week 1 

📊 Introduction to Data, Data Analytics, and Machine Learning

This document provides a beginner-friendly overview of key concepts in data analysis and machine learning. Whether you're just starting with data science or need a quick refresher.

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📌 What is Data?

Data refers to raw facts and figures collected from observations, measurements, transactions, etc. It has no meaning unless processed.

• Example:
  • Numbers: 21, 35, 47
  • Text: "Red", "India", "Sunny"

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📌 What is Data Analytics?

Data Analytics is the process of collecting, cleaning, transforming, and interpreting data to discover useful insights for decision-making.

• Example:
  • Finding which product sold the most last month.
  • Analyzing website traffic to improve performance.

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📌 Data Analyst vs Data Scientist

Feature	Data Analyst	Data Scientist
Goal	Understand trends and patterns	Predict future outcomes
Focus	Reporting and visualization	Modeling and experimentation
Tools	Excel, SQL, Power BI, Tableau	Python, R, TensorFlow, Scikit-learn
Output	Dashboards, reports	Predictive models, algorithms

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📌 Types of Data

Type	Description	Example
Structured	Organized into tables	SQL Databases, Excel
Unstructured	No specific format	Images, Videos, Emails
Semi-Structured	Partial structure	JSON, XML, HTML logs
Quantitative	Numeric and measurable	Age, Salary
Qualitative	Descriptive or categorical	Gender, Country, Color

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📌 Types of Learning in AI/ML

Type	Description	Example
Supervised Learning	Input and labeled output available	Email spam detection
Unsupervised Learning	Only input, no labels	Customer segmentation
Semi-Supervised	Few labeled and many unlabeled examples	Webpage classification
Reinforcement Learning	Agent learns through rewards/punishments	Game AI, robotic control

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📌 Types of Data Analysis

Type	Description
Descriptive	Summarizes past data (what happened)
Diagnostic	Investigates causes (why it happened)
Predictive	Forecasts future outcomes
Prescriptive	Suggests actions based on predictions
Exploratory	Finds unknown patterns or relationships

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📌 Mean, Median, Mode in Data Analysis

Term	Description	Python Example
Mean	Average value	df['col'].mean()
Median	Middle value in sorted data	df['col'].median()
Mode	Most frequently occurring value	df['col'].mode()

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📌 Example of a Dataset

Name, Age, Gender, Score
Alice, 24, Female, 85
Bob, 27, Male, 78
Charlie, , Male, 92
Diana, 23, Female,
📌 What is the method called? (Listwise Deletion)
Listwise Deletion is a method for handling missing data by removing any row that contains even a single missing value.

Pros: Simple and easy to implement

Cons: Can lead to significant data loss

📌 How to Perform Listwise Deletion in Python (Pandas)
python
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import pandas as pd

# Load data
df = pd.read_csv('data.csv')

# Drop rows with any missing values
df_clean = df.dropna()

# Show cleaned data
print(df_clean)
📝 License
This project is free to use for learning and educational purposes.
                                                                                    week 2
from pathlib import Path

# Content for the README.md file on NumPy fundamentals
numpy_readme_content = """
# 🧮 NumPy Fundamentals

This document provides an overview of essential NumPy operations for beginners, including array creation, reshaping, random generation, vector operations, and memory efficiency.

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## 📌 What is NumPy?

**NumPy** (Numerical Python) is a powerful library for numerical computing in Python. It offers fast operations on arrays and matrices, and is the foundation for most data science libraries.

---

## 📌 Basic Array Creation

```python
import numpy as np

# 1D Array
arr1 = np.array([1, 2, 3])

# 2D Array
arr2 = np.array([[1, 2], [3, 4]])

# Zeros and Ones
zeros = np.zeros((2, 3))
ones = np.ones((3, 3))

# Empty Array
empty = np.empty((2, 2))
📌 Array Generation Functions
python
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# Range of values
np.arange(0, 10, 2)       # [0 2 4 6 8]

# Evenly spaced numbers
np.linspace(0, 1, 5)      # [0.   0.25 0.5  0.75 1. ]

# Identity matrix
np.eye(3)                 # 3x3 Identity matrix
📌 Random Generation Functions
python
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# Random floats between 0 and 1
np.random.rand(3, 2)

# Random integers
np.random.randint(1, 10, size=(2, 3))

# Normal distribution
np.random.randn(4)

# Seed for reproducibility
np.random.seed(42)
📌 Array Attributes
python
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arr = np.array([[1, 2, 3], [4, 5, 6]])

arr.shape      # (2, 3)
arr.ndim       # 2 (dimensions)
arr.size       # 6 (total elements)
arr.dtype      # int64 (data type)
arr.itemsize   # 8 (bytes per element)
📌 Data Types in NumPy
python
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# Specifying data types
np.array([1, 2, 3], dtype='float32')
np.array([1, 2, 3], dtype='int8')
📌 Performance and Memory Efficiency
NumPy uses contiguous memory blocks and vectorized operations, making it significantly faster and more memory-efficient than Python lists.

python
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import time

a = list(range(1000000))
b = np.arange(1000000)

# Performance comparison (list vs array operations)
📌 Vectorized Operations
python
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a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

# Element-wise operations
a + b     # [5 7 9]
a * b     # [4 10 18]
a ** 2    # [1 4 9]
📌 Reshaping and Resizing
python
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arr = np.arange(12)

# Reshape to 3x4
arr.reshape(3, 4)

# Flatten array
arr.flatten()

# Resize (in-place)
arr.resize((2, 6))
📌 Useful Array Methods
python
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arr.sum()           # Sum of all elements
arr.mean()          # Mean
arr.std()           # Standard deviation
arr.max()           # Maximum value
arr.min()           # Minimum value
arr.argmin()        # Index of min
arr.argmax()        # Index of max
arr.T               # Transpose
📝 License
This project is free to use for educational purposes.