Introduction to Data Science - BSCS Notes

Introduction to Data Science — Complete BSCS Notes

Introduction to Data Science

Definition: Data Science is the field of extracting knowledge and insights from data using statistics, machine learning, and computing.

Big Data: Extremely large datasets that cannot be processed using traditional systems.

Datafication: Converting real-world activities into digital data.

Skills Required:
• Programming
• Statistics
• Machine Learning
• Data Visualization

Example: Netflix recommending movies using user data.

Statistical Inference

Population: Complete group of data.

Sample: Small subset taken from population.

Probability Distribution: Describes likelihood of outcomes.

Model Fitting: Creating mathematical model from data.

Example: Predicting student marks using previous results.

Introduction to Python

Definition: Python is the most popular language in Data Science.

Python Example:


import pandas as pd

data = [10,20,30]

print(data)

Libraries: NumPy, Pandas, Matplotlib, Scikit-learn

Exploratory Data Analysis (EDA)

Definition: Process of understanding and analyzing datasets before modeling.

Steps:
• Cleaning data
• Handling missing values
• Finding patterns

Example: Analyzing sales dataset.

Basic Machine Learning Algorithms

Linear Regression: Predict continuous values.

k-NN: Finds nearest neighbors.

k-Means: Clustering algorithm.

Naive Bayes: Probability-based classification.

Python Example:


from sklearn.linear_model import LinearRegression

Feature Generation & Selection

Feature: Input variable used in ML model.

Feature Selection: Choosing important features only.

Example: Age and salary used for prediction.

Dimensionality Reduction

PCA: Principal Component Analysis reduces dimensions.

SVD: Singular Value Decomposition for matrix reduction.

Purpose: Faster processing and visualization.

Mining Social-Network Graphs

Social Network Graph: Users connected as nodes and edges.

Graph Clustering: Grouping related nodes.

Community Detection: Finding groups in social networks.

Example: Facebook friend recommendations.

Data Visualization

Definition: Representing data visually using charts and graphs.

Tools: Matplotlib, Tableau, Power BI

Visualization Types:
• Bar Chart
• Pie Chart
• Line Graph

Python Example:


import matplotlib.pyplot as plt

plt.plot([1,2,3],[4,5,6])
plt.show()

Data Science & Ethical Issues

Privacy: Protecting personal data.

Security: Preventing unauthorized access.

Ethics: Fair and responsible use of data.

Next-Generation Data Scientists: Experts combining AI, analytics, and ethics.

CSI-603 Introduction to Data Science

Introduction to Data Science — Complete BSCS Notes