Learning with Elasticsearch – Practical Examples

Module 7: Deploying AI for Business Impact

  • Objective: Learn the basic use cases associated with AI deployments
  • Topics:
    • Basics of Machine Learning and Its Models
    • LLM Training
    • Retrieval Augmented Generation
    • Integrating AI with Large Language Models (LLMs) and Elasticsearch
    • Utilizing NLP for Data Processing and Insights,
    • Implementing AI for Predictive Analytics and Decision Making
  • Activities: Creating a simple AI model to analyze and predict customer buying behavior.

Module 8: Full-Stack Cloud Migration and Application Development

  • Objective: Gain comprehensive knowledge in developing and migrating applications to the cloud, covering both front-end and back-end technologies.
  • Topics:
    • SDLC phases
    • Cloud migration strategies
    • DevOps methods for continuous deployment
    • Testing frameworks and methods
    • ITIL standards and libraries for service management
    • Full-stack development, including web technologies and databases.
  • Activities: Developing and deploying a full-stack application on the cloud using containerization technologies.

Module 9: Advanced Data Management and Visualization

  • Objective: Master advanced techniques in data management and learn how to visualize data effectively.
  • Topics:
    • Advanced database management with PostgreSQL and MongoDB
    • Using Elasticsearch for search and insight
    • Ingesting and indexing data from various sources
    • Data visualization with Tableau.
  • Activities: Building a dashboard for sales data analysis using Tableau.

Module 10: Real-time Processing and Search Technologies

  • Objective: Learn the techniques of real-time data processing and the application of search technologies in business intelligence.
  • Topics:
    • Real-Time vs. Batch Data Processing
    • Elasticsearch for High-Performance Searches
    • Lucene, SoLR, Elastic & Cloud Search Provider Comparisons
    • Building Efficient ETL Processes for Data Management
    • Search Engines and Information Retrieval Mechanisms

Module 11: Data Engineering and Development

  • Objective: Acquire the skills to build robust data engineering solutions, including data modeling, pipelines, and database management.
  • Topics:
    • Data Modeling and Database Design Principles
    • Building Data Pipelines for Scalable Solutions
    • Advanced Database Management: SQL and NoSQL Solutions
    • Cloud Monitoring and Telemetry with Metrics, Logs, and Traces
    • Microservices and Containerization for Flexible Software Deployment

Final Project: Integrating Data Technologies into Business Strategy

  • Objective: Combine all learned skills to create a comprehensive strategy for integrating data technologies into a business environment.
  • Topics:
    • Strategy Development for Data-Driven Business Solutions
    • Creating Value with Data Insights and Analytics
    • Case Study: Digital Transformation with AI and Big Data
    • Designing a Scalable and Resilient Data Infrastructure
    • Future Trends in Data Technology and Implications for Business

Visual Roadmap:

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