Data Scientist Nanodegreee - Udacity

Certification Link

Introduction

This repository contains 5 DS projects and insights during my pursuit of the Data Scientist Nanodegree at Udacity:

Data Science Basic Skills

• Python, SQL, SAS, R; Data Visualization; Command Line Essentials, Git & GitHub; Practical Statistics, Linear Algebra, Machine Learning

Part 1. Cross-industry Standard Process for Data Mining CRISP-DM

• Business Understanding，Data Understanding， Data Preparation, Data Modeling, Result Evaluation, Deployment
• Communicating to non-technical stakeholders via GitHub Repo, Blog Post, dashboard, story-telling.
• Project: StackOverflow 2021-2023 Survey Data Analysis
• Medium Post: Decoding Data Science Career: PhD or not

Part 2. Software Engineering

• Modularized code & version control, Test-driven development (TDD) and Unit test; Object-Oriented Programming (OOP), PyPi
• Web Development: front-end (HTML, CSS, Javascript, Bootstrap, Plotly), back-end (Flask)
• Project (Web App): COVID-19 Time Series Forecasting Interactive Dashboard using Dash and Plotly

Part 3. Data Engineering

• ETL pipeline, NLP pipeline, Machine Learning Pipeline, SQLite, Flask Web App
• Project: Disaster Message Multi-label Classification ML Pipeline

Part 4. Experimental Design (A/B Testing)

• Statistical Considerations in A/B Testing, Metric Analysis, Post-Analysis
• Project: Starbucks Promotion Strategy Optimization
• Medium Post: After A/B Test: Optimize a Advertising Promotion Strategy by Audience Targeting

Part 5. Recommendation System

• Knowledge-Based Recommendations, Collaborative Filtering Based Recommendations, Content-Based Recommendations, Matrix Factorization for Recommendations
• Project: Recommendation for New Articles in IBM Watson Studio

Capstone Project. Big Data Analysis with Spark

• Apache Spark, Spark Data Frames, Spark ML, Spark SQL, AWS Elastic MapReduce (EMR)
• Project: Music App User Churn Prediction using 12GB activities data
• Medium Post: Predict User Churn with Spark & AWS

Licensing, Authors, Acknowledgements

I would like to extend my sincere gratitude to data science teams in

• Udacity
• IBM
• Starbucks
• Airbnb
• Sparkify
• Appen
• Stack Overflow
• AWS (Amazon Web Services)
• Apache

for their contribution in making this valuable resource available to the public. A special acknowledgment goes to Udacity for their exceptional guidance throughout this project. Feel free to utilize the contents of this work, and when doing so, please remember to appropriately attribute the contributions of myself, and/or Udacity.