Introduction to Data Science

Instructor: Dr. Aihua Li

Skills you'll gain

  •   Mathematical Theory & Analysis
  •   Probability & Statistics
  •   Data Visualization Software
  •   Data Collection
  •   Arithmetic
  •   Data Wrangling
  •   Probability
  •   Regression Analysis
  •   Information Privacy
  •   R Programming
  •   Data Ethics
  •   Sampling (Statistics)
  •   Simulations
  •   Data Structures
  •   Data Analysis
  •   Statistical Analysis
  •   Data Science
  •   General Mathematics
  •   Analytical Skills
  •   Statistics
  • There are 6 modules in this course

    We reside in a world experiencing an explosion of information, with a rapid and exponential growth of data. This surge in data captures increasing interest across various fields. Data science involves the gathering of extensive data and the fusion of domain expertise, programming skills, mathematics, and statistical knowledge to derive meaningful insights. Given the breadth and depth of data science, this course aims to furnish you with a comprehensive theoretical foundation and framework to initiate your journey in this field. "Data" permeates every aspect of data science. The course is divided into five parts, each centered around core topics related to "data". The initial part introduces data ethics, outlining the ethical issues surrounding data collection, usage, and reporting. The second part delves into data collection, acquisition sources, and data structures. The third part focuses on cutting-edge research in Data Science, immersing you in the realm of data science. The fourth part acquaints you with basic data processing using programming, specifically in R, the prevailing data analytics tool. Here, you will gain familiarity with R fundamentals, execute basic data wrangling tasks, develop an understanding of data storage and management, and gain experience in data visualization. The fifth part of the course imparts fundamental knowledge of probability and statistics, preparing you to move to the next stage of exploration.

    Privacy, Transaction Transparency and Anonymity

    Data Validity and Algorithmic Fairness

    Societal Consequences and Code of Ethics

    Data Sources and Data Acquisition

    Data Types and Data Structures

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