MA 235 - Introduction to Data Science

The course will prepare students to learn the more advanced concepts and skills of data science.

Statistical software will be used. No previous knowledge of a Statistics software package or programing is necessary. (Some experience with programming is recommended.)

2. Utilizing technology to create visual representations of statistical data.

3. Create and interpret hypothesis tests for population parameters.

4. Acquire and clean their own data, combining data from multiple sources, and data wrangle.

5. Demonstrate the use of complex models and begin to construct models of their own.

6. Formulate simple algorithms to solve problems, and can code them in a high-level language appropriate for data science work.

7. Create a written, structured presentation, or report consistent with Statistical literature.
Course Outline:
I. R Basics

I.A. Objects

I.B. Functions

1C. Scripts

II. Data

II.A. Tidy Data

II.B. Summarizing

II.C. Data Frames

III. Data Visualization

III.A. Plots

III.B. Robust Visualization

IV. Statistics

IV.A. Probability Distributions

IV.B. Random Variables

IV.C. Central Limit Theorem

V. Statistical Inference

V.A. Sampling methods

V.B. Confidence Intervals

V.C. p-Values

VI. Regression

VI.A. Simple Regression

VI.B. Multiple Regression

VI.C. Least Squares Estimates

VII. Data Wrangling

VII.A. Reshaping

VII.B Web Scraping

VII.C. Text Mining

VIII. Machine Learning

VIII.A. Smoothing

VIII.B. Predict Function

VIII.C. Regression

VIII.D. Nearest Neighbor

IX. Big Data

Applied work: 20-70%

Group work: 0-30%

Facilitated discussion: 0-20%
Mandatory Course Components:
Students will obtain a solid introduction to R as a functional programming language and will be able to use R to effectively compute statistical and graphical procedures. 
Equivalent Courses:
None