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Data 1501 Course Outline

DATA 1501: Introduction to Data Science

Course Description

This course is intended to provide an introduction into the field of Data Science. Students will develop skills in appropriate technology and basic statistical methods by completing hands-on projects focused on real-world data and addresses the social consequences of data analysis and application.

Course Learning Outcomes

Required Outcomes for all Sections of the Course

(should account for 70 – 80% of course content)

  • Explain the importance of and be able to formulate a data analysis problem statement that is clear, concise, and measurable.
  • Identify and appropriately acknowledge sources of data.
  • Be able to apply basic data cleaning techniques to prepare data for analysis.
  • Be able to identify the categorical and/or numerical data types in a given data set.
  • Apply appropriate descriptive and inferential methods to summarize data and identify associations and relationships. 
  • Use appropriate tools and technology to collect, process, transform, summarize, and visualize data.
  • Be able to draw accurate and useful conclusions from a data analysis. 
  • Effectively communicate methods and findings in a variety of modes.
  • Differentiate between ethical and unethical uses of data science.

Additional Optional Learning Outcomes

(should account for 20 – 30% of course content)

  • Identify goals and methods of testing hypotheses.
  • Explain the bootstrap methods.
  • Identify legal issues surrounding the use of data.
  • Mine data to develop predictive models and evaluation.

Course Content

Provide a topical outline demonstrating the breadth and depth of the course. Please be as comprehensive as possible within the limits of an outline.


(70%-80% of course content):

What are data?

  • Sources of data, data collection and types of data
  • Sampling from a population
  • Data errors and appropriateness/Cleaning Data
  • The role of data in decision making at various levels of society

Methods of Data Analysis, including, but not limited to:

  • Distributions (including measures of central tendency and spread)
  • Expressions, names, and tables
  • Joins
  • Arrays
  • Functions
  • Modeling/mining the data

Using Computational Tools and Statistical Techniques for basic data manipulation

Interpreting results of the data analysis/Data Interpretation, possibly including, but not limited to the following:

  • Correlation
  • Chance
  • Decisions and error probabilities
  • Classification
  • Confidence intervals
  • Simulations
  • Empirical, Categorical, and Numerical Distributions
  • Assessing Models

Communicate data-driven insights in multiple media modes

  • Data visualization - (including graphs, charts, and histograms - univariate qualitative, univariate quantitative, bivariate)
  • Communication of the Data Science Findings and What It Means
  • Converting data into actionable information and the role of data in decision making at various levels of society

Ethical Aspects of Data Science

  • Accuracy
  • Misrepresentation
  • Privacy
  • Security

Additional topics

(20%-30% of course content):

  • A/B Testing
  • Experiments
  • Hypothesis testing
  • Regression/Least squares
  • Prediction intervals
  • Inference for the true slope
  • Bootstrap
  • Bagging
  • Clustering
  • Frequent Patterns (Shopping Basket Analysis)
  • Information Retrieval
  • Anomaly Detection
  • Legal issues surrounding data
  • Causality and Experiments

Instructional Strategies

  • Provide a list of the instructional strategies that will be used to achieve course learning outcomes, such as lecture or non-traditional methods such as online classes or the use of experiential instruction.
  • Lectures will be a blend of statistics and data science concepts and hands-on exploration of the topics using statistical software, including but not limited to R, Python, Excel, Google Sheets, etc.