Module 6 Lesson 1: Intro to Data Science

You've learned how to build programs, handle files, and design systems. Now, we use those skills for one of the most exciting fields in tech: Data Science. Data Science is the practice of extracting meaningful insights from "raw" data using statistics, programming, and domain knowledge.

Lesson Overview

In this lesson, we will cover:

• The Data Science Workflow: From raw data to decision-making.
• The Big Three: NumPy, Pandas, and Matplotlib.
• Why Python?: Community, speed, and library support.
• Real-world Applications: Recommendation systems, medical diagnosis, and more.

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1. The Data Science Workflow

Data science isn't just about math; it's a process:

1. Collection: Gathering data from files, databases, or websites.
2. Cleaning: Fixing missing values or incorrect data (80% of the job!).
3. Exploration: Finding patterns and trends using charts.
4. Modeling: Using Machine Learning to predict the future.
5. Communication: Explaining the results to humans.

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2. The Ecosystem (The Tools of the Trade)

Python doesn't do data science alone. It relies on a "Stack" of powerful libraries:

• NumPy: For high-performance math and numbers.
• Pandas: For tables and spreadsheets (DataFrames).
• Matplotlib / Seaborn: For drawing charts and graphs.
• Scikit-Learn: For Machine Learning (Module 7).

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3. Why Python for Data?

If you want to do data science, you use Python. Why?

• Readability: Data scientists are often mathematicians first and coders second. Python’s simple syntax is perfect for them.
• Integration: Python connects easily to "Big Data" tools like Spark and SQL.
• Libraries: You don't have to write statistics code from scratch; someone has already written a library for it.

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4. Real-world Example: The Netflix Effect

How does Netflix know you want to watch a documentary about space? They use Data Science. They analyze your "Data" (what you watched, when you paused, what you liked) using Python libraries to find patterns in millions of other users and predict what you'll enjoy next.

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Practice Exercise: Your Personal Data Audit

Think about a service you use daily (like Spotify, YouTube, or Amazon).

1. Identify 3 types of Data they collect about you.
2. Identify 1 Insight they might gain from that data.
3. Identify how they might use that insight to change your experience.

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Quick Knowledge Check

1. What is the main goal of Data Science?
2. Which library is used primarily for handling table-like data (spreadsheets)?
3. Why is "Cleaning" considered the most time-consuming part of the job?
4. Name one reason why Python is preferred over other languages for data analysis.

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Key Takeaways

• Data Science is about turning data into knowledge.
• The field relies on a specialized ecosystem of libraries.
• Python is the industry standard due to its simplicity and powerful tools.
• Every major tech company uses data science to make decisions.

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What’s Next?

Before we can handle complex tables, we need to master the foundation of all numerical computing in Python. In Lesson 2, we’ll start our journey with NumPy: Arrays and Vectorization!