Program Description:

This program provided hands-on training in the core components of data analysis and visualization using Microsoft Power BI. Key areas of focus include:

• Data Preparation & Transformation: Mastery of Power Query to import, clean, reshape, and combine data from diverse sources, ensuring optimal data quality for analysis.

• Data Modeling: Expertise in designing effective data models, including defining relationships, hierarchies, and measures to streamline data analysis and reporting processes within Power BI.

• Data Visualization: Proficiency in creating compelling visualizations (charts, graphs, maps, tables) that effectively communicate insights, patterns, and key performance indicators. Application of design principles to enhance clarity and user engagement.

• Advanced Analytics with DAX: In-depth understanding of Data Analysis Expressions (DAX) for complex calculations, statistical analysis, and forecasting to uncover deeper insights and support informed decision-making. The program incorporated the following real-world projects to apply these skills:

• Seven Sages Brewing Sales Analysis: Designed a data model and visualizations within Microsoft Power BI to optimize product performance analysis and support strategic decision-making for a growing brewing company.

• Waggle Pet Device Comparison: Created a comprehensive report in Microsoft Power BI comparing the performance of new pet tracking devices to guide informed product decisions.

• National Clothing Chain Market Analysis: Conducted in-depth data analysis using Microsoft Power BI to identify target demographics, develop customer segmentation, and inform acquisition strategies.

Projects:

• Objective:
  • Analyze the performance and user reception of the Lapcat prototype in comparison to the successful Lapdog device. Provide actionable insights to guide Waggle’s executive decision-making regarding the launch of the Lapcat product.
• Data Sources:
  • Lapcat prototype field testing data (includes metrics like daily steps, device ratings, pet and owner demographics). Presumably existing Lapdog device data for comparison purposes.
• Tools:
  • Microsoft Power BI: Software for data modeling, data transformation, visualization creation, and report design.
  • Power Query: Power BI’s built-in tool for data cleaning, transformation, and preparation before analysis.
  • DAX: Data Analysis Expressions for creating calculated measures and columns within the Power BI environment.
• Scope:
  • Design a multi-page Power BI report addressing CEO’s key questions, product team’s analysis requests, and company branding requirements. Implement visual components: bar charts, line charts, scatter plots, bubble maps, tables, cards, etc.
  • Integrate user experience enhancements: Slicers (various types), navigation buttons, and bookmarks.
  • Utilize DAX to create calculated measures or columnsfor deeper analysis and custom metrics.
• Capstone Project

Program Description:

Udacity’s Data Engineering with AWS Nanodegree program provides training in the design and implementation of scalable data solutions on Amazon Web Services. Key areas of focus include:

• Data Modeling: Expertise in both relational (SQL) and NoSQL data modeling techniques, including Apache Cassandra. Skillful in designing models that align with diverse business requirements and ETL processes.
• Cloud Data Warehousing: Solid understanding of OLAP concepts and experience building a data warehouse using Amazon Redshift. Proficient in ETL processes to optimize data for analytics purposes.
• Spark & Data Lakes: Deep dive into the Big Data ecosystem, including data lakes, lakehouse architecture, and the Apache Spark framework. Adept at processing and transforming large datasets within AWS data lakes using Spark, AWS Glue, and Athena.
• Data Pipeline Automation: Knowledge in creating and orchestrating efficient data pipelines using Apache Airflow. Ability to incorporate data validation, quality checks, and leverage AWS components like S3 and Redshift for a streamlined data engineering process.

The program offers real-world scenarios to apply these skills:

• Music Streaming App Data Modeling: Design of data models to support analytics for a music streaming application.
• Cloud Data Warehouse Implementation: Construction of a scalable data warehouse to generate actionable insights.
• Data Lakehouse Solution: Development of a data lakehouse for sensor data analysis and machine learning models.
• Automated Data Pipeline: Creation of a robust data pipeline using Airflow, integrating data quality checks, backfills, and AWS services.

Projects:

• Data Modeling with Postgres
• Data Modeling with Apache Cassandra
• Data Warehouse with AWS
• Data Lake with Spark
• Data Pipeline with Airflow

Capstone Project: COVID-19 Data Pipeline for Hospital Resource Forecasting

• Objective: Develop an ETL process to facilitate forecasting of COVID-19 cases (Confirmed, Deaths, Recovered, Active) and Bed Utilization Rates. The goal is to help hospitals optimize resource allocation during the pandemic.
• Data Sources: Johns Hopkins University CSSE COVID-19 daily reports, USA Hospital Beds - COVID-19 dataset from the AWS Data Exchange, and JHU’s UID_ISO_FIPS_LookUp_Table.
• Tools: AWS CloudFormation, Apache Airflow, Amazon Redshift, AWS Data Exchange.
• Scope: Design an ETL process, create three Redshift data tables (covidcases, masternode, and hospital), and integrate with a previous machine learning project Machine Learning Engineer Capstone Project.

Program Description:

Projects:

GPA: 3.8/4.0

Courses included:

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GPA: 3.4/4.0

Courses included:

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