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    Data Science

    Data Science woman

    Preparing students for careers in the rapidly growing field of data analysis and modeling.  Through practical coursework and projects, students learn to write code, work with large datasets, and apply advanced techniques to solve complex problems in various industries.

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    Department Overview

    Data science is an interdisciplinary field focused on discovering patterns and describing relationships using data. It uses techniques from both computer science and math, particularly statistics. Data science typically involves building, testing and interpreting a data model, a representation of a real-life system that organizes data elements and informs how the elements relate to one another. Data scientists use computers to write code and store, modify, and visualize large datasets.

    DUBOIS Project at Laney College
    Laney College is collaborating with UC Merced and UC Berkeley to develop an inclusive, interdisciplinary data science curriculum. This innovative project integrates computer programming, mathematics, and social sciences to empower students with skills for analyzing and interpreting data in real-world contexts. By prioritizing equity and accessibility, the DUBOIS Project positions Laney College as a leader in preparing students for success in the growing field of data science.

    Career Opportunities

    A degree in data science opens the door to a wide range of exciting and high-demand career opportunities. Some popular roles for data science graduates include:

    Data Scientist – Analyze and interpret complex datasets to uncover trends and insights, build predictive models, and develop machine learning algorithms to solve real-world problems.

    Data Analyst – Use statistical tools to interpret data and provide actionable insights, create reports, dashboards, and visualizations to support business decisions.

    Machine Learning Engineer – Design and implement machine learning algorithms for automation and predictive analytics, work on AI applications like natural language processing, computer vision, and recommendation systems.

    Data Engineer – Develop and maintain data pipelines and architectures for processing large datasets, ensure the availability, scalability, and reliability of data systems.

    Business Intelligence Analyst – Analyze business data to identify opportunities, improve operations, and inform strategies, using tools like Tableau, Power BI, or Qlik to create insightful visualizations.

    Statistician – Apply statistical methods to interpret data and solve problems across industries like healthcare, finance, and government.

    Big Data Engineer/Architect – Design and manage big data frameworks like Hadoop or Spark, optimize the performance of data infrastructure.

    AI Researcher – Conduct research in artificial intelligence to develop new technologies and algorithms.

    Quantitative Analyst (Quant) – Use mathematical and statistical methods to evaluate financial markets and develop investment strategies.

    Data Consultant – Provide expert guidance on data strategies and analytics solutions to businesses and organizations.

    Industries hiring data science professionals include technology (e.g., Google, Meta, Amazon), finance (e.g., investment banks, fintech companies), healthcare (e.g., predictive modeling, genomics), retail and e-commerce (e.g., customer analytics, supply chain optimization), government and nonprofits (e.g., policy analysis, public health research), and media and entertainment (e.g., recommendation systems, audience analytics).

    Contact Us

    Department Chairs
    Johnnie Williams (Co-Chair CIS Department)
    Email: jwilliams@peralta.edu

    Kim Bridges (Co-Chair CIS Department)
    Email: kbridges@peralta.edu

    Dean Math, Sciences & Engineering
    Inger Stark
    Email: istark@peralta.edu
     
    Dean of Humanities, Social Sciences, & Library
    Tarek ElJarrari
    Email: teljarrari@peralta.edu
     
     

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    Degrees & Certificates

    Data Science with math
    Data Science - A.S. Degree

    This Degree is designed for students who complete the first two years of college math, statistical analysis, and computer programming. It differs from our transfer degree in the IGETC or CSU Breadth Requirements.

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    Data Science employees
    Data Analytics - A.S. Degree

    This Degree is an expansion on the CIS department Data Science Fundamentals Certificate of Achievement and prepares students for careers as data analyst, data scientist, machine learning engineers, business analyst, computer systems analyst, logistics analyst, marketing analyst, and database managers. 

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    Computer Information Systems Degree
    Data Science Fundamentals - Certificate of Achievement

    This Certificate prepares students for careers as data analyst, data scientist, machine learning engineers, business analyst, computer systems analyst, logistics analyst, marketing analyst, and database managers

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    Data Science Courses

    Mathematics (MATH) Courses

    Math 118 Foundations in Data Science

    Course Number: MATH 118 (cross listed with CIS 118)
    Units: 4
    Class: 3 hours lecture, 2.5 hours laboratory (GR)
    Prerequisite(s) MATH 203
    Acceptable for credit: CSU, acceptance for UCs pending

    Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

    MATH 13 Introduction to Statistics

    4 units, 4 hours lecture (GR)
    Prerequisite: MATH 203 or MATH 206 or MATH 211D or MATH 230 or MATH 240.
    Acceptable for Credit: CSU, UC

    Introduction to theory and practice of statistics: Collecting data, Sampling; observational and experimental studies. Organizing data: Univariate and bivariate tables and graphs; histograms. Describing data: Measures of location, spread, and correlation. Theory: Probability; random variables; binomial and normal distributions. Drawing conclusions from data: Confidence intervals; hypothesis testing; z-tests, t-tests, and chi-square tests; one-way analysis of variance. Regression. Non-parametric methods.     1701.00
    AA/AS area 4b; CSU area B4; IGETC area 2A;
    (C-ID: MATH 110)

