Data Science and Analytics

Through a variety of writing projects requiring competence in clear, correct, and effective English, students use inferential and critical skills in the process of composing documented essays. Extensive reading materials serve as structural models and as the bases for discussion and for the writing of essays involving response, analysis, and synthesis.

This course emphasizes problem solving with programming using the Python programming language and problem solving with applications using Microsoft Excel.  In addition to problem solving skills the course presents current technologies and their impacts on society.  The course is designed for students who already possess familiarity with computer applications.  It is recommended for students planning to transfer to an upper division college that has a computer programming requirement in its computer literacy course.

Emphasis is on those topics from algebra and trigonometry that best prepare students for the first course in calculus. The areas of study are algebraic and transcendental functions and their graphs. Of special interest are polynomials, rational, exponential, logarithmic and trigonometric functions. Additional topics include vectors, polar coordinate systems, matrices and determinants. TI83/84 graphing calculator required.

In this survey course, students will gain knowledge of foundational topics in the field of data science and analytics.  It offers an overview of data science and data analytics, data collection and cleansing, data analysis, visualization, and dissemination.   Students will examine different types of data, storage formats, and some of the tools available today for performing data science tasks.  Prevalent software tools and techniques will be integrated into all topics.

An in-depth study of descriptive statistics, probability theory, sampling distributions, principles of hypothesis testing, and regression analysis. The material is designed to give students the knowledge and skills for gathering, organizing, and interpreting statistical data as relevant to business. This course will also provide a sound foundation for the study of more advanced topics.

Students will be introduced to the data processing method.  Students will learn how to collect raw data, cleanse, extract, sort, process, analyze, store and present the data in a readable format.  Students will use various techniques to collect and cleanse data from different sources available.  The use of common data collection and cleansing software will be integrated throughout the course.

Focusing on the programming of data science and common techniques of analytics, students will learn to use data mining software to develop data analysis projects including problem formulation, collecting relevant data, wrangling and formulating data, applying data analysis techniques, aggregating and visualizing data, and presenting results.  Students will write programs to analyze various sized structured and unstructured data sets.

Providing an overview of Big Data, the processes, tools, and techniques used to work with it, this course will introduce students to the methods used to collect, integrate, and extract large volumes of data from multiple file formats and database platforms.  Students will explore common data analysis tools, software, and techniques for processing large volumes of data will be studied.  Real-time and batch processing techniques for data aggregation and presentation will also be presented.  Large data file software tools will be integrated througout the course.

Introducing the tools and techniques required to present complex data in visually meaningful representations for better data driven decision making.  In this course, students will learn how to organize, analyze, and interpret data, determine alternate ways to tell stories with data, and to draw and present conclusions.  This process includes data modeling, data aggregation, and filtering, mapping data attributes to graphical attributes, and strategic visual encoding.  Dashboard design and software tools to produce effective presentations will be integrated throughout the course.

Providing students with an introduction to machine learning concepts, techniques, and procedures, this course focuses on how machine learning is used in data analytics to help develop data driven decisions and how computers gain the ability to learn on their own without being explicitly programmed.  Common algorithums used in machine learning to detect patterns and predict outcomes will be explored and implemented through commonly used software tools.