Source Of Summary:

Handout 0 - Introduction.pdf

Lecture 1 – Introduction

1. Course Description & Overview 📖💡

Course Description:

• Practical Data Handling & Statistical Tools:
  • Learn to use Python, predictive analysis software (like SAS and Apache Spark), plus other analytics tools. 💻📊
• Real-World Use Cases:
  • Applications in Finance, Media, and Health. 💵🎥🏥

Course Overview:

• Data Science as a Career:
  • One of the best jobs available in terms of openings, salary, and career opportunities! 🚀💰
• Definition:
  • Data science is the application of computational and statistical techniques to solve real-world problems and answer scientific inquiries. 🔍🧪
• The Iterative Cycle:
  • Design a problem, build an algorithm (or determine that a solution isn’t possible), and evaluate the insights. ♻️🛠️
• Core Ingredients:
  • Data Science = Statistics + Data Processing + Machine Learning + Scientific Inquiry + Visualization + Business Analytics + Big Data + …
  • Note: Machine learning might be part of the solution but it isn’t always required! 🤖✅
• Focus on All Data Sizes:
  • Data science deals with both big and small data. 📏📐

2. Detailed Course Components 🧩

Topics Covered (subject to change):

• Data Collection & Management:
  • Relational data, matrices & vectors, graphs & networks, free text processing, geographical data. 🌐🗄️
• Statistical Modeling & Machine Learning:
  • Classification, regression, hypothesis testing, kernel methods, clustering, dimensionality reduction, recommender systems, deep learning, and more! 📈🤖
• Visualization:
  • Techniques for data exploration, presentation, and interactivity. 📊🎨