Data Science Certificate of Achievement

Program Description

The Data Science Certificate of Achievement equips students with the foundational knowledge, skills, and practical experience to tackle complex, data-driven challenges in today’s digital age. With a focus on programming, statistics, machine learning, big data analytics, and data visualization, students engage in projects and collaborations that prepare them for the data-driven workforce. The program fosters critical thinking, problem-solving, and interdisciplinary collaboration, emphasizing both technical skills and domain-specific knowledge. Ideal for students with a mindset of lifelong learning and ethical responsibility, the program offers a pathway to meaningful careers and further education in the evolving field of data science.

The pathway below represents an efficient and effective course taking sequence for this program.  Individual circumstances might require some changes to this pathway.  It is always recommended that you meet with an academic counselor to develop a personalized educational plan.

Division

Academic Services, Business, Computer Technology, and Hospitality

Department

Computer Science

Career and Academic Pathway (CAP)

Business, Information Technology, Hospitality

Program Learning Outcomes

  1. Apply statistical techniques to analyze data sets, interpret results, to draw appropriate conclusions to support decision-making processes.

  2. Use programming skills in languages commonly used in data science, such as Python or R, to manipulate data, perform analysis, and create visualizations.

  3. Demonstrate proficiency in collecting, cleaning, and managing various types of data from diverse sources, including structured and unstructured data.

  4. Explain fundamental concepts of machine learning algorithms and apply them to solve predictive modeling and pattern recognition problems.

  5. Create visualization of data effectively using tools and libraries such as Matplotlib, Seaborn, or ggplot2 and communicate insights and findings clearly.

  6. Administer data using database management systems, including querying databases using SQL, designing databases using design principles, and data warehousing concepts.

  7. Discuss the ethical considerations and legal regulations surrounding data usage, privacy, and security, and apply ethical principles in their data-related work.

  8. Develop critical thinking and problem-solving skills to identify data-related issues, formulate research questions, and propose appropriate solutions.

  9. Collaborate effectively with team members, communicate findings and insights to both technical and non-technical stakeholders, and contribute within interdisciplinary teams.

  10. Plan, implement, and administer data science projects effectively, including defining project scope, setting goals, allocating resources, and meeting deadlines.

  11. Employ data analysis techniques emphasizing open-source inclusivity, diverse methodologies, and ethical practices, focusing on addressing institutional inequities and ensuring data-driven decisions are accessible and fair across all communities.

Program Map

GE General Education
O Available Online
Program Requirement
P Program Prerequisites

Term 1

10 units
CIT 100 or CS 110
A survey course for the use of software tools such as word processing, spreadsheets, graphics, presentation and database using Microsoft Office.

Course Details:

OR
This course is an overview of the computer science discipline investigating the design and use of the computer devices, the art and science of problem solving and programming, the representation of data, human-computer interactions and ethical considerations, and information security principles. Also included is hands-on experience with command line and GUI operating systems; application of HTML, CSS, and scripts to web pages; and computer programming with an object-oriented language such as C++, Java, or C#.

Course Details:

  1. Transfers to CSU
    2. CIT 103 - Amazon Web Services (AWS) Academy: Cloud Foundations

    The Amazon Web Services (AWS) Academy Cloud Foundations course is intended for students who seek an overall understanding of cloud computing concepts, independent of specific technical roles. It provides a detailed overview of cloud concepts, AWS core services, security, architecture, pricing, and support.

    Course Details:

    CS 102 or CS 102H

    This course will cover the most common Python libraries as well as teach you programming best practices. We will explore different aspects of Python, including web, utility applications, machine learning, computer vision, IoT (Internet of Things), and data modeling applications. By the completion of the course, learners will complete a project using Python related to their major or area of interest.

    Course Details:

    1. Departmental Recommendation: MATH 102
      2. or
    2. Departmental Recommendation: MATH 115
    3. Departmental Recommendation: STAT C1000
    4. Departmental Recommendation: STAT C1000H
      5. or
    OR

    This course will cover the most common Python libraries as well as teach you programming best practices. We will explore different aspects of Python, including web, utility applications, machine learning, computer vision, IoT (Internet of Things), and data modeling applications. By the completion of the course, learners will complete a project using Python related to their major or area of interest. This course is intended for students in the Honors Program, but is open to all students who desire more challenging course work.

