Introduction to Data Science

You may say, TASMN and Data Science, but one of our community members, who is expert in his field, is willing provide an introductory course on Data Science. If you are interested please continue to read.
Space is limited, please RSVP soon.

Course Description
This course is intended to provide an introduction to data science to gain insights from various forms of
data. Particular attention will be given to basic statistical concepts, data visualization, and introductory
machine learning methods. R programming language will also be used for processing and visualization of
data and running machine learning methods.

Prerequisite
• Willingness to learn.
• Be able to dedicate two hours per week outside of the lecture.
• Willingness to learn basic computer programming (one cannot be a data scientist without being
a competent programmer).

Course Goals
After completing the entire course, you should be able to:

1. Become proficient in basic statistical concepts such as variance, distribution, correlation,
   precision, and sensitivity.
2. Become familiar with data visualization methods such as scatter plots, histograms, box plots,
   and smoothing.
3. Differentiate between regression and classification.
4. Build basic machine learning models and interpret the results.

Instructor Information
Mustafa Sir, Ph.D.
Assistant Professor, Information & Decision Engineering
E-Mail: mustafa.y.sir@gmail.com
Google Scholar: https://scholar.google.com/citations?user=mPQwlnQAAAAJ&hl=en

Research Interest: My research expertise is in developing data-driven optimization methods to address
challenges in improving patient access to medical services, reducing cost, and making clinical decisions.

Tentative Course Content and Schedule
Week 1 FEB 24th 2019

1. Introduction to R

• Installing and introducing RStudio
• Basic commands
• Loading data
• Viewing data
• Data wrangling with dplyr

2. Data visualization

• ggplot
• Scatter plots
• Smoothing
• Histogram
• Box plots