Data Science and Analytics with Python

Data Science and Analytics are fields that use programming, statistics, and machine learning to extract insights and solve problems based on data. Python is one of the most popular programming languages for Data Science, thanks to its simplicity, extensive libraries, and active community support.

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Key Steps in Data Science Workflow

1. Data Collection:

   • Gathering data from sources like databases, APIs, web scraping, or files (e.g., CSV, Excel, JSON).

2. Data Cleaning:

   • Handling missing values, removing duplicates, and correcting data types.

3. Exploratory Data Analysis (EDA):

   • Using descriptive statistics and visualizations to understand the dataset.

4. Data Transformation:

   • Feature engineering, scaling, normalization, and encoding categorical variables.

5. Modeling and Analysis:

   • Applying statistical models or machine learning algorithms to analyze or predict outcomes.

6. Visualization and Reporting:

   • Creating reports and dashboards to present insights.

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Python Libraries for Data Science

Python’s rich ecosystem of libraries simplifies every step of the data science process:

Library	Purpose
NumPy	Numerical computations and array operations.
Pandas	Data manipulation and analysis.
Matplotlib	Basic plotting and visualizations.
Seaborn	Advanced statistical visualizations built on Matplotlib.
Scikit-learn	Machine learning models and preprocessing tools.
TensorFlow/Keras	Deep learning frameworks for neural networks.
Statsmodels	Statistical analysis and hypothesis testing.
Plotly	Interactive visualizations and dashboards.
NLTK/Spacy	Natural language processing (NLP).

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Example Workflow: Analyzing a Dataset

1. Import Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

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2. Load and Explore Data

# Load dataset
data = pd.read_csv("house_prices.csv")

# Display basic information
print(data.head())
print(data.info())
print(data.describe())

# Check for missing values
print(data.isnull().sum())

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3. Data Cleaning

# Fill missing values with mean
data['LotFrontage'] = data['LotFrontage'].fillna(data['LotFrontage'].mean())

# Drop irrelevant columns
data = data.drop(['Alley', 'PoolQC'], axis=1)

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4. Exploratory Data Analysis (EDA)

# Correlation heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(data.corr(), cmap='coolwarm', annot=True)
plt.title('Correlation Matrix')
plt.show()

# Scatter plot for relationship between 'GrLivArea' and 'SalePrice'
sns.scatterplot(x='GrLivArea', y='SalePrice', data=data)
plt.show()

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5. Data Preparation

# Select features and target variable
X = data[['GrLivArea', 'GarageCars', 'YearBuilt']]
y = data['SalePrice']

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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6. Apply Machine Learning Model

# Train a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

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7. Evaluate Model

# Calculate Mean Squared Error (MSE)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

# Plot predictions vs actual values
plt.scatter(y_test, y_pred)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual vs Predicted Prices")
plt.show()

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Key Applications of Data Science

1. Business Analytics:
   • Sales forecasting, customer segmentation, and churn prediction.
2. Healthcare:
   • Disease prediction, patient management, and drug discovery.
3. Finance:
   • Fraud detection, algorithmic trading, and credit scoring.
4. Marketing:
   • Sentiment analysis, recommendation systems, and A/B testing.
5. Natural Language Processing (NLP):
   • Chatbots, text summarization, and sentiment analysis.
6. Computer Vision:
   • Image recognition, facial detection, and object classification.

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Data Visualization with Python

Example: Visualization of Sales Data

# Load dataset
sales_data = pd.read_csv("sales_data.csv")

# Bar chart: Sales by category
sns.barplot(x='Category', y='Sales', data=sales_data)
plt.title('Sales by Category')
plt.show()

# Line chart: Monthly sales trend
sales_data['Date'] = pd.to_datetime(sales_data['Date'])
monthly_sales = sales_data.groupby(sales_data['Date'].dt.to_period('M')).sum()

monthly_sales['Sales'].plot(kind='line', figsize=(10, 5))
plt.title('Monthly Sales Trend')
plt.ylabel('Sales')
plt.show()

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Learning Resources

1. Books:
   • Python for Data Analysis by Wes McKinney.
   • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron.
2. Online Courses:
   • DataCamp: Python for Data Science tracks.
   • Kaggle: Free micro-courses and datasets.
3. Practice Platforms:
   • Kaggle: Competitions and datasets.
   • HackerRank and LeetCode for coding challenges.

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By using Python’s libraries and tools, you can tackle a wide range of data science tasks, from cleaning raw datasets to building predictive models and visualizing insights. Whether you’re analyzing trends or deploying machine learning algorithms, Python provides a versatile foundation for modern data-driven projects.