Can You Become a Data Scientist With a Liberal Arts Degree

Why study Data Science & Data Analytics?

Large information has immense opportunities. Technological advancement also creates new and exciting opportunities for those who apply, analyze, and understand the always-changing applied science. The Bureau of Labor Statistics reports enormous projected job growth in the field of mathematical science—every bit much equally 27.9 per centum from 2016 to 2026. Data Scientists are being employed in nearly every industry today, from social media to pharmacology to transportation and automation.

A high-demand profession

The labor market place demand for calculator science degrees and related disciplines is growing substantially

• The need for information scientists far exceeds the supply.

• Co-ordinate to the U.S. Bureau of Labor Statistics, the number of jobs in data scientific discipline is projected to grow 27.9%  in the menstruation betwixt 2016 and 2026

• Careers requiring machine learning skills pay an boilerplate of $114,000

"Data Scientist is ranked first amongst the '50 Best Jobs in America for 2019.'"

— glassdoor

Lower Sectionalization Courses

Estimator Scientific discipline I (Lab, iii units)
This form provides a foundation in computational literacy, assuasive students from a variety of disciplines to read, write, and translate code. The grade volition inform through assigned readings, lectures, and workshops that programming is not simply technical skill merely an essential form of literacy. It serves as a standalone course for those seeking to understand the basics of programming. The course construction is based on the "creative coding" model in which students work with programming languages to produce interactive graphics offset on the beginning solar day of course. Principles such as conditional statements, Boolean operations, loops, functions, and classes volition be covered in an applied fashion, allowing students to necktie syntax and semantics of code to real-time graphics. Prerequisite: None.

Calculator Scientific discipline Ii (Lab, three units)
This course is a continuation of CORE 101: Calculator Science I. This course introduces bones principles of algorithmic and object-oriented problem solving, programming linguistic communication concepts, including control structures, data types, and classes. It too provides an introduction to Arrays, Inheritance, File I/O, and GUIs. Trouble assay, plan pattern, evolution and implementation, and related topics are covered. Students consummate several programming projects using an advisable computer language. Prerequisite: CORE 101, Informatics I

Data Structures and Algorithms (Lab, three units)
This form provides a written report of algorithms and their related information structures, including linear lists, linked lists, trees, graphs, sorting techniques, and dynamic storage allocation. The algorithms are used to manipulate these structures and their applications. Applications are implemented using an appropriate figurer language.  Prerequisite: Core 102, Computer Science II

Windows-Based Application Development (Lecture, 3 units)
In this grade, students will learn how to create Windows-based applications using Visual Studio and the .NET Framework. This course teaches the fundamental concepts behind these applications, including event-driven programming, and will use both C# and Visual Bones .Net languages. Students will also create frontends to databases, design games, build their controls, and write programs that interact with Microsoft Office software. Prerequisite: Core 102, Computer science Ii

Large Data Learning Analytics (Lecture, 3 units)
This course provides in-depth coverage of various topics in big data, from data generation, storage, management, transfer to analytics, with a focus on the state-of-the-fine art technologies, tools, architectures, and systems that found large-data computing solutions in high-performance networks. Existent-life large information applications and workflows in various domains are introduced every bit use-cases to illustrate the development, deployment, and execution of a broad spectrum of emerging big-information solutions. Prerequisite: Cadre 102, Calculator Science II

Database Design and Programming (Lecture, 3 units)
This course provides in-depth coverage of database concepts, relational and non-relational database systems, database surround, theory, and applications. The design, evolution, and implementation of database systems are included. A practical database project is developed by students utilizing a popular database development system. Students generate user interfaces and reports. Prerequisite: Core 102 Informatics Two.

Analytic Geometry I (Lecture, 5 units)
This course covers limits, derivatives, applications of differentiation, integrals, and the fundamental theorem of calculus. Proofs of primary calculus theorems are reviewed. Pre-requisites: MATH 251, Trigonometry with a course of "C" or ameliorate

Analytic Geometry Ii (Lecture, five units)
Techniques of integration, numerical integration, improper integrals, and applications of the integral. Taylor polynomials, sequences and series, and ability series are besides studied. Pre-requisites: MATH 260, Belittling Geometry I with a grade of "C" or better

Linear Algebra (Lecture, 3 units)
An introduction to matrix algebra, vector spaces, and linear transformations. Topics include systems of linear equations, subspaces, linear independence, bases and dimension, abstract vector spaces, orthogonality, least-squares methods, inner product spaces, determinants, eigenvalues, and diagonalization. Pre-requisites: MATH 260, Belittling Geometry I with a grade of "C" or improve

