Learned how to use Snowflake’s architecture, build SQL-based data pipelines, and design scalable data models. The course included hands-on labs where I queried data, managed schemas, and optimized storage.

Can apply these skills to build and maintain cloud-based ETL workflows in real-world analytics or BI projects.

This certification proves I can work with modern data warehousing tools and handle real-time data workflows efficiently.

Explored large language models, prompt engineering, and Google’s AI tools like Vertex AI. I also completed labs where I built and tested GenAI applications using real datasets.

Can use these skills to create AI-powered apps, chatbots, and content generation tools using Google Cloud.

This certification shows I understand how to responsibly develop and deploy GenAI solutions at scale.

Built a strong foundation in Python for data science, covering pandas, scikit-learn, statistics, and machine learning. Through projects and coding exercises, I practiced everything from data wrangling to model training.

Can apply this in business settings to analyze trends, make predictions, and drive decisions using data.

This certification shows that I can independently manage end-to-end data science workflows.

Learned to build ETL and ELT pipelines using Python libraries. I practiced extracting data from APIs, transforming it, and loading it into databases in hands-on exercises.

Can now automate data movement and transformation across systems—useful for any data engineering or analytics project.

This certification proves my ability to handle real-world data pipeline challenges using Python.

Learned the basics of generative AI, large language models, and real-world use cases in business. The course covered responsible AI, risks, and how GenAI fits into the Databricks ecosystem.

Can use these insights to guide GenAI adoption in enterprise tools, especially within the Lakehouse architecture.

This certification shows I understand the potential and limitations of GenAI from both a technical and business perspective.

Completed a job simulation involving AI-powered financial chatbot development for BCG's GenAI Consulting team.

Gained experience in Python programming, including the use of libraries such as pandas for data manipulation.

Integrated and interpreted complex financial data from 10-K and 10-Q reports, employing rule-based logic to create a chatbot that provides user-friendly financial insights and analysis.

Gained a solid understanding of the Databricks Lakehouse Platform, including Spark, Delta Lake, MLflow, and Unity Catalog. I passed an accreditation quiz that tested my grasp of platform features and architecture.

Can now support data workflows that integrate analytics, data engineering, and machine learning on a unified platform.

This certification validates that I can contribute meaningfully to projects using Databricks for data and AI development.