This program of data science analyses how to build systems and algorithms to extract knowledge, find patterns, generate insights and predictions from diverse data. It combines techniques and theories from mathematics, statistics, computer science, and information science to find meaning in data so that the derived knowledge can be used to make informed decisions. It also explores big data, a field that intends to analyze data sets that are too large or complex to be dealt with by traditional processing software.
We also offer this program as a bachelor's or Ph.D.
Data Science Online via distance learning
This module is applicable to Specialist, Expert, Bachelor's, Master's & Ph.D. (Doctor) Degree Programs. This academic program is designed at the postgraduate level (Master’s or Doctoral). This module may also be adapted to complete the course requirements of Specialist, Expert Diploma, or Bachelor’s Degree. A further option is an enrollment into each of the courses listed within this specialization module. This module may be combined or completed with other modules from this faculty. For example: Bioinformatics - Cyber Security - Information Science - Information Technology - Library & Data Management - Management Information Systems - Museology - Statistics.
Method of instruction: Distance Learning Higher Education
This distance learning education program is completed by a traditional correspondence instruction method. Once you sign up for the course, Bircham International University will send you (to your mailing address) the suggested textbooks. After reading the book, you will be asked to write a 20 to 35 pages report that reflects your understanding of the book. This report is equivalent to the exam and can be submitted by email or mail. Bircham International University will evaluate your written work. If passed, BIU will issue the corresponding diploma. For more instructions about BIU pedagogy, tutoring, and evaluation, please read our distance learning education study guide.
Students enrolling in this distance education program should be aware that:
Location: Bircham International University needs a functional geographical location to ship the book and materials for successful completion of this program of study.
Communication: Email, courier, phone are key communication instruments with Bircham University that play an important role in the progress and support of this program of study.
Capacity: Any impediment, physical or psychological, to read a book and write a report must be communicated to Bircham International University prior to enrollment into this distance learning program.
Technology: No specific technology is required to complete this distance education program.
Language: Book reading and report writing in another language other than English must be requested (and approved by BIU) prior to enrollment in any distance learning program.
Discrimination: There is no discrimination with respect to race, color, gender, beliefs, or religion.
Age: Check the admission requirements for each distance education degree program.
Duration - Data Science Online via distance learning
For a program of 21 credits, the estimated time for completion is 21 weeks. For 45 credits, the estimated time for completion will be 45 weeks, and so on. Calculations are approximate. The length of each distance learning degree program is calculated based on an average of 15 hours of learning per week. It also depends on the number of validated credits from previous knowledge and the level of commitment to the studies.
Master's Degree - Data Science Online
Tuition Fee: Min. 4.680 Euros (6.120 US$) | Max. 7.020 Euros (9.180 US$).
Master's Degree Online: 36 - 54 Academic credits required for this distance learning degree program.
Composition: Data Science via distance learning = 39 Academic credits + Additional courses may be selected from other modules in the Faculty of Computer Science from Bircham International University if required. This selection must be approved by the Distance Learning University Education Board. For example Information Science. + 13 Academic credits (Research methodology and final project or thesis.
Courses list (each subject accounts for 3 academic credits):
1 BIU Earned Credit = 1 USA Semester Credit (15 hours of learning) = 2 ECTS Credits (30 hours of study).
You may study any subject as an independent online continuing education course.
Computation & Data Structure
This course explains data structures and algorithms in computer science. It includes recursion, sequential, linked, and dynamic allocation of storage, stacks, queues, trees, graphs, hash tables, as well as internal and external sorting and searching. Emphasis is placed on the design, implementation, and evaluation of algorithms.
Academic Supervisor: Kenneth K. PembamotoData Communications
This course covers the main concepts and components of data communications particularly modern communication standards, protocol systems and their implementation, and transmission technology. It also examines the principles of complex networks: Open System Interconnection (OSI), Wide Area Networks (WANs), and Local Area Networks (LANs).
Academic Supervisor: Javier Fernandez SalgadoStatistical Methods
This course examines the objectives and pitfalls of statistical studies and diverse statistical methods.
Academic Supervisor: Alex VuginshteynInformation Science
This course of information science is primarily concerned with the efficient analysis, collection, classification, manipulation, storage, retrieval, movement, dissemination, and protection of data in order to convert it into useful knowledge.
Academic Supervisor: Fernando Fernández SánchezDatabase Management
This course analyzes database management systems and explains how data resources are designed and managed to support information systems in organizations. This course develops a framework for database system analysis and design and focuses on data modeling and management. Additional topics include security, data languages, DBMS, Object Orientation, and distributed databases.
Academic Supervisor: Javier Fernandez SalgadoData Structures
This course studies formal logic and algorithms of data structures, representations languages and programs, conditions and restrictions, and data reprogramming for troubleshooting in computation and performance of electronic devices.
Academic Supervisor: Abdo Miled Abou JaoudeData Science
This course of data science uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. It analyzes the process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. Data science is related to data mining, machine learning, and big data.
Academic Supervisor: Alex VuginshteynNumerical Analysis
This course studies the development and application of numerical analysis, methods, and algorithms to problems in economics, applied sciences, and engineering.
Academic Supervisor: Leon Fidele Ruganzu UwimbabaziData Management
This course of data management studies how to assist researchers with the organization, management, and curation of research data to enhance its preservation and access now and into the future.
Academic Supervisor: Patricia Aja SánchezBig Data
This course of big data explores the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract informative value from large or complex data sets. Big data is characterized the large volume of data in many environments, the wide variety of data types stored, and/or the velocity at which the data is generated, collected, and processed.
Academic Supervisor: Alex VuginshteynData Analysis
This course of data analysis examines the analytical and logical reasoning to gain information from the data. The main purpose of data analysis is to find meaning in data so that the derived knowledge can be used to make informed decisions. It involves the interpretation of data to determine patterns, relationships, or trends. Data analysis is the most crucial part of any research.
Academic Supervisor: Alex VuginshteynNumerical Optimization
This course deals with methods of solving optimization problems infinitely many variables, with or without constraints, sequential optimization problems involving random variables, control of uncertain dynamic systems, risk, feedback, and adaptivity.
Academic Supervisor: Leon Fidele Ruganzu UwimbabaziApplied Statistics
This course reviews the applicability and certainty of statistical tools, random variables analysis, and probability, and how they may be used in decision theory, and to estimate risk and uncertainty.
Academic Supervisor: Alex Vuginshteyn
Admission requirements - Data Science
For official admission status at Bircham distance education university; you need to send in a filled out, dated, and signed official Application for Admission. You may download this application form from the website or request it by email or mail. Please send this application and enclosed documents to our address. You may also submit this application and attached documents by email in a PDF Format.
Bircham International University issues an admission certificate after receiving your complete application for admission. This document will show the number of credits transferred and validated from previous education and experience, and the number of credits required to complete the distance learning degree program's major. Bircham University can not perform this evaluation without the complete application for admission.