Course Description

Data science is the fastest growing field in this generation. Data scientist has been named the number one job in the U.S. NM is a team of mathematicians and computer scientists. We use data to build decision-making system to help management make data-driven business decisions based on mathematics and science. We create this course to help aspiring young people to learn data science.

This course is different from many other “standard” data science curriculums which are by-and-large Python programming in statistics and machine learning for adults. First, our target readers are high school to junior college students who may not have been exposed to the necessary mathematics for data science. As data science is an interdisciplinary field of mathematics, statistics and computer science, we start with linear algebra, which is fundamental to all numerical computing, and gradually build up the more advanced mathematics to regression and machine learning. We want to pave a solid theoretical foundation for young readers. Unlike a college textbook, the mathematics concepts are intentionally made not rigorous so that junior students can understand the intuition behind them. Many examples are provided and illustrated with charts, graphs and pictures for easy understanding.

Second, our choice of programming language is Kotlin, a modern open-source Java Virtual Machine (JVM) based language designed to make people happy. Kotlin is the next-generation data science language. JetBrains develops Kotlin and Google officially makes it the preferred language for Android. Unlike Python which is notoriously difficult to share code with your friends, the data science applications developed in Kotlin can be easily deployed to any 13 billion devices that support Java, mostly likely including your mobile phones. Kotlin applications can be easily integrated with the dozens of Java application frameworks such as Spring Boot. Moreover, Kotlin, complied to JVM bytecode, has superior performance compared to Python interpretation.

All the example code in this online course can be copied-and-pasted in the S2 IDE for running. As we continue to develop and improve this course, we welcome your feedback and comments.

Happy coding!

After this course you will learn

  1. Mathematics needed for data science
  2. Statistics 
  3. Kotlin and Java application development
  4. Developing data-driven solutions to business and scientific problems

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