Syllabus

• Week 1

  Introduction to Data Science

  • Introduction to Data Science

    We go over the beginnings of Data Science for Educators, what you need to know to teach data science and examples of the work done in data science.

  • What You Need to Know to Become a Data Scientist

    What you need to know to become a Data Scientist.

  • Examples of Data Science

    We'll be going into specific examples of data science.

• Week 2

  The Data Science Process

  • Data Science Process: The Beginning

    We'll discuss the process of data science, data types, and categories of data science.

  • Data File Formats

    We'll be diving into the exciting world of data formats used in Data Science.

  • Processes Involved in Data Science

    We dive into the processes involved in Data Science.

• Week 3

  Introduction to Machine Learning

  • What is Machine Learning?

    We discuss and define what Machine Learning is, an introduction and history of the concept and terminology used in Machine Learning.

  • Types and Examples of Machine Learning

    We go into detail about the types of Machine Learning and give examples of each.

• Week 4

  Statistics & Probability - Introduction of Bayes Probability and K-Nearest Neighbors

  • Probability: An Introduction and Bayes Probability

    We discuss and define Bayes Probability.

  • K-Nearest Neighbors (KNN)

    An introduction of the K-Nearest Neighbors algorithm and its importance for classification.

• Week 5

  Statistics & Probability - Introduction of Regression and Gradient Descent

  • Statistics: An Introduction

    We define statistics and it's deeper meaning in Data Science.

  • Regression

    We discuss Regression and what it means in Data Science.

  • Gradient Descent

    We discuss gradient descent in Data science.

  • Logistic Regression

    We discuss and define Logistic Regressoin.

• Week 6

  Introduction of Data Mining

  • Data Mining: An Introduction

    We define what Data Mining is.

  • Tools in Data Mining

    We discuss various tools used in Data Mining.

  • Examples of Data Mining

    We give real-world examples of Data Mining.

• Week 7

  Introduction of Big Data

  • Big Data: An Introduction

    What is Big Data? We talk about it.

  • Market for Big Data

    We discuss the markets for Big Data.

  • Efficient Management of Big Data

    We discuss efficient management, algorithms and problems and their solutions of Big Data.

  • Big Data Tools

    We discuss the tools and software used in the study of Big Data.

• Week 8

  Visualization in Data Science

  • Understanding Data through Visualization

    We discuss visualization in Data.

  • Aesthetics of Visualization

    We discuss the aesthetics of visualization.

  • Types of Visualization

    We discuss the types of visualization used in Data Science.

  • What We've Learned

    We review what we've learned in the course by assessing our obtainment of the learning objectives and consider next steps for incorporating data science in the high school classroom.

Who will you learn with?