AWS BI Engineer Nanodegree Notes

Welcome! This repo contains my personal notes, reflections, and resources from the AWS BI Engineer Nanodegree. I'm documenting each lesson and project to support fellow learners, especially those navigating tech from 0.

📚 Contents

• 6 Lessons
• 2 Projects

✨ Goals

In the Future AWS Business Intelligence Engineer Nanodegree program, you can expect to gain a variety of skills that are essential for a career in business intelligence and data analytics. Here’s a list of skills you will likely develop throughout the program:

Data Analysis:

• Analyzing and interpreting data to derive actionable insights.
• Using statistical methods to understand data trends.

Data Visualization:

• Creating interactive dashboards and visualizations using tools like Amazon QuickSight.
• Designing effective visual representations of data to communicate findings.

Business Intelligence Tools:

• Proficiency in using Amazon QuickSight for data analysis and reporting.
• Understanding the functionalities of business intelligence platforms.

Data Preparation:

• Cleaning and transforming raw data into usable formats.
• Using ETL (Extract, Transform, Load) processes to prepare data for analysis.

SQL Proficiency:

• Writing SQL queries to extract and manipulate data from databases.
• Understanding database structures and how to interact with them.

Machine Learning Basics:

• Introduction to machine learning concepts and how they apply to business intelligence.
• Using predictive analytics to forecast trends.

Data Storytelling:

• Crafting compelling narratives around data insights to influence decision-making.
• Communicating findings effectively to stakeholders.

Cloud Computing:

• Understanding cloud-based data storage and processing using AWS.
• Familiarity with AWS services related to data analytics.

Collaboration and Communication:

• Working effectively in teams and communicating technical concepts to non-technical stakeholders.
• Presenting data findings and recommendations clearly.

Project Management:

• Managing data projects from conception to execution.
• Applying best practices in project management within a data context.

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