Discrete Math (Lecture, 3 units)
An introduction to the mathematics needed in informatics. Logic and boolean algebra, discrete logic circuits (apps of and/or/nor), number systems, proofs, set theory, matrix theory, counting methods, discrete probability, sequences, induction, recursion, counting, and graph theory (including trees). Pre-requisites: MATH 149, Intermediate Algebra with a course of "C" or better, or Placement

Upper Partition Courses

Applied Artificial Intelligence (Lecture, 3 units)
This course provides an introduction to the basic principles, techniques, and applications of Artificial Intelligence. Some of the specific topics include knowledge representation, logic, inference, trouble solving, search algorithms, game theory, perception, learning, planning, and agent design. Students will experience programming in AI language tools. Potential areas of further exploration include skilful systems, neural networks, fuzzy logic, robotics, tongue processing, and computer vision. Prerequisites: CORE 201, Data Structures and Algorithms.

Advanced Data Structures and Algorithm Analysis (Lecture, 3 units)
This course is a continuation of Cadre 201. The course explores the avant-garde information structures (including trees and graphs), the algorithms used to dispense these structures, and their awarding to solving practical computer science and data analytics problems. A vital element of the form is the role of avant-garde data structures in algorithm design and the apply of amortized complexity assay to determine how data structures affect performance. Prerequisites: Core 201, Information Structures and Algorithms.

Avant-garde Database Evolution (Lecture, iii units)
This course explores advanced topics in client server and database evolution. Information technology covers the programming and administration of database systems and includes views, stored procedures, triggers, indexes, constraints, security, roles, logs, maintenance, transaction processing, XML, reporting, and other relevant topics. Students will be exposed to several database packages and will perform considerable database programming. Pre-requisites: CSDA 210, Database Design and Programming

Information Mining (Lecture, 3 units)
An introduction to basic concepts backside data mining. Survey of data mining applications, techniques, and models. Word of ideals and privacy issues apropos invasive utilise. Introduction to data mining software suite. CSDA 400, Advanced Database Evolution.

Machine Learning (Lecture, 3 units)
Car learning uses interdisciplinary techniques, such as statistics, linear algebra, optimization, and information science, to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. This course introduces several cardinal concepts and methods for machine learning. The objective is to familiarize the students with some basic learning algorithms and techniques and their applications, as well as full general questions related to analyzing and handling large information sets. Several software libraries and data sets publicly available will be used to illustrate the application of these algorithms. The emphasis will exist thus on motorcar learning algorithms and applications, with some broad explanation of the underlying principles. CORE 201, Data Structures and Algorithms.

Senior Project (Lecture, 3 units)
This course provides an opportunity for students to apply theories, ideas, principles, and skills learned in the classroom to a project of trouble solving in practice. Using the internship, students further develop skills for becoming data analytics professionals. The internship experience is about understanding data analytics and business concern needs and practices within an organizational context, including their civilisation, calculating and management systems, operations, resources, products, services, markets, service areas, and specialty areas. The experience is obtained in organizations approved past the CSDA Department nether the guidance of a Woodbury faculty supervisor and a qualified mentor at the selected organization. Senior Standing; Informatics in Data Analytics Major.

Internship (Lecture, v units)
A piece of work experience is a graduation requirement of all CSDA students. CSDA 490X, Internship is a co-requisite to apply for internship hours. Students will keep and submit internship journals as part of this course. Students will as well fulfill internship requirements, such as obtaining signed evaluations from host company supervisors indicating that they have completed the accompanying internship successfully and demonstrated appropriate professional person conduct. Students may enroll in CSDA 490, Internship for additional credit hours with the permission of the chair.

Probability and Statistics I (Lecture, iii units)
Introductory probability covering the design of experiments, axioms of probability, sample spaces, probability rules, independence, conditional probability, Bayes' Theorem, discrete and continuous random variables, expectation, moment generating functions, and central limit theorem. Also covered are various distributions, including articulation, binomial, Poisson, geometric, normal, exponential, Chi-foursquare, Pupil'due south t, and uniform. Pre-requisites: MATH 261, Analytical Geometry 2 with a grade of "C" or better

Probability and Statistics 2 (Lecture, 3 units)
This is the second form in probability and statistics and covers survey sampling, estimation theory, confidence intervals, hypothesis testing, linear regression, and correlation and assay of variance, and real-earth applications. Prerequisite: MATH 310 with a grade of "C" or better.

