Computer Science: Data Science Option - AS (232D)

Division: Mathematics, Engineering Technologies and Computer Sciences (METCS) Division

General Education Requirements (30 Credits)
Written & Oral Communication (6)
ENG 101College Composition I3
ENG 102College Composition II3
Quantitative/Scientific Knowledge, Skills & Reasoning (15)
MTH 101Statistics and Probability I4
MTH 121Calc with Analytic Geom I4
MTH 239Introduction to Linear Algebra3
PHY 103General Physics I4
Society & Human Behavior (3)
Select one of the following:3
ANT 101, ANT 105, ECO 101, ECO 102, POL 104, PSY 101, PSY 102, PSY 219, SOC 101, SOC 108, SOC 219
Humanistic Perspective (3)
Select any English Literature course3
or Select one of the following courses:
Historical Perspective (3)
Select any History (HST) course3
Major Requirements (30 Credits)
CSC 121Computer Science I3
CSC 122Computer Science II3
CSC 225Data Structures3
CSC 231Database Design4
MTH 122Calc with Analytic Geom II4
MTH 141Mathematical Statistics3
DSC 141Data Science I - Fundamentals2
DSC 241Data Sci II - Machine Learning2
DSC 242Intro to Artificial Intel3
DSC 243Data Visualization3
Total Credits60
  •  If you do not place into MTH 121 Calc with Analytic Geom I the prerequisites are: MTH 100 Intro. to College MathematicsMTH 119 Pre-Calculus I and MTH 120 Pre-Calculus II. Math Placement is determined by the Mathematics Department. These courses should be taken in high school or the summer before your first semester at ECC.

    Notes:

    1. For an explanation of why General Education courses are included in this Program, please refer to the Section on General Education for an explanation of its Purpose and Requirements. 
    2. This plan assumes the student is eligible to enroll in College Level Courses (designated as 100 +, e.g., ENG 101 College Composition I, HST 101 World Civilization I, MTH 100 Intro. to College Mathematics).  Placement results will determine College Level Readiness in English and Mathematics.  
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Upon completion of this program, graduates will be able to:

  • Evaluate the role of data scientists and data analysts;
  • Identify and apply commonly used data collection methods;
  • Describe and apply common data cleansing methods;
  • Learn and apply programming languages to manipulate data;
  • Explain the role of the tools used for performing data analysis;
  • Apply artificial intelligence techniques to real-world problems;
  • Describe how data visualization helps in data representations; and
  • Utilize data visualization tools to display data.