New students can start the program in spring or fall semesters.
What is Data Science?
Data Science is an inter-disciplinary field of study that deals with capturing, maintaining, processing, and analyzing data, as well as communicating the results of data analysis. The field continues to prove to be one of the most promising and in-demand career paths for skilled professionals
Effective data scientists are able to identify relevant questions, collect data from a multitude of data sources, clean and organize the information, analyze the information, translate results into solution, and communicate the findings in a way that positively affects business decisions. Lander University's data science program educates and trains you in these skills. After completing our Data Science program, you will be equipped with both the underlying theory and the skills to apply that theory in the real-world.
Data Science Curriculum
The curriculum of Lander's Data Science program covers the broad set of skill sets required by data science. The courses in the program are designed to provide instruction and experience in problem solving, data science programming, statistics, big data analysis, data visualization, machine learning and its mathematical theory. The program culminates in a capstone course in which you will apply what you have learned in a real-world scenario to solve problems or make decisions based on data. The courses provide balanced theory and hands-on experience using the latest computer tools. Ambitious students are welcomed for research with faculty members as well.
Lander's data science program hosts a machine learning server equipped GPUs and a large main memory to host a large number of sessions at the same time. It is currently used in data science courses and research.
Emphases Offered
Since Data Science is an interdisciplinary field, the program offers emphases in three separate subjects -- Business Analytics, Computer Information Systems, and Mathematics. Each emphasis is designed to provide courses to deepen the understanding in each area. If a student is more interested in discovering and applying business intelligence for organizations, Business Analytics emphasis provides a curriculum with business contexts. For students interested in careers as data science developers, Computer Information Systems emphasis should be an excellent choice. The Mathematics emphasis offers an opportunity to study theoretical aspects more in depth and provides the mathematical skills required of many graduate programs.
Why Become a Data Scientist?
For the past six years, Glassdoor has ranked Data Scientist as #3 or better in their Best Jobs in America report. The median base salary is reported as $113,736 with over 29,000 jobs posted. A search of "Data Scientist" listings posted at indeed.com on March 2, 2020 produced approximately 12,000 job vacancies in the U.S. including over 1,800 in the eight southeastern states. At linkedin.com, the search produced 22,000 results nationally, and over 1,400 jobs in South Carolina and its two neighboring states. The jobs are from diverse industries such as insurance, finance, healthcare, biotechnology, IT, education, retail, sports, just to name a few.
There are diverse career paths for the graduates. The job titles include data scientist, data analyst, data engineer, analytical scientist, business intelligence analyst, machine learning engineer, machine learning scientist, machine learning software engineer, and many more.
Frequently Asked Questions
Note: The information below provides convenient links to some of the courses required for this degree; however, it should not be used as a course registration guide. Please refer to the official Lander University Academic Catalog for the most accurate and up-to-date program requirements.
| GENERAL EDUCATION REQUIREMENTS1 | CREDIT HOURS |
||
|---|---|---|---|
| A. Core Skills |
|
||
| ENGL 101 | Writing and Inquiry I | 3 | |
| ENGL 102 | Writing and Inquiry II | 3 | |
| MATH 123 |
Calculus and its Applications |
3 | |
|
B. Humanities and Fine Arts |
6 | ||
| C. Behavioral and Social Perspectives (6 hours selected from 2 different disciplines) |
6 | ||
| D. Scientific and Mathematical Reasoning | |||
| MATH 211 | Statistical Methods I | 3 | |
| Approved Lab Science | 4 | ||
| E. Founding Documents of the United States | |||
| HIST 111R2 | United States History to 1877 OR HIST 112R2 United States History since 1877 OR POLS 101R2 American National Government |
3 | |
| F. World Cultures | 3 | ||
| G. LINK 101 | 1 | ||
| Total General Education Requirements | 35 | ||
1 For approved courses see the General Education section
2 If you already have credit for HIST 111, do not take HIST 111R; if you already have credit for HIST 112, do not take HIST 112R; if you already have credit for POLS 101, do not take POLS 101R
| MAJOR PROGRAM CORE REQUIREMENTS | CREDIT HOURS |
|
|---|---|---|
| CIS 120 | Fundamentals of Information Systems & Information Technology | 3 |
| CIS 130 | Problem Solving and Programming Methods | 4 |
| CIS 230 | Computer Programming Principles I | 4 |
| CIS 234 | Introduction to C/C++ Programming | 1 |
| CIS 360 | Database Design | 3 |
| DSCI 130 | Introduction to Data Science | 3 |
| DSCI 230 | Introduction to Data Science Programming | 3 |
| DSCI 231 | Data Visualization | 3 |
| DSCI 330 | Big Data Analysis | 3 |
| DSCI 340 | Applied Machine Learning | 3 |
| DSCI 440 | Applied Deep Learning | 3 |
| DSCI 499 | Data Science Capstone | 3 |
| MATH 125 | Introduction to Discrete Mathematics | 3 |
| MATH 208 | Applied Linear Algebra | 3 |
| MATH 213 | Supervised Machine Learning | 3 |
| MATH 214 | Unsupervised Machine Learning | 3 |
| MAJOR PROGRAM EMPHASIS REQUIREMENTS | CREDIT HOURS |
|---|---|
| Select 15 hours CIS 140, CIS 231, CIS 240, CIS 250, CIS 320, CIS 321, CIS 498 |
15 |
| Total Major Program Requirements | 64 |
| Additional Electives (3-9 hours must be 300- or 400-level) |
21 |
| TOTAL FOR BS DEGREE | 120 |