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Data handling statistics coursework


It is a 'hands-on' course in which the emphasis is on the development of skills in the selection and application of upper-level statistical methodology. Emphasis is also placed on the presentation of statistical analyses in a written format that promotes reproducible research. Topics covered in the course include: hypothesis testing, experimental design, inference, analysis presentation, generalised linear modelling; mixed-effects modelling, multinomial regression, and model selection.

Expertise with the statistical computing language R and RStudio will be extended, including the application of R Markdown for promoting reproducible research. Examples will be drawn from the biological, physical and social sciences.

Units are offered in attending mode unless otherwise indicated that is attendance is required at the campus identified. A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.

TNE Program units special approval requirements. About Census Dates. Students are introduced to the use of computers and spreadsheets in statistical applications. Units are offered in attending mode unless otherwise indicated that is attendance is required at the campus identified. A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.

TNE Program units special approval requirements. About Census Dates. Please note: international students should refer to this page to get an indicative course cost. Blended delivery: 1hr video lectures, 1 hour tutorial, 1 hour computer lab, 1 hour face-to--face weekly. The University reserves the right to amend or remove courses and unit availabilities, as appropriate.

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The degree requires four core courses and four chosen courses in topics such as biostatistics, web application development, machine learning, computer security, and computer simulation in physics. Applicants must have an undergraduate degree in business, science, or engineering, giving them at least basic knowledge in statistical and mathematics techniques, computer programming, information systems and databases, and communications.

During the final semester, students participate in a paid practicum where they implement the concepts they have learned to obtain industry experience working as part of a data science team. Applicants must have completed an undergraduate degree, but no specific field is required. The Center for Data Science was established with the intersection of computer science, statistics and mathematics in mind.

The program is divided into six core courses which focus on mathematical and programming backgrounds. Students have the option to pick from six electives based on their area of interest. Students will gain a better understanding of why people make decisions and will also be able to predict future outcomes. Students are also required to take 3 electives.

Data science and analytics is one of 12 concentrations in MS in Information Technology. This program balances the study of management strategies and technology leadership with advanced coursework in an IT concentration. Students are required to complete a suite of core and capstone courses. Three to five additional courses must be selected to complete a concentration. A professional and research track are offered for the M.

The MBS requires 6 courses in business, such as market assessment and principles of accounting, as well as 5 courses in data science, such as regression analysis, cloud computing and big data, and database design and management.

Saint Louis University — St. Students cover 3 main topics, specifically analytics, computing, and health sciences. Each of these blocks is comprised of 3 courses, such as predictive modeling, machine learning, programming, health data management, high performance computing in healthcare, medical diagnosis and treatment, and communication and leadership in the health care industry.

Students receive advanced training in data manipulation, data visualization, data mining, machine learning, predictive analytics, and programming in R, SQL and Python. This program focuses heavily on business analytics, at which time students learn to integrate scientific methods from statistics, computer science and data based management to help make business decisions.

Classes are housed in the Data Science Institute, a state of the art data science laboratory. The curriculum of this program leads students to pathways of internships and employment opportunities. Additionally, students will have a specialization in either marketing analytics or healthcare analytics. Stanford University — Stanford, California M.

The focus of this program is to assist students in strengthening their data science fundamentals, as well as their mathematical, statistical and computational skills. A unique component of this program is that students are offered various electives and may choose based on their field of interest. The program works well for those with undergraduate experience looking to bolster their career prospects, as well as individuals seeking career advancement in their respective industries.

The blend of hybrid and traditional courses benefits the working professional and the full time student. The program typically takes most students two calendar years to complete, although full time students may be able to complete it in one calendar year.

Students may also gain skills for government positions requiring strong skills in data analysis. Research credits are available. Both thesis and non-thesis options are available. The curriculum requires 30 graduate credits in an approved plan of study. This one year program is innovative, professionally relevant, and valuable.

Students will learn operation research, predictive modeling, data mining, forecasting big data programming, management and data visualization. The focus will be on application and interpretation of modern data analysis techniques. Students are expected to complete 36 credits, including a course in computational methods. Students choose one of three practicum courses and two elective courses. According to SUNY, the practicum course serves as the capstone experience.