    Course Details:

    1. Departmental Recommendation: MATH 102
    2. Departmental Recommendation: MATH 115
    3. Departmental Recommendation: STAT C1000
    4. Departmental Recommendation: STAT C1000H

    Term 2

    14-15 units
    CS 104 - Data Programming with Python

    This course takes students further in Python knowledge and also explores the Python packages and libraries for different applications such as machine learning, data science, and artificial intelligence.  It also utilizes the popular packages such as Numpy, Pandas, MatPlotLib, SKLearn, and Pytorch.  It takes the students through the usage of popular coding notebook with the IPython kernel such as Jupyter, and the popular cloud GPU services.  By the completion of this course, students will have experience using notebook for coding using the aforementioned packages and services to further into area of interest related to programming, especially in the area of data science, machine learning, and artificial intelligence.

    Course Details:

    1. Departmental Recommendation: CS 102
    CS 189 - Introduction to Machine Learning
    This course will introduce you to the fundamental concepts and applications of Machine Learning, including data acquisition. Moreover, the course will focus on supervised and unsupervised learning and data modeling. By the end of this course, you will have a solid understanding of the basics of Machine Learning and will be able to apply it to real-world scenarios.

    Course Details:

    1. Prerequisite: CS 110
    2. Prerequisite: CIT 100
    CS 160 or MATH 180
    This course introduces the essential elements of data science using Python. Although some Python programming knowledge is helpful, it is not a requirement for this general introduction. This course covers data collection, importing, cleansing, wrangling, management, modeling, testing, validating, visualization and presentation. Basic statistical and machine learning techniques are introduced for prediction as well as solving engineering problems. Python programming language and Jupyter notebooks are used for practices.

    Course Details:

    1. Prerequisite: CIT 103
    OR
    Introductory course on data collection and management, data manipulation, data modeling, statistical inference, and statistical modeling with data. Students will gain experience using a computer programming language (e.g. Python, R, etc.) to carry out statistical analysis.

    Course Details:

      One course from the following:

      This is a comprehensive course in the development and maintenance of a database. It provides a working knowledge of designing a database that includes: setting field properties, storing, retrieving, printing, and indexing records, creating informational and technical queries, developing customized forms and reports, establishing different types of relationships, and integrating Access with the Web. The course emphasis is on developing a practical ability to use a database in a Windows environment with full graphical user interface functionality.

      Course Details:

      1. Departmental Recommendation: CIT 100
        2. or
      2. Departmental Recommendation: CIT 101
      This course focuses on the concepts of relational databases including database management systems, database design fundamentals and Structured Query Language (SQL).

      Course Details:

      1. Prerequisite: CIT 116
    1. Transfers to CSU
      2. This course introduces students to the fundamental concepts and applications of AI, including the various stages of AI project cycles. Moreover, the course will focus on the challenges and issues surrounding AI, such as ethics, bias, culture, regulations, and professional expectations. Students will have a solid understanding of the basics of AI and will be able to apply it to real-world scenarios.

      Course Details:

      1. Prerequisite: CS 110
      2. Prerequisite: CIT 100
      This course is an examination of intermediate object-oriented programming concepts and their application using the C++ language. Topics include programming control mechanisms; algorithm development; analysis of iterative and recursive solution complexity for various algorithms; templates and data structures; exception handling; object-oriented design and modeling; object-oriented programming in software engineering; pointers architecture.

      Course Details:

      1. Departmental Recommendation: CS 110
    2. Transfers to CSU
      3. This course surveys discrete structures used in computer science with an emphasis on applications. Topics covered include: functions, relations, and sets; basic logic; proof techniques; basics of counting; graphs and trees; and discrete probability.

      Course Details:

      1. Prerequisite: CS 110
      2. Prerequisite: MATH 102
    3. Transfers to CSU

      4. This course is an introduction to algorithmic analysis and data structures. Topics include formal computing algorithms, algorithmic strategies, and basic algorithm analysis; canonical data structures; intermediate recursion; human-computer interaction; professionalism and ethical behavior; software information assurance, software engineering, and software reuse.

      Course Details:

      1. Prerequisite: CS 130
      2. Prerequisite: CS 190
      3. Prerequisite: MATH 250
      4. Corequisite: CS 130
      5. Corequisite: MATH 250
    4. Transfers to CSU

      5. This course is an introduction to algorithmic analysis and data structures. Topics include formal computing algorithms, algorithmic strategies, and basic algorithm analysis; canonical data structures; intermediate recursion; human-computer interaction; professionalism and ethical behavior; software information assurance, software engineering, and software reuse. This course is intended for students in the Honors Program, but is open to all students who desire more challenging course work.

      Course Details:

      1. Prerequisite: CS 190
      2. Prerequisite/Corequisite: CS 130
      3. Prerequisite/Corequisite: MATH C2210