Introduction to Data Science

Introduction to data

• Data can be numbers, text, images, or anything with information.
• Time series data is a sequence of information taken at regular intervals, like weather measurements or heart rate data.
• Geographical data is linked to a location on Earth, useful for transportation applications.
• Unstructured data lacks a pre-defined format, making it challenging to analyze.
• Data vs. Information: Data itself may not be meaningful until it is processed or interpreted, turning it into information.

Introduction to data science

• Data science involves analyzing data to gain insights for business or research.
• It draws from fields like mathematics, statistics, artificial intelligence, and computer engineering
• Key Components:
  • Computer Engineering: builds the infrastructure to handle and process data.
  • Artificial Intelligence: facilitates learning from data and making predictions.
  • Statistics: analyzes data to find patterns and make inferences.
  • Mathematics: provides the foundation for algorithms and data analysis techniques.

The big picture

• Data science aims to transform data into actionable insights and decisions.

Sources of Data

• Data can come from many sources:
  • Sensors: collect data from the environment.
  • Internet: websites, social media, etc., generate vast amounts of data.
  • Market: customer behavior, sales data, and market trends.
  • Social Media: user interactions, posts, and trends.
  • Medical Data: patient records, medical images, and research data.
  • Business: financial data, customer information, and operational data.
  • University: research data, student records, and administrative data.
  • Digital Pictures and Videos: images and videos can be analyzed for content and patterns.

Data Decisions Actions

• Data is valuable as it can drive decisions and actions.

Data Scientists and Data

• Data scientists are focused on:
  • Identifying and acquiring relevant data sources.
  • Cleaning and preparing data for analysis.
  • Uncovering relationships and patterns within data.
  • Extracting insights and value from data.
  • Communicating results through visualizations or reports.

E-Commerce

• Data science is used in e-commerce websites like Amazon and Flipkart to personalize recommendations and improve the user experience.

Recommender Systems

• Recommender systems use data science to predict user preferences, for example, suggesting movies based on past ratings.

Healthcare

• Data science plays a vital role in healthcare:
  • Tumor detection: analyzing medical images to identify tumors.
  • Drug discoveries: identifying potential drug candidates based on data.
  • Medical Image Analysis: processing medical images to extract information.