隨著網(wǎng)絡(luò)的普及，每時每刻都會有大量的數(shù)據(jù)被產(chǎn)生和存儲下來，如何將這些數(shù)據(jù)變成有價值的商業(yè)信息則成為了各個公司競爭的核心之力，從而誕生了數(shù)據(jù)科學(xué)專業(yè)，為了順應(yīng)時代的需求，約翰霍普金斯大學(xué)就開設(shè)了數(shù)據(jù)科學(xué)碩士專業(yè)，下面，就隨小編來看看吧，希望對大家有所幫助：

　　MSE in Data Science

　　數(shù)據(jù)科學(xué)碩士學(xué)位將提供應(yīng)用數(shù)學(xué)、統(tǒng)計學(xué)和計算機科學(xué)的培訓(xùn)，作為理解和欣賞現(xiàn)有數(shù)據(jù)科學(xué)工具的基礎(chǔ)。該項目旨在通過強調(diào)掌握將真實世界的數(shù)據(jù)驅(qū)動問題轉(zhuǎn)化為數(shù)學(xué)問題所需的技能，并通過使用各種科學(xué)工具來解決這些問題，從而培養(yǎng)下一代數(shù)據(jù)科學(xué)的領(lǐng)導(dǎo)者。

　　課程設(shè)置：

　　數(shù)據(jù)科學(xué)導(dǎo)論(必修)

　　EN.553.636 Introduction to Data Science

　　核心區(qū)域

　　統(tǒng)計學(xué)

　　秋季和春季學(xué)期

　　EN.553.630 Introduction to Statistics. 

　　秋季學(xué)期

　　EN.553.632 Bayesian Statistics

　　EN.553.730 Statistical Theory I

　　EN.601.677 Causal Inference

　　EN.553.613 Applied Statistics and Data Analysis

　　春季學(xué)期

　　EN.553.731 Statistical Theory II

　　EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning

　　EN.553.733 Advanced Topic in Bayesian Analysis

　　EN.553.739 Statistical Pattern Recognition Theory & Methods

　　EN.570.654 Geostatistics: Understanding Spatial Data

　　EN.553.639 Time Series Analysis

　　機器學(xué)習(xí)

　　秋季與春季學(xué)期

　　EN.601.675 Machine Learning

　　秋季學(xué)期

　　EN.520.612 Machine Learning for Signal Processing

　　EN.520.637 Foundations of Reinforcement Learning

　　EN.520.647 Information Theory

　　EN.520.651 Random Signal Analysis

　　EN.525.724 Introduction to Pattern Recognition (online)

　　EN.553.740 Machine Learning I

　　EN.580.709 Sparse Representations in Computer Vision and Machine Learning

　　EN.601.634 Randomized and Big Data Algorithms

　　EN.601.677 Causal Inference

　　EN.601.682 Machine Learning: Deep Learning

　　EN.601.780 Unsupervised Learning: Big Data to Low-Dimensional Representations

　　EN.601.674 Machine Learning: Learning Theory

　　春季學(xué)期

　　EN.520.638 Deep Learning

　　EN.520.648 Compressed Sensing and Sparse Recovery

　　EN.520.666 Information Extraction

　　EN.535.741 Optimal Control and Reinforcement Learning (online)

　　EN.553.602 Research and Design in Applied Mathematics: Data Mining

　　EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning

　　EN.553.741 Machine Learning II

　　EN.601.676 Machine Learning: Data to Models

　　EN.625.692 Probabilistic Graphical Models (online)

　　量化

　　秋季學(xué)期

　　EN.553.761 Nonlinear Optimization I

　　EN.553.665 Introduction to Convexity

　　EN.520.618 Modern Convex Optimization

　　春季學(xué)期

　　EN.553.762 Nonlinear Optimization II

　　EN.553.763 Stochastic Search and Optimization

　　EN.601.681 Machine Learning: Optimization

　　EN.553.766 Combinatorial Optimization

　　計算機

　　秋季與春季學(xué)期

　　EN.601.633 Introduction to Algorithms

　　秋季學(xué)期

　　EN.553.688 Computing for Applied Mathematics

　　EN.601.620 Parallel Programming

　　EN.601.647 Computational Genomics: Sequences

　　春季學(xué)期

　　EN.601.646 Sketching and Indexing for Sequences

　　EN.520.617 Computation for Engineers

　　選修課

　　計算醫(yī)學(xué)

　　秋季與春季學(xué)期

　　AS.410.633 Introduction to Bioinformatics (online)

　　AS.410.635 Bioinformatics: Tools for Genome Analysis (online)