This experience includes comprehensive analysis of data sets with oral presentations or poster presentations of results. During this one year program students learn how to use advanced technologies to manipulate data, utilize statistical methods to interpret data and obtain necessary business skills. Graduates of this program have found careers in data science, business analytics, business intelligence and big data fields. The program focuses on statistics and machine learning, with courses in data infrastructure and systems, data analysis and interfaces, and theoretical elements.

The thesis-option consists of 24 credit hours of computer science course work plus six credit hours of thesis research, and the non-thesis option consists of 30 credit hours of computer science coursework.

The entire program takes approximately one and a half to two years to complete. Students have the option to take a full course load in summer, allowing them to complete the program in three semesters. Coursework includes studies in machine learning including deep learning , data mining, modeling and quantitative analysis of massive datasets, application and technology in strategic decisions, collecting and managing massive datasets, and implementing practical solutions to current big data problems using algorithmic techniques and software development tools.

The program includes a set of core required courses and provides an opportunity for students to select from a wide range of electives related to data analytics, biostatistics, bioinformatics, business intelligence, and cyber security. The program requires at least 36 credits of coursework.

Lastly, the capstone requirement involves comprehensive analysis of data sets along with oral presentations or poster presentations of such results. Training will start with sound basic theory and there is an emphasis on practical aspects of data, computing and analysis.

This knowledge will allow individuals to formulate mathematical models of data to identify trends and present them in effective ways. By selecting this program, those who are currently in the midst of their career as a data scientist can expand their knowledge-base and training skills. As a student of this program, students take classes at the San Diego Supercomputer Center.

University of Delaware — Newark, Delaware M. The program aims to provide a solid background in the methods of data science for working with large and dynamic data sets. Students can choose to complete the program full- or part-time on campus.

Those individuals without a foundation in programming can choose bridge courses in computer and data science fundamentals. Students also choose a track to complete their final 3 courses in: spatial analytics, data science analytics, project management, or management science. The certificate requires 4 of those same introductory courses. The concentration of this programs is broken down into four data science courses: theory of data science; systems of data science; data analysis; and machine learning.

Along with the department collaboration, the students have access to the Center for Scientific Computing and Visualization Research , which allows students to work with faculty using high-performance computing. The goal of the program is to prepare students for employment in professional fields that require data analysis and a comprehensive understanding of informatics.

Upon graduating from the program, graduates will have skills in computer programming, statistics, data mining, machine learning, data analysis and visualization. Four concentrations are available in computer intelligence, applications, business analytics, and big data informatics. This two-year program offers three emphasis areas: statistics, algorithms, and infrastructure and large-scale computing along with several different elective options.

There is a final capstone project upon completion of coursework. Through the highest possible standards of education and research, the department provides consummate biostatistical knowledge and skills to scholars for translating data into evidence and worthy conclusions. Students focus on 3 emphasis areas: biostatistics, bioinformatics and genomics, and data science. The program requires 42 credit hours and can be completed in as little as 2 years. This concentration has eight major components: data visualization, scientific methods, statistical modeling, statistical computing, real-world data applications, data consulting, data research, and data technology.

Graduate students must complete a project in partnership with the local business community to gain real-world experience. Students dive into new technologies for handling vast amounts of complex and changing data points. This program is designed to allow students to pursue research and develop business skills. Hands-on experience is designed to help students apply learned knowledge to solve business problems.

Students are expected to complete coursework in 16 months, with 4 courses per semester. Applicants must have academic experience with statistics, linear algebra, and programming. The program takes an average of 2 years to complete. Students must complete 30 credits with thesis and non-thesis options. Students must also complete 9 credits of core courses, 3 elective courses, and a 3-credit capstone project if pursuing the non-thesis track. Students in the thesis track must complete 9 credits of core courses, 2 electives, 9 credits of analytics courses, and a 6-credit thesis course.