Applied Statistical Analysis (Lecture, 3 units)
Review of descriptive statistics, hypothesis testing and estimation, to the lowest degree square method, and Gauss-Markov theorem. SAS and R programming language are taught, including procedures to carry out elementary linear regression and multiple linear regression, not-linear regression, model option and model diagnostics, report generation, and working with big data sets. Prerequisite: MATH 311 with a course of "C" or better.

Electives

Topics in Computer science in Information Analytics (Lecture/Lab: Varies, 3 units)
Special course offerings dependent upon the interest of students and faculty. Prerequisites: Varies

Independent Study (Lecture/Lab: Varies, ane-half dozen units)
Private investigation in an area of special interest selected by the student with the approving of an advisable fellow member of the kinesthesia. Regular or periodic meetings with the assigned faculty member are required. Xxx hours required for each unit of credit. Prerequisites: Permission of the department chair.

Combinatorics (Lecture, 3 units)
A one-semester introduction to combinatorics. Topics include enumeration, generating functions, recurrence relations, construction of bijections, introduction to graph theory, Polya'south Theorem, network algorithms, and extremal combinatorics. Prerequisites: Mathematical Statistics 2 and Linear Algebra both with a grade of "C" or better.

Statistics (Lecture, 3 units)
This course will introduce students to statistical methods and practices which are most relevant to the analysis of fiscal and economical data. Topics include autoregressive models, moving boilerplate models, and their generalizations. The class develops models that are closely focused on particular features of financial series such as the challenges of fourth dimension dependent volatility. Prerequisites: Mathematical Statistics ii and Linear Algebra both with a grade of "C" or better.

Spatial and Geo Statistics (Lecture, 3 units)
Topics cover practical spatial and geostatistical analysis, including spatial and temporal autocorrelation, bespeak patterns, interpolation, and multivariate analysis. Prerequisites: Mathematical Statistics 2 and Linear Algebra both with a grade of "C" or better.

Topics in Mathematical Statistics (Lecture, 3 units)
Topics selected from statistics and/or probability, such as nonparametric statistics, multivariate statistics, experimental design, decision theory and advanced probability theory. Mathematical Statistics ii and Linear Algebra both with a course of "C" or better.

Topics in Probability and Statistics (Lecture, 3 units)
This course volition cover topics in probability and statistics not covered elsewhere in the program. Office A is usually devoted to multivariate statistics, Office B to stochastic processes, and Function C to probability theory. Part D is left to a topic chosen by the individual instructor. Prerequisites: Mathematical Statistics ii and Linear Algebra both with a grade of "C" or better.

Career Opportunities

Many career opportunities exist for Data Science & Analytics degree holders. Depending on your chosen concentration, or area of expertise, career possibilities for Woodbury graduates include:

• Data Annotator
• Large Information Engineer
• Data Architect
• Business Intelligence
• Analyst
• Data Scientist
• Drug Discovery Scientist
• Genomics/Epidemiologist
• Data Warehouse Analyst
• Social Science and Public Policy Analyst
• Ethical Hacker
• Meteorologist

Acquire more than about our Bachelor of Scientific discipline in Information science Information Analytics!

Are you ready to pursue your Bachelor's in Information science Data Analytics degree? Are you wondering if Woodbury is right for yous? Visit us—information technology's the best style to get a experience for what the Woodbury community is all virtually. You can also request more than information about the Estimator Science Data Analytics program.

"It'southward a great time to exist a data scientist entering the job market place. That'due south co-ordinate to recent data from job sites Indeed and Dice."

— TechTarget

A Message from the CSDA Chair

Dr. Samuel Sambasivam

Professor and Chair, Informatics Data Analytics

Our Computer Science Information Analytics (CSDA) is helping to create leaders with a deep agreement of the mechanics of working with data and the capacity to place and communicate data-driven insights that ultimately influence decisions. Our CSDA program prepares students with a professionally focused, on-tendency educational experience—led by good kinesthesia. Through experiential learning opportunities, collaboration betwixt a diverse grouping of boyfriend students, our CSDA plan produces the next generation of knowledgeable, experienced analytics leaders.

Our CSDA students:

•  Learn Python, Social Media Mining, SQL, Tableau, Advanced Statistics, Machine Learning, Git/GitHub, and more.
•  Complete projects using real information sets from the worlds of finance, healthcare, government, social welfare, and more—assuasive you lot to build a strong portfolio with a professional sit-in of mastery.
• Develop physical, in-need skills to graduate from the program gear up to apply your knowledge in the professional earth.

Gear up to Learn More? Feel free to accomplish out to me to talk nigh the program!
Email: Samuel.Sambasivam@woodbury.edu

Get the latest CSDA

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Source: https://woodbury.edu/program/college-of-liberal-arts/programs/computer-science-in-data-analytics/