Foundations of Data: Data Science

Grades:��9 - 12

Equip high school students with the knowledge and skills to thrive in a data-driven world. Foundations of Data: Data Science introduces learners to essential concepts in data collection, analysis, visualization, and prediction. Students explore real-world data challenges through hands-on projects using Python and industry-standard tools, gaining experience with statistical analysis, machine learning, and data modeling techniques. The course provides a strong foundation for students pursuing future studies or careers in data science and analytics.

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Program Details

Foundations is an innovative series of titles designed for high school students, each covering a specific area of the digital world. These key features of every Foundations program bring skills to life:��

Clear instruction and active learning: Students thrive in their learning when presented with well-deﬁned goals, clear instruction, relevant examples, hands-on activities, and project-based learning.
Timely and relevant coverage: Content aligns with the latest industry practices, preparing students for future careers.
Complete teacher support: Educators prepare efficiently with a print teacher’s edition, lesson plans, lesson guides, and pre-built assessments.
Flexible delivery: Students benefit from a hardbound textbook or use the digital resources complete with an eBook, an adaptive SmartBook®, assessment, videos, and more.��

Students using Foundations of Data: Data Science will:

Learn how to collect, clean, and analyze data using tools like Python, Pandas, NumPy, and Jupyter Notebook.
Explore key data science concepts, including statistical analysis, data visualization, predictive modeling, and optimization.
Build and present data-driven insights through real-world projects focused on solving practical problems.
Develop foundational skills for future careers in data analytics, machine learning, and data-driven decision-making.

Sample Chapter

1. Introduction to Data Science

Lesson 1: Data, Information, and Knowledge
Lesson 2: Working with Data
Lesson 3: Data Science Fundamentals

2. Data Collection and Validation

Lesson 1: Data Collection
Lesson 2: Data Types
Lesson 3: Data Entry Validation

3. Exploratory Data Analysis

Lesson 1: Data Analysis
Lesson 2: Python Libraries for Data Analysis
Lesson 3: Data Visualization

4. Predictive Data Modeling and Forecasting

Lesson 1: Predictive Data Modeling
Lesson 2: Forecasting

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