{
  "college": "rio-hondo-college",
  "edition": "2026-2027",
  "code": "DSCI",
  "name": "Data Science",
  "sourceUrl": "https://riohondo.curriqunet.com/catalog/iq/22181/25453/23652",
  "courses": [
    {
      "code": "DSCI 100",
      "subject": "DSCI",
      "number": "100",
      "title": "Foundations of Data Science",
      "units": "4",
      "hours": "72 Lecture",
      "advisory": "ENGL C1000, READ 101",
      "cid": [],
      "transfer": [
        "CSU",
        "UC"
      ],
      "description": "This course introduces basic programming and statistical concepts, including programming for data cleansing, manipulation, visualization, and statistical computation for intelligence gathering. Students apply common built-in language functions for analysis of real-world datasets, including global and local economic data, commercial business, document collections, and social networks. This course also delves into machine learning and data-driven decision-making using statistical concepts like hypothesis testing, confidence intervals via bootstrapping, regression and inference for regression, and predictive modeling. In the course, students also learn about social issues surrounding data privacy and ownership.",
      "notes": [
        "Enrollment requires appropriate placement (based on high school GPA and/or other measures), or completion of an intermediate algebra course."
      ],
      "offered": [
        {
          "term": "spring-2027",
          "name": "Spring 2027",
          "sections": 1,
          "open": 1,
          "url": "/c/rio-hondo-college/schedule/spring-2027/dsci/#dsci-100",
          "subject": "DSCI"
        }
      ]
    },
    {
      "code": "DSCI 101",
      "subject": "DSCI",
      "number": "101",
      "title": "Statistics for Data Science",
      "units": "4",
      "hours": "72 Lecture",
      "prerequisite": "DSCI 100",
      "cid": [],
      "transfer": [
        "CSU",
        "UC"
      ],
      "description": "This course is an introduction to descriptive and inferential statistics, emphasizing the combined use of mathematics and programming for data science applications. The course provides hands-on experience with data analysis using modern statistical software, including the interpretation of statistical findings. Topics include numerical and graphical summarization of central tendency and spread, probability, normal, and binomial distributions. This course also addresses sampling distributions, t-distribution, the chi-squared distribution, estimation, hypothesis testing, and analysis of variance with linear and multiple regression. Students work with big data and complete a research project employing simple statistical inference and advanced modeling techniques.",
      "notes": [],
      "offered": []
    }
  ]
}