Earn a Bachelor of Science (BS) degree in
Data Science Analytics from TESU in partnership with Statistics.com
BS degree in Data Science Analytics
Looking to earn a BS degree (perhaps a second one!) that will attract the attention of employers?
Earn a Bachelor of Science (BS) degree from Thomas Edison State University's Heavin School of Arts and Sciences (TESU) through a curricular partnership withThe Institute for Statistics Education at Statistics.com.
This innovative BS degree in Data Science Analtyics from TESU will train you in predictive modeling, forecasting, customer segmentation, data visualization and risk analysis.
The data science component (area of study - both core and electives) is taught online at Statistics.com. The General Education and Electives required by the program are completed via online courses and other credit earning options offered by TESU. TESU specializes in awarding credit for demonstrated college-level competencies. You may be able to earn substantial credit award based on your prior learning and colege-level expertise.
Planning my Program
This course will teach you how to choose an appropriate time series model, fit the model, to conduct diagnostics, and use the model for forecasting. Next three dates:November 23, 2018 to December 21, 2018show more dates >> Learn more and register >>
Interactive Data Visualization
This course covers the principles of the visual display of data, both for presentation and analysis. Next three dates:October 26, 2018 to November 30, 2018show more dates >> Learn more and register >>
Introduction to Social Network Analysis (SNA)
This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks. Next three dates:To be scheduled.show more dates >> Learn more and register >>
Introductory Statistics for Credit
This course is the equivalent of a semester course in introductory statistics. Academic credit can be earned via the American Council on education's Credit Recommendation Service.Next three dates:To be scheduled.show more dates >> Learn more and register >>
Optimization - Linear Programming
The course introduces the use of mathematical models for managerial decision making and covers how to formulate linear programming models for decision problems where multiple decisions need to be made in the best possible way while simultaneously satisfying a number of logical conditions (or constraints). You will learn how to use spreadsheet software to implement and solve these linear programming problems. Next three dates:August 17, 2018 to September 14, 2018show more dates >> Learn more and register >>
Predictive Analytics 1 - Machine Learning Tools
This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. The course includes hands-on work with XLMiner, a data-mining add-in for Excel. Next three dates:September 28, 2018 to October 26, 2018May 22, 2020 to June 21, 2020show more dates >> Learn more and register >>
Predictive Analytics 2 - Neural Nets and Regression
This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. The course includes hands-on work with XLMiner, a data-mining add-in for Excel. Next three dates:October 26, 2018 to November 23, 2018show more dates >> Learn more and register >>
Predictive Analytics 3: Dimension Reduction, Clustering and Association Rules
This course covers key unsupervised learning techniques - association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques. Next three dates:To be scheduled.show more dates >> Learn more and register >>
Financial Risk Modeling
This course teaches participants how to model financial events that have uncertainties associated with them. Next three dates:To be scheduled.show more dates >> Learn more and register >>
Integer & Nonlinear Programming and Network Flow
This course covers a number of advanced topics in optimization. You will learn: 1) how to formulate and solve network flow problems, 2) how to model and solve optimization problems where some or all of the decision variables must be integers, 3) how to deal with multiple objectives in optimization problems, and 4) techniques for handling optimization problems where the objective function or constraints are not linear functions of the decision variables. Next three dates:September 21, 2018 to October 19, 2018show more dates >> Learn more and register >>
Introduction to Python for Analytics
In this course you'll learn basic Python skills and data structures, move on to how to load data from different sources, rearrange and aggregate it, and finally how to analyze and visualize it to create high-quality products. Next three dates:September 07, 2018 to October 05, 2018show more dates >> Learn more and register >>
R Programming - Introduction 1
This course will provide an easy introduction to programming in R.Next three dates:November 02, 2018 to November 30, 2018show more dates >> Learn more and register >>
In this course you will learn how multiple linear regression models are derived, use software to implement them, learn what assumptions underlie the models, learn how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models. Next three dates:October 05, 2018 to November 02, 2018show more dates >> Learn more and register >>
Risk Simulation and Queuing
This course covers modeling technique making decisions in the presence of risk or uncertainty. Specific topics include risk analysis using Monte Carlo simulation for risk simulation, queuing theory for problems involving waiting lines, and decision trees for analyzing problems with multiple discrete decision alternatives.Next three dates:November 16, 2018 to December 14, 2018show more dates >> Learn more and register >>
Spatial Statistics with Geographic Information Systems
Spatial statistical analysis uses methods adapted from conventional statistics to address problems in which spatial location is the most important explanatory variable. This course will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS or Mapinfo. Next three dates:November 02, 2018 to November 30, 2018show more dates >> Learn more and register >>
SQL and R - Introduction to Database Queries
The purpose of this course is to teach you how to extract data from a relational database using SQL, and merge it into a single file in R, so that you can perform statistical operations. Next three dates:November 09, 2018 to December 07, 2018show more dates >> Learn more and register >>
Tuition and Fees: $5000
The above estimate includes the program registration fee, and individual course fees. It reflects considerable savings that are available if you pay the program cost upon enrollment. You also have the option of paying in monthly installments, or on a course-by-course basis:
- Pay for your program upon enrollment (check or credit card): $5000
- Pay for program upon enrollment via training or purchase order, or if needing quote or invoice: $5500
- Pay in 18 monthly installments: $290/month
- Pay as you go: $5,953
Who is this program for?
This program is aimed at working adults who want to learn to use the tools and problem-solving skills of statistics and analytics in the rapidly growing field of data science.
Cost for the data science courses from Statistics.com
$5,700 pay in full
$5,950 pay as you enroll
*does not include the cost of text or materials per course
Students need to apply directly to TESU to be admitted into the BS program, however, the Statistics.com courses may be taken before admission to TESU, provided that these students adhere to the ACE standards in their Statistics.com courses.
Transfer your Statistics.com course to TESU
Thomas Edison State University accepts the credit recommendations of the American Council on Education (ACE) CREDIT. All the courses listed in this program have been recommended for ACE CREDIT.
Register online at Statistics.com for the courses in this program
When the course begins, you will specify your marking preference- choose "I will be seeking academic credit recommendation through ACE and I am a student at TESU."
Successfully complete the course and pass the proctored online exam
Create your own account at the ACE Registration site
Have your ACE transcript sent to Thomas Edison State University, Office of the Registrar, 111 W. State St., Trenton, NJ 08608
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