Johns Hopkins is well-renowned for providing world-class data science education and training

Skilled data science professionals are responsible for turning massive and complex datasets into actionable insights. More and more organizations are opening up their doors to hire data science experts to gain an edge over their competitors. It is quite evident that data science has been a lucrative career opportunity for aspiring tech professionals. The rising importance of this domain has pushed educational institutes and universities across the world to introduce data science courses in their curriculums. Candidates, who aspire to build a career in data science can enroll themselves in data science courses offered by some of the top universities in the world. Johns Hopkins University on the other hand is one of the top universities in the world that offers data science education. The university focuses on advancing the students' careers in the data science ecosystem by providing knowledge on some of the most avant-garde tools used in the domain, including solving complexities in the data pipeline and storage. Here, we have listed some of the top data science courses that Johns Hopkins University offers.

Data Analytics and Policy, Certificate

The Certificate in Data Analytics and Policy provides students with the knowledge and skillset needed to perform a sophisticated data analysis, draw substantive conclusions, and communicate results to improve policy-making and governance. Through the use of cutting-edge tools and skills, students will be able to address contemporary policy challenges in both the public and private sectors.

M.Sc. in Data Analytics and Policy

This master's degree prepares students to use statistics and analytics to tackle policy challenges in the public and private sectors. The schedule for completing this 12-course degree program is flexible. Since several students work full-time while attending the program or on a part-time basis, they can complete their degree in two years. Full-time students can complete the degree in one year. The MS in Data Analytics and Policy is offered primarily online.

Master's in data science Program Online

Johns Hopkins Engineering department offers this online, part-time data science graduate program that aims to address the huge demand for data scientists who will be qualified to serve as knowledgeable resources in the constantly developing data-driven world. This online master's degree in data science will prepare students to succeed in specialized jobs which involve everything, starting from streamlining data pipeline and storage to statistical analytics. The course will enable the students to gain practical skills and advance their careers to meet the growing demands of data scientists.

Capstone Project in Data Science

This course permits graduate students in data science to work with a faculty mentor to explore a topic in depth or conduct research in selected areas of data science. The students applying for this program are required to complete the submission of a significant paper or project related to this field. They also need the formal permission of a faculty mentor, the student's academic advisor, and also from the program chair.

Data Engineering Principles and Practice

This course will cover the core concepts and skills regarding data engineering with a focus on practical use cases. The program will also cover some of the core supporting technologies in the data engineering world: data pipelines, containerization, schedulers, cloud technologies, and modern data engineering tools/stacks. The course is geared towards giving students direct experience in building solutions to problems associated with data engineering.

Big Data Processing Using Hadoop

This course breaks down the walls of the complexity of distributed processing of big data by providing a practical approach to developing applications on top of the Hadoop platform. By completing this course, students will gain an in-depth understanding of how MapReduce and distributed file systems work. In addition, they will be able to author Hadoop-based MapReduce applications in Java and also leverage Hadoop subprojects to build powerful data processing applications.

Algorithms for Data Science

This course provides a survey of computer algorithms, examines fundamental techniques in algorithm design and analysis, and develops problem-solving skills required in all programs of study involving data science. Topics include advanced data structures for data science (tree structures, disjoint set data structures), algorithm analysis, and computational complexity, to name a few.

Principles of Database Systems

This course examines the underlying concepts and theories of database management systems. Topics include database system architectures, transaction management, data models, query languages, conceptual and logical database design, and physical organization. It also covers the causes of performance problems and how to improve database application performance during database design and implementation.

Social Media Analytics

The modern social media landscape is quite different from its traditional form. With a myriad of applications that are currently being developed to enable individuals to connect, businesses are wondering how to utilize the data generated from these vast platforms. This course will train candidates to address this issue through a multidisciplinary approach that combines social network analysis (SNA), natural language processing, and data analytics. This program combines all these topics to address contemporary topics such as marketing, population influence, and others.

Statistical Methods and Data Analysis

This course introduces statistical methods that are widely used in modern applications. A balance is struck between the presentation of the mathematical foundations of concepts in probability and statistics and their appropriate use in a variety of practical contexts. The students will be trained through a virtual mode on multiple data analysis procedures, starting from descriptive statistics to the implementation of common procedures for estimation, hypothesis testing, and others.