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24 courses found which satisfy the condition "Data Science".

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**Algorithm Design and Analysis**

"Algorithm Design and Analysis" is an important course for the major of computer science. This course introduces the basic models of algorithm design, the basic methods of algorithm analysis and the semi formal description of the problem as well as the proof of the algorithm. It will help the junior students of computer science establish a complete and systematic method for analyzing and solving the problem.

**Java Program Design**

None

**Cloud Computing and Big Data Analysis**

None

**Discrete Mathematics**

None

**Probability Theory**

This Course aims at guiding students to describing and modelling non-determinative iphenomenons mathematically, and provides chances for students to practice Set Theory,Calculus and Advanced Algebra.

**Mathematical Statistics**

Mathematical Statitics is a basic course with wide application, it mainly focuses on the analysis of randon sample and other data set, including how to effectively collect data, parameter estimation , hypothesis testing, linear model and statistical design. The purpose is to let the students to understand elementary ststistica concepts and ideas, to study the most commonly used statistical methods and to solve some practical problems, and to establish the way of statistical thinking.

**the cplusplus programming language**

C++ is a low-level language with modern programming ideas. Maybe you have to learn other programming languages, but C + + allows you to better understand the computing machine. Creating more complex programs would need that you would have to have direct access over how memory is used. Pointers, for instance is something that you cannot utilize in other high level languages. If you are really interested about serious programming, C++ should be your main priority.

**Principles of Database**

None

**Data Structure and Algorithm**

Understand principle and theory of Data Structures and Algorithms, able to design and implement fundamental data structures and algorithms.
Covers programming, data structures, algorithms.
Topics include the organization and implementation of fundamental data structures such as list, binary tree, tree and forest, and graph; sorting and searching; data abstraction and problem solving.

**Linear Algebra**

The content of the course consists of polynomials, linear spaces and linear transformations. This course will train the students with mathematical thinkings and the preliminary ability for solving practical problems.

**Statistical Computing**

After taking this course, students can learn the basic knowledge about computational statistics and learn how to generate random numbers. They know how to test if the random variables come from some distribution or not. They know how to do statistical simulations using generated random numbers. EM and MCMC algorithms are also introduced in this class.

**Digital Image Processing**

The technology of Digital Image Processing (DIP) was widely used in physics, biomedicine, Geomatics and remote sensing. The content of this course includes the elements of DIP, image transformation, image enhancement, image restoration, image reconstruction and image compression etc. The experiments of DIP including statistical parameters calculation, histogram equalization, median filter, Sobel sharpening and DCT transform is to improve the programming ability of students.

**Introduction to Computer Systems**

The objective is to provide a strong foundation that a serious student can build on in later courses across the spectrum of computer science and engineering. The idea is that a more complete understanding of the fundamentals will help a student acquire a deeper understanding of more advanced topics, whether that topic is in computer architecture, operating systems, data base, networks, algorithm design, software engineering, or whatever. The approach is "motivated" bottom-up. That is, after providing some overview of why a new concept is important, we attempt to tie that new concept to what the student already understands. Starting with the transistor as a switch, we build logic gates, then more complex logic structures, then gated latches, culminating in an implementation of memory. From there, we study the computer’s instruction cycle, and then a particular computer, the LC-3 (for Little Computer 3). The first programming assignment is in the machine language of the LC-3. From there, we move up to Assembly Language, and learn how an assembler works. The remaining programming assignments are in LC-3 Assembly Language. An LC-3 Simulator allows the student to debug his/her own programs. Input (via the keyboard) and output (via the monitor) both use the physical device registers. System service routines, written in LC-3 Assembly Language, are used to perform I/O functions. They are invoked by user programs by the TRAP instruction and corresponding trap vector. Subroutine calls and returns complete the LC-3 instruction set.

**Machine Learning**

None

**Multivariate Statistical Analysis**

None

**Statistical Software**

The course teaches student using the SAS system with an easy starting attitude, it includes SAS programming, data management, reporting and graphics, basic statistical analysis techniques. The course will also introduce R, another statistical software. R is especially suitable for programming statistical algorithms, and it is one of the most prefered developement and computing tools used by statisticians.

**Data Mining**

None

**Linear Model**

None

**Analytical Techniques of Data**

The course mainly forcus on basic principles of data analysis and use data analyisis techinic as a tool to do research on sociological issues. Main topics including basic knowledge of SPSS, data input, data management,data clean,and how to use "Frequencies,Descriptives,Explore, Crosstabs, Means, T-Test, ANOVA, Correlate,Regression,and Plot " to do sociological research.

**Computational Method**

None