Data Analytics and Predictive Modeling - Major #2800 - Certificate of Achievement

This program map only provides one possible pathway for degree and/or certificate obtainment and is for reference only. Please schedule an appointment with a counselor to develop your individualized Student Education Plan.
It is also important to discuss your transfer options with a counselor because each university will require specific courses.

This certificate will provide students with experience in the field of data science including such areas as data management, data analysis, data collection, and data visualization. It is suitable for students who wish to begin work in the field, for those who wish to supplement their existing coursework with additional experience in these data science areas, and for students who have obtained a bachelor's or other degrees in any number of analytical and scientific fields and wish to upgrade or update their skills and training.

1. Extract data from a database to solve data-related problems using programming languages used for data science and statistical software.

2. Produce, communicate, and interpret data analysis using data visualization, numerical summaries, modeling, and statistical inferences.

3. Recognize questions that can be investigated, the data source to select to make the investigation and understand the methods of randomly collecting data.

4. Fit, Interpret and evaluate statistical models for prediction and inference.

5. Acquire data from various sources, maintain databases, and perform data exploration techniques to prepare data for analysis.

6. Provide students with mathematical tools to help them form a mathematical foundation for data science.

Program Map

Term 1

13-14 units
CIT 28
Client/Server Databases
This course covers the fundamentals of relational databases: how to design, connect, create, and query tables using Structured Query Language (SQL). (A, CSU)

Course Details:

      1. CSU Transfer
        DS 10
        Introduction to Data Science
        This course is an Introduction to the foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. The course teaches critical concepts and skills in computer programming and statistical inference in conjunction with hands-on analysis of real-world datasets, as well as social issues surrounding data analysis such as privacy and design. (A, CSU, UC)

        Course Details:

        1. Advisory: ENGL C1000
          1. CSU Transfer
            Finite Mathematics
            In this course students will learn the following mathematical concepts: applications of linear; exponential and logarithmic functions; solving systems of linear equations using matrix operations and inverses; solving linear programming techniques using graphing methods and simplex methods; applying interest theory concepts to solve mathematical finance problems; calculate probability; determine the number of values within sets using Venn Diagrams and counting principles; use graphs and properties to determine limits, rates of change, and derivatives of a function. (A, CSU-GE, UC, I)

            Course Details:

            1. Prerequisite: MATH 103
              2. OR
            2. Prerequisite:
              1. CSU Transfer
                OR
                This course includes applications of linear, exponential, and logarithmic functions; matrix operations; and inverse matrices; linear programming techniques, mathematics of finance, probability, and counting theory; as well as limits, rates of change, and derivatives. (A, CSU, UC, Cal-GETC)

                Course Details:

                1. Prerequisite: MATH 103
                  2. OR
                2. Prerequisite:
                  1. CSU Transfer
                    Business Statistics
                    This course covers using probability and predictive techniques to facilitate decision-making using data from disciplines including business, social sciences, psychology, life sciences, health sciences, and education. The analysis will incorporate EXCEL/Other Statistical Software and a graphing calculator. Descriptive measures include central tendency and dispersion, probability theory, discrete and continuous probability distributions, sampling distributions, central limit theorem, time series, index numbers, statistical inference using one and two sample hypothesis tests using both the standard and t distributions for both means and proportions, estimation, correlation, regression, analysis of variance (ANOVA), and nonparametric methods including Chi-squared. (A, CSU-GE, UC, I)

                    Course Details:

                    1. Prerequisite: MATH 103
                      2. OR
                    2. Prerequisite:
                      1. CSU Transfer
                        OR
                        (A, CSU, UC, Cal-GETC)

                        Course Details:

                        1. Prerequisite:
                          1. CSU Transfer
                            OR
                            This course includes the following topics: descriptive statistics, sampling, hypothesis testing, estimation, selected non-parametric techniques, ANOVA, and regression, with applications from psychology, biology, and social sciences. This course is designed for psychology majors and UC transfers. (A, CSU, UC, Cal-GETC)

                            Course Details:

                            1. Prerequisite: MATH 103
                              2. OR
                            2. Prerequisite:
                              1. CSU Transfer
                                OR
                                This course includes the following topics: descriptive statistics, sampling, hypothesis testing, estimation, selected non-parametric techniques, ANOVA, and regression, with applications from psychology, biology, and social sciences. This course is designed for psychology majors and UC transfers. (A, CSU, UC, Cal-GETC)

                                Course Details:

                                1. Prerequisite: MATH 103
                                  2. OR
                                2. Prerequisite:
                                  1. CSU Transfer

                                    Term 2

                                    6-8 units
                                    Major Course

                                    Select an emphasis and take the two courses listed. 

                                    Most emphasis courses are only offered in the spring semester. 

                                    In this course, students will learn a deeper dive into topics introduced in introductory statistics and data sciences courses with an emphasis on application to real-world problems using the R programming language. (A, CSU, UC)

                                    Course Details:

                                    1. Prerequisite: DS 23
                                      2. OR
                                    2. Prerequisite: STAT C1000
                                      3. OR
                                    3. Prerequisite: MATH 42
                                      4. OR
                                    4. Prerequisite: PSYC 42
                                    5. Advisory: ENGL C1000
                                      1. CSU Transfer
                                        AND
                                        In this course, students will learn to create a variety of dashboard and graphics to display data using a variety of software packages. (A, CSU)

                                        Course Details:

                                        1. Advisory: ENGL C1000
                                          1. CSU Transfer
                                            OR
                                            This course covers the use of the Python programming language, and involves activities such as analysis, understanding, solving problems using algorithms, correctness and resource requirements, coding of algorithms in proper syntax, testing, debugging, maintaining source code, documenting and implementation of the built system and its management. (A, CSU, UC)

                                            Course Details:

                                            1. Prerequisite: CIT 15
                                            2. Advisory: ENGL C1000
                                              1. CSU Transfer
                                                AND
                                                This course is an introduction to machine learning, datamining, and statistical pattern recognition. (A, CSU)

                                                Course Details:

                                                    1. CSU Transfer

                                                      Term 3

                                                      1 unit
                                                      CIT 19
                                                      Work Experience Education
                                                      The purpose of this course is to create supervised employment experience, extending the classroom to a real-life occupational employment opportunity in computer information technology. This course requires collaborative learning objectives established specific to computer information technology. (A, CSU)

                                                      Course Details:

                                                          1. CSU Transfer