    Business (BUS) Courses

    BUS 43B Introduction to Microsoft Excel for Business Applications

    Course Number: BUS 46B
    Units: 4
    Class: 3 hours lecture, 3 hours laboratory (GR or P/NP)
    Prerequisite(s) Working knowledge of computer and internet and knowledge of basic mathematical skills.
    Acceptable for credit: CSU

    Introduction to spreadsheets using Microsoft Excel Windows version on the PC with emphasis on business applications: Calculations using functions and formulas; modifying, changing, and formatting cell entries; saving, retrieving, and printing worksheets; linking and consolidating spreadsheets; creating charts; working with database features; and using macros.  0514.00

    Computer Information Systems (CIS) Courses

    CIS 116 Introduction to Computational Thinking with Data

    4 units, 3 hours lecture, 3 hours lab, (GR)
     
    Acceptable for Credit: CSU
    Not open for credit to students who have completed MATH 116.

    Introduction to computational thinking with data and quantitative reasoning: Collecting data, sampling, and simulation; tables, graphs and data manipulation; histograms and distributions; elements of good programming style.     0707.00

    CIS 6 Introduction To Computer Programming

    Course Number: CIS 6
    Units: 5
    Class: 4 hours lecture, 3 hours laboratory (GR or P/NP)
    Prerequisite(s) None
    Acceptable for credit: CSU, UC

    Introduction to computer programming: Algorithm design, flow charting, and debugging; elements of good programming style. Course may be instructed in any programming language. 0707.10

    CIS 25 Object-Oriented Programming Using C++

    Course Number: CIS 25
    Units: 4
    Class: 3 hours lecture, 3 hours laboratory (GR or P/NP)
    Prerequisite(s) CIS 6 or 26
    Acceptable for credit: CSU, UC

    Object-oriented methods of software development using C++: Design and implementation of objects, class construction and destruction, encapsulation, inheritance, and polymorphism. 0707.10

    CIS 36 JAVA Programming Language I

    Course Number: CIS 36A
    Units: 4
    Class: 3 hours lecture, 3 hours laboratory (GR or P/NP)
    Prerequisite(s) CIS 25 or 26 or 215
    Acceptable for credit: CSU, UC

    Introduction to object-oriented program design: Overview of the Java programming language including developing applets for web pages and stand-alone applications. 0707.10

    CIS 36 JAVA Programming Language II

    Course Number: CIS 36B
    Units: 4
    Class: 3 hours lecture, 3 hours laboratory (GR or P/NP)
    Prerequisite(s) CIS 36A, 25 and familiarity with Java Programming Language
    Acceptable for credit: CSU, UC

    Object-oriented program design using the Java programming language: Designing and programming with exceptions, threads, file input/output (I/O); networking and graphics classes; developing code using tools such as Java 2D API and SWING; and working with projects in areas such as animation. 0707.10

    CIS 98 Database Programming with SQL

    Course Number: CIS 98
    Units: 4
    Class: 3 hours lecture, 3 hours laboratory (GR or P/NP)
    Prerequisite(s) CIS 6 or 25 or 36A or 61
    Acceptable for credit: CSU, UC Programming in database management systems using SQL: DML (Data Manipulation Language) and DQL (Data Query Language) features; database program design, programming structures, strategies, and techniques. 0707.20 AA/AS area 4c

    CIS Open Lab

    Phone: 510-464-3455
    Location: Building G, Rm 273 (G-273)

    Hours: Monday – Friday | 9:30am – 2:30pm

    CIS Open Lab is held when the computer labs are not in use for classes. These times may vary from day to day, five days of the week, but it allows students time to use computers during school hours for research and homework.

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    Student Support Services

    Laney College offers a comprehensive range of student support services to help students achieve their academic and career goals, including tutoring, academic advising, and career counseling.

    Additional resources such as mental health services, disability support, and financial aid assistance are also available.

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    Tutoring Services

    Phone: 510-464-3155
    Location: Building A, Rm 105 (A-105)

    Hours: Monday – Thursday | 10:00am – 6:00pm

    Tutoring is limited, but some CIS services are available in-person (CIS 5 and CIS 6, Tuesday mornings 9:30am-12pm in G273).

    Please ask the lab staff about CIS tutoring in general, or email the Tutoring Manager for more information.

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    CIS Open Lab & Tutoring

    CONTACT INFO
    Phone: (510) 464-3455

    Lab Technicians
    Maribel Bazan
    Email: mbazan@peralta.edu

    Tuan Doan
    Email: tdoan@peralta.edu

    CIS Lab Hours
    Monday - Friday
    9:30am – 2:30pm
     

    CIS Lab Location
    Building G, Room 273 (G-273)

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