Data science is a multidisciplinary field that integrates statistics, programming, and domain knowledge to derive insights from various data types. The data science workflow includes problem definition, data collection, exploration, modeling, evaluation, and deployment, utilizing tools such as Python, R, and machine learning libraries. An example includes predicting house prices by analyzing historical datasets and applying machine learning techniques.

1. Introduction to Data Science
A Beginner's Guide to Data Analysis
and Machine Learning

2. What is Data Science?
• Data Science is a multidisciplinary field that
combines:
• - Statistics and Mathematics
• - Programming and Technology
• - Domain Knowledge
• It aims to extract insights and knowledge from
structured and unstructured data.

3. Data Science Workflow
• 1. Define the Problem
• 2. Collect and Prepare Data
• 3. Explore and Visualize Data
• 4. Build Predictive Models
• 5. Evaluate and Interpret Results
• 6. Deploy and Monitor the Solution

4. Popular Tools and Libraries
• - Programming Languages: Python, R
• - Data Manipulation: Pandas, NumPy
• - Data Visualization: Matplotlib, Seaborn,
Plotly
• - Machine Learning: Scikit-learn, TensorFlow,
PyTorch
• - Big Data: Hadoop, Spark
• - Database: SQL, NoSQL

5. Predictive Modeling Example
• 1. Problem: Predict house prices based on
features like size, location, etc.
• 2. Data: Housing dataset with historical prices
and features.
• 3. Steps:
• - Clean and preprocess the data
• - Visualize relationships between variables
• - Train a machine learning model (e.g., Linear
Regression)