Both tracks conclude in a presentation, either of the capstone project or the thesis defense. Students then choose 3 electives and complete a final practicum that draws on prior coursework. Students can elect to finish the degree full-time in 14 months, or part-time, and can take classes online, in person, or a combination of the two. This is a 4 semester, full time, STEM program. All lectures are conducted live and on-campus on some Saturdays, and online using video conferencing in the evenings.

This enables full-time students and working professionals alike to learn the skills needed to build their data science careers. You will learn how to design and develop advanced solutions using R and Python, including machine learning, neural networks, predictive modeling, customer analytics, dynamic visualization, and much more.

You will also learn the critical consultative skills that employers are looking for, including presentations skills and interviewing techniques. You will also complete a final capstone project with a major corporation. When students embark on the Technical and Depth Area Electives, they must choose their courses from three different buckets, one of which can be a 2-semester sequence of thesis or practicum. Two courses must represent a depth of sequence, which could be the thesis or practicum or two courses.

These buckets include Applications courses and Methods courses. This program is designed for students with a background in mathematics through calculus and some programming experience. Students will study computation and statistical methods and will be given the opportunity to meet corporate recruiters. There is a 4-credit practicum at the end of the program where students engage with industry partners who provide real-world data sets to implement an end-to-end data analytics solution.

This is a full-time masters can be completed in one year at the downtown San Francisco campus. Students take 7-week courses in topics such as data acquisition, machine learning, web analytics, and interview skills. They will primarily use programming languages R and Python and will learn distributed computing technology such as MapReduce, Hadoop, and Spark.

Required courses include analysis of algorithms, database systems, and foundations of artificial intelligence. Students then must choose at least one course each focusing on some aspect of data systems and data analysis, as well as additional electives. University of St. Thomas — St. Paul, Minnesota M. Thomas M. Degree in Data Science prepares students to pursue careers in the emerging and high-growth fields of data science and big data. It combines in-depth understanding with hands-on skills, technologies, techniques, and analysis tools for data science.

Graduates of this program will have the theoretical, practical, and comprehensive knowledge to manage and analyze large-scale, complex data to enable efficient data-driven discoveries and decisions. The Data Science program is intended to prepare individuals for work in industry and government or further graduate study. The program prepares you for your first position in the information and library professions and also prepares you to be agile and flexible throughout your career, as you and the information landscape grow and change.

The Data Science concentration within the School of Information Science is one of the many ways this program prepares students to be adaptable to a variety of professions after graduation. At minimum, students must complete 12 credits of Common Core courses, 9 credits of Electives courses, and 9 credits of Path Specific courses. The Path Specific courses consist of 3 options: coursework only, coursework and project, or coursework and thesis.

All students must complete and pass all coursework and a comprehensive exam to graduate from the Complex Systems and Data Science M. The program features collaboration, project-based learning, strong mentorship relationships, and partnerships with public and private sector companies.

Students begin in the summer semester with courses in programming and statistical computing for data science, moving to courses in data mining, linear models, ethics of big data, machine learning, and electives such as clinical trials methodology, risk analysis, and theory of computation.

All coursework, including a capstone project, must be completed on campus. This interdisciplinary curriculum has been developed by faculty from 6 different departments at UW and with input from leading companies looking to hire data science professionals. Students are taught how to build a deep expertise in managing, modeling and visualizing big data. Small teams of students will collaborate on data analysis projects to solve real-world data analysis challenges. There is also the option of attending the program full or part-time.

The program aims to educate data science leaders so they may derive insights from real-world datasets through the use of the latest analytical methods and models and present their findings understandably and effectively. Applicants must have prior coursework or equivalent experience in statistics, computer programming, and database administration.

Computation focuses on programming, data structures, computer systems, and methods. Data analysis focuses on data exploration, analysis, prediction, inference, and algorithms. The curricular practicum aims to convey workplace skills, ethical standards, and awareness of data science to date.

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Topics include: collecting, processing and presenting quantitative information; descriptive statistics for summarising data; data exploration techniques;. Data Handling and Statistics 3 is the third applied statistics units offered by the School of Natural Sciences (Mathematics). It provides an extension of. Collection of Data Data Handling GCSE coursework Hypothesis. Wf - Statistical Coursework There are more vowels used in a page written out in.