　　EN.605.620 Algorithms for Bioinformatics (cannot be taken with EN.605.621)

　　EN.605.621 Foundations of Algorithms (cannot be taken with EN.605.620)

　　秋季學(xué)期

　　AS.410.671 Gene Expression Data Analysis and Visualization (online)

　　EN.605.653 Computational Genomics

　　春季學(xué)期

　　EN.553.650 Computational Molecular Medicine (offered spring)

　　EN.520.659 Machine Learning for Medical Applications

　　計算機視覺

　　秋季與春季學(xué)期

　　EN.601.661 Computer Vision

　　EN.520.614 Image Processing and Analysis

　　秋季學(xué)期

　　EN.520.646 Wavelets & Filter Banks

　　EN.520.665 Machine Perception

　　春季學(xué)期

　　EN.601.783 Vision as Bayesian Inference

　　EN.520.623 Medical Image Analysis

　　EN.553.693 Mathematical Image Analysis

　　EN.520.615 Image Processing and Analysis II

　　EN.525.733 Deep Learning for Computer Vision (online)

　　金融數(shù)學(xué)

　　秋季學(xué)期

　　EN.553.627 Stochastic Processes and Applications to Finance I

　　EN.553.641 Equity Markets and Quantitative Trading

　　EN.553.642 Investment Science

　　EN.553.644 Introduction to Financial Derivatives

　　EN.553.646 Risk Measurement and Management in Financial Markets

　　EN.553.649 Advanced Equity Derivatives

　　春季學(xué)期

　　EN.553.628 Stochastic Processes and Applications to Finance II

　　EN.553.645 Interest Rate and Credit Derivatives

　　EN.553.753 Commodity Markets and Trade Finance

　　數(shù)學(xué)與數(shù)據(jù)科學(xué)

　　秋季學(xué)期

　　EN.553.633 Monte Carlo Methods

　　EN.553.792 Matrix Analysis and Linear Algebra

　　EN.601.634 Randomized and Big Data Algorithms

　　語言與言語

　　秋季學(xué)期

　　EN.601.665 Natural Language Processing

　　春季學(xué)期

　　EN.520.666 Information Extraction

　　EN.520.680 Speech and Auditory Processing by Humans and Machines

　　EN.601.769 Events Semantics in Theory and Practice

　　額外課程

　　EN.520.650 Machine Intelligence

　　EN.580.691 Learning, Estimation and Control

　　EN.601.615 Databases

　　EN.601.663 Algorithms for Sensor-Based Robotics

　　Data Science Capstone Experience

　　EN.553.806 Capstone Experience

　　申請條件：

　　學(xué)生必須完成學(xué)士學(xué)位，最好是工程、數(shù)學(xué)、計算機科學(xué)或其他科學(xué)專業(yè)。此外，候選人應(yīng)至少完成微積分(通過多元微積分)，線性代數(shù)，微分方程，概率，計算機編程(如c++或Python)的本科水平的課程，最好輔之以統(tǒng)計學(xué)課程和至少一門證明寫作課程。

　　語言要求：

　　托福成績不低于100分(網(wǎng)考)或600分(紙考)，雅思成績達到7分

　　約翰斯·霍普金斯大學(xué)

　　約翰斯·霍普金斯大學(xué)(The Johns Hopkins University)，簡稱Hopkins或JHU，成立于1876年，是一所世界頂級的著名私立大學(xué)，美國第一所研究型大學(xué)，也是北美頂尖大學(xué)學(xué)術(shù)聯(lián)盟美國大學(xué)協(xié)會(AAU)的14所創(chuàng)始校之一。美國國家科學(xué)基金會連續(xù)33年將該校列為全美科研經(jīng)費開支最高的大學(xué)。截止目前，學(xué)校的教員與職工共有37人獲得過諾貝爾獎。

　　綜上所述，以上講的就是關(guān)于約翰霍普金斯大學(xué)數(shù)據(jù)科學(xué)碩士專業(yè)的相關(guān)問題介紹，希望能給各位赴美留學(xué)的學(xué)子們指點迷津。近年來，赴美留學(xué)一直是廣大學(xué)生最熱門的話題，同時，很多學(xué)生對于簽證的辦理、院校的選擇、就業(yè)的前景、學(xué)習(xí)的費用等諸多問題困擾不斷，別擔心，IDP留學(xué)專家可以為你排憂解難，同時，更多關(guān)于赴美留學(xué)的相關(guān)資訊在等著你，絕對讓你“瀏覽”忘返。在此，衷心祝愿各位學(xué)子們能夠順利奔赴自己心目中理想的學(xué)校并且學(xué)業(yè)有成!