DATA 101 Introduction to Data Science

Lecture Hours: 4
Credits: 4

Explores fundamental concepts in data science using a quantitative approach. Introduces computational and analytical techniques used in data science and data analytics, including the use of spreadsheets, programming and statistical inference. Exposes students to the profession of data science including the practice of using data to make decisions and the ethical components of using data. Data sets from a variety of real world domains will be used throughout the course.

Prerequisite: Placement into WR 115 (or higher), or completion of WR 090 (or higher); and placement into MTH 105Z or higher; or MTH 095 or higher; or consent of instructor. (All prerequisite courses must be completed with a grade of C or better.)
Student Learning Outcomes:

Discover, organize, analyze, and visualize data using spreadsheets and the Python programming language
Appropriately handle data and apply the data lifecycle. Identify potential errors and sources of bias in data collection and analysis.
Apply concepts of statistical inference including sampling and simulation to create and test models.
Determine the reasonableness and implications of solutions by recognizing the limitations of the method used in context.
Develop and test null and alternative hypotheses to answer domain-specific questions (questions in domains such as science, business, economics, political science, journalism, athletics)
Outline ethical ramifications of data collection, data-driven decision making, data sharing, and privacy
Effectively communicate, orally and in writing, using appropriate representations, quantitative results, and computational processes.
Describe the nature of data science as a profession including career and educational opportunities in data science.

Content Outline

Introduction to Data Science
- The nature of data.
- Data in statistics.
- Data in solving real-world problems.
Programing in Data Science
- Referencing
- Expressions, variables and data types
- Predefined functions.
- User defined functions.
- Operations on tables.
- Display of data
Describing, Exploring, and Comparing data.
- Data representations.
- Real world situations.
- Causality versus association.
Sequences and Tables
- Definition of a sequence.
- Basic statistical calculations on sequences.
- Visual representations of data.
Functions and Tables
- Function definition.
- Function types.
- Conditional statements and iterations.
- Manipulation of tables.
Probability and Chance.
- One variable statistics.
- Pivot tables.
- Real world models.
- Distribution of data.
- Decision criteria.
Comparing Samples.
- Hypothesis testing and the test statistic.
- Causality.
Ethics of data science
- Privacy concerns.
- Disclosure of public data.
- Best practice policies.