Certificates Obtained
Information on Each Certification
DP-203: Data Engineering on Microsoft Azure
The Microsoft DP-203 exam, Data Engineering on Microsoft Azure, validates the skills needed to design and implement data solutions using Azure services. This includes tasks such as:
- Designing and implementing data storage solutions (Azure Data Lake, Azure SQL, etc.)
- Developing data processing solutions (Azure Databricks, Azure Synapse)
- Monitoring and optimizing data solutions
- Securing data solutions and managing data governance
Earning this certificate demonstrates proficiency in building scalable, reliable data engineering solutions on Azure.
DP-900: Microsoft Azure Data Fundamentals
The Microsoft DP-900 exam, Azure Data Fundamentals, covers core data concepts and how they are implemented using Microsoft Azure data services. Key areas include:
- Core data concepts and relational data on Azure
- Working with non-relational data on Azure
- Data workloads and analytics on Azure
- Fundamentals of data security and compliance
This certification proves understanding of fundamental data principles and the ability to work with both relational and non-relational data in Azure.
AI-900: Microsoft Azure AI Fundamentals
The Microsoft AI-900 exam, Azure AI Fundamentals, tests foundational knowledge of AI concepts and Azure services that support AI workloads. Topics covered include:
- Principles of machine learning on Azure
- Computer vision workloads on Azure
- Natural language processing (NLP) workloads on Azure
- Responsible AI and security considerations
Achieving this certification shows proficiency in basic AI workloads and use of Azure AI services.
Experis Academy: Data Analytics Certificate
During the Experis Academy bootcamp, I developed a comprehensive skill set in data analytics. Key accomplishments include:
- SQL Proficiency: Writing efficient queries to manipulate and extract insights from large datasets.
- Python Programming: Automating data workflows and implementing analysis using Numpy, Pandas, and Matplotlib.
- Statistical Analysis: Conducting hypothesis tests and designing A/B experiments.
- Data Visualization: Crafting interactive dashboards with Tableau and Power BI.
- Web Scraping & ML Basics: Collecting web data and applying introductory machine learning models.
- Capstone Project: Leading a final project that showcased full-cycle data engineering, from sourcing to storytelling.
This experience honed my ability to transform raw data into actionable insights for business decision-making.