Certificate of Achievement (Transcripted): Data Analytics

Below is a sample academic plan that shows you how you can complete your Certificate in a timely manner. Your path may look different depending on your circumstances?for example, if you attend part-time, choose to take Intersession or Summer courses, or have earned prior credit (Credit for Prior Learning, Dual Enrollment, Transfer Credit). You are required to meet with a counselor to set up a comprehensive education plan that is tailored to your individual circumstances and goals.

The Certificate of Achievement for Data Analytics is designed for students who have an interest in working with datasets, database systems, SQL (Structured Query Language), Pandas, Jupyter, and other related DA tools. The program is also tailored for working professionals who might want to change careers in BIG DATA or Data Analytics.
 

Demonstrate writing SQL scripts for Data Analytics.

Demonstrate using libraries for SQL and Pandas.

Demonstrate analyzing statistical information.

Program Map

GE General Education Course
M Major Course
E Elective Course
SI Summer/Intersession Course *
* Course is appropriate for Summer and Intersession, which are accelerated terms.

Getting Started

Comprehensive Education Plan: You are required to meet with a counselor for a comprehensive educational plan that is tailored to your individual circumstances and goals. Please schedule an appointment with a counselor in your first semester.

First Semester

7 units
CMPR 114
Python Programming
Students will be introduced to the syntax of Python, methods used to incorporate Python into creating business applications, and create interactive forms. Students will also learn to enhance business applications using interactive programming utilizing forms, objects, looping constructs, variables, classes, and the use of various data types.
    STAT C1000
    This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. Formerly: MATH 219 - Statistics and Probability.
    This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. This course has embedded support. Formerly: Math 219S - Statistics and Probability with Support.
    This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. This is an honors course. Formerly: Math 219H - Honors Statistics and Probability.
    Introduces psychology and behavioral science majors to descriptive and inferential statistical methods. Knowledge of these methods is essential to the understanding, interpretation, and performance of scientific research. Topics covered include probability theory, hypothesis testing, correlation, analysis of variance, the graphical representation of data, basic research design, and the use of computer software to perform statistical analyses.

    Second Semester

    8 units
    CMPR 167
    Microsoft Access
    Relational Database Management using Microsoft Access. Includes design, creation and maintenance of a RDBMS, reports and form generation, queries, importing and exporting data, macros and modules using Access Basic.
      CMPR 115
      Python for Data Analytics
      The student will learn fundamentals for Data Analytics using Python and will learn skills such as an introduction to data analysis, understanding data, data processing, writing python code, using IPython shell, reading and writing data using Panda, interacting with databases, data manipulation, and data visualization using the library called matplotlib. This course will prepare students to become a Certified Python Data Analytics Professional.
        BA 188
        Microsoft Excel
        Build essential Excel skills to create, edit, and manage business spreadsheets. Learn formulas, functions, charts, sorting, filtering, and formatting techniques using current business standards. Apply Excel to real-world tasks such as budgeting, data organization, and reporting. Use AI tools (e.g., Microsoft Copilot) to generate formulas, analyze data, and streamline spreadsheet tasks. Includes the Microsoft Office Specialist (MOS) Excel exam, earning an industry-recognized certification valued by employers.