For students that want to make the most of their four years’ worth of college or expand their education after graduation, summer courses offer that opportunity. A course takes at most two months and can have more flexibility for the student’s progress, especially with distance learning options.
What is a summer course in data science? Data science resides at the intersection between statistics and computer science, dealing with collecting and analyzing large amounts of data. Students can learn about hardware and software employed to generate such data, how to interpolate trends and emergent behavior from large sample sizes, and how to graph and visualize the data to convey and support their inferences. Distinguishing data science from statistics is often an emphasis of the program, along with focusing on technology, especially the use of the Internet as an information channel.
Students that take a summer course in data science can develop skills in statistical analysis and computer science. More broadly applicable abilities they might build on, though, include communication, group work, acting as a liaison, and insight into other people.
Tuition for summer courses is relatively inexpensive compared to traditional college courses taking place in the fall or winter semesters. Exact pricing varies, though, and depends mainly on the individual schools and on the course hours the program requires.
Data science as a field has grown immensely in recent years, so students pursuing an education in data science can expect to enter an expansive job industry. Many companies employ data scientists as market researchers, database managers, or risk management consultants. Data scientists are also involved in work centered on predictive or pattern detection algorithms, which can put them in positions involving cyber security.
A summer course can be a powerful first step into a new branch of one’s education. If you want to learn more, search for your program below and contact directly the admission office of the school of your choice by filling in the lead form.
Faculty of Applied Sciences - Ukrainian Catholic University
Lviv Data Science Summer School is an educational initiative from Applied Sciences Department of the Ukrainian Catholic University. The Summer School participants – under
Lviv Data Science Summer School is an educational initiative from Applied Sciences Department of the Ukrainian Catholic University. The Summer School participants – undergraduates, PhD students, young professionals – studies state-of-the-art methods and tools of Data Science, Machine Learning, Business Analytics. The school is oriented towards the basic level of participants’ knowledge. This year school dates are July 12-25.
The goal of the Summer School is to give the practice-oriented knowledge in the field of Data Science. The school’s program will incorporate three following stages. During the first stage participants will attend introductory courses that would make them familiar with the main theme of the school. During the second stage the students will be involved in several elective practice-oriented courses. The third stage will be substantially dedicated to working on the projects. The project topics will be provided by the supervisors: school’s lecturers, representatives of IT companies, and other partner organizations. The project work during several days means implementation and approbation of the previously obtained knowledge and skills. The project teams will present their results publicly at the end of the school....
The Data Science Summer School offers a broad multidisciplinary perspective on the different pillars of data science, including data mining and big data analytics, machin
The Data Science Summer School offers a broad multidisciplinary perspective on the different pillars of data science, including data mining and big data analytics, machine learning and AI, network science and complex systems, digital ethics, computational social science and applied data science, featuring lectures by high-level international scholars.
Data Science is emerging as a disruptive consequence of the digital revolution. It is based on the combination of big data availability, sophisticated data analysis techniques and scalable computing infrastructures. Data Science is rapidly changing the way we do business, socialize, conduct research, and govern society. It is also changing the way scientific research is performed. Model-driven approaches are supplemented with data-driven approaches. A new paradigm has emerged where theories and models and the bottom up to the discovery of knowledge from data mutually support each other. Given the interdisciplinary nature of Data Science this exploits data and models for advancing knowledge in different disciplines or across diverse disciplines (e.g., biology, economics, medicine, etc). The main topics of the summer school are related to big data analytics(i.e., extraction of knowledge from big data, machine learning; providing an overview of the main techniques used to automatically learn and improve from experience and complex systems; methods and technologies particularly related to network science). Moreover, lectures will highlight the ethical implications that data science could lead and the countermeasures that each data scientist can apply to perform analysis with respect to the individuals involved in the data. ...
Tel-Hai College and MIGAL-Galilee Research Institute are offering a unique workshop in bioinformatics. Participants will gain practical experience in NGS analysis and the
Tel-Hai College and MIGAL - Galilee Research Institute is offering a unique workshop in bioinformatics. Participants will gain practical experience in NGS analysis and theory, statistical and analytical approaches and hands-on practice using a variety of tools.
The 5-day workshop will include the following topics:
Introduction to next-generation sequencing data analysis, Linux command line
Cleaning and trimming raw data, introduction to alignment algorithms and tools
Transcriptome and genome assembly, Expression profiling and statistics
Genome-wide association mapping, variant calling, and processing, phylogenetic analysis, and inferences...
Develop leadership and teamwork
Discover and master international innovative business models and strategies
Gain the tools to solve the challenges facing global business
IAE Pau-Bayonne is the Graduate School of Management, the guarantor of graduate public training offer in Management, within the University of Pau and Pays de l’Adour. Our school offers selective degrees at Master and Ph.D. level, tailored to the needs of companies, and fed from the latest progress in the Management Sciences research area. IAE Graduate School of Management prepares our students to integrate high position jobs in various fields of Management in France or abroad.
-Develop leadership and teamwork
-Discover and master international innovative business models and strategies
-Gain the tools to solve the challenges facing global business’
-Combine in depth learning, international mobility and work experience...
George Washington University, Summer & Non-Degree Programs
This hands-on program is designed for international students who are English language learners to introduce them to concepts in cross-disciplinary research in a U.S. univ
Cross-disciplinary principles of data science and academic writing for research purposes
Program Dates: July 1 - August 10, 2018
This hands-on program is designed for international students who are English language learners to introduce them to concepts in cross-disciplinary research in a U.S. university. Students will engage with professionals to help solve problems in science and technology disciplines, as well as gain skills in research and writing techniques. In this program students will:
Connect with professionals in research-focused institutions.
Develop skills in problem analysis, literature research, teamwork, communication, and presentation.
Develop research-based writing skills including vocabulary, source evaluation, documentation, and cohesiveness.
Work in cross-disciplinary teams, using research methodologies to address issues and solve problems in areas such as statistics, computer science, mathematics, bioinformatics and more.