Welcome to the 'Learn Data Science with Raheel' repository! This repository is designed to help you embark on a journey to master the art of data science. Whether you're a beginner or looking to enhance your skills, you'll find a collection of resources designed by Raheel to guide you through the world of data science."
- Definition: Data Science is the interdisciplinary field that deals with extracting knowledge and insights from data.
- Data Science combines domain expertise, programming skills, and statistical/mathematical knowledge to analyze and interpret complex data sets.
- Three main components: domain expertise, programming skills, and statistics/mathematics knowledge.
- Domain Expertise: Understanding the subject area or industry that the data is related to.
- Programming Skills: Proficiency in programming languages like Python to manipulate and analyze data.
- Statistics/Mathematics Knowledge: Using statistical methods and mathematical concepts to draw meaningful conclusions from data.
- Data-driven decision-making in various industries.
- Data Science enables organizations to make informed decisions by analyzing data patterns and trends.
- Examples: Healthcare, Finance, Marketing, Technology.
-** Healthcare:** Analyzing patient data for personalized treatment plans.
- Finance: Detecting fraudulent transactions using anomaly detection algorithms.
- Marketing: Targeted advertising based on customer behavior analysis.
- Technology: Developing recommendation systems for content streaming platforms.
- Data, Information, Knowledge hierarchy.
- Data: Raw facts and figures without context.
- Information: Processed data with context, making it meaningful.
- Knowledge: Insights and understanding derived from information.
- Structured vs. Unstructured data.
- Structured Data: Organized data in rows and columns (e.g., spreadsheets, databases).
- Unstructured Data: Data without a fixed format (e.g., text, images, videos).
- Introduction to Big Data.
- Big Data refers to datasets that are too large and complex to be processed using traditional methods.
- Big Data challenges include storage, processing, analysis, and visualization of massive datasets.
- Python: General-purpose programming language.
- Python's readability and versatility make it a popular choice for data analysis and scientific computing.
- Pandas: Data manipulation and analysis library.
- Pandas provides data structures like Series and DataFrame, making data manipulation and cleaning more efficient.
- NumPy: Numerical computing library.
- NumPy offers support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.
- Matplotlib: Data visualization library.
- Matplotlib enables the creation of various types of visualizations, aiding in the interpretation and communication of data insights.