This document certifies that Omayma Said has successfully completed the Data Science Specialization from Johns Hopkins University on Coursera. The specialization covers the concepts and tools for the entire data science pipeline, including using data science tools, analyzing complex problems, managing large datasets, deploying statistics, creating visualizations, building machine learning models, publishing analyses, and developing data products. The specialization does not provide academic credit but verifies completion on Coursera.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consisted of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The certificate verifies that Asmi Ariv successfully completed the online, non-credit specialization in data science.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consists of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The certificate verifies that Mei Chiao Lin successfully completed the online, non-credit specialization in data science.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consists of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The certificate verifies that Gintautas Buzorius successfully completed the online, non-credit specialization in data science.
This document certifies that LUCA VIGNALI has successfully completed the Data Science Specialization from Johns Hopkins University on Coursera. The specialization covers the concepts and tools for the entire data science pipeline, including using data science tools, analyzing complex problems, managing large datasets, deploying statistics, creating visualizations, building machine learning models, publishing analyses, and developing data products. The certificate does not confer academic credit but verifies completion of the online, non-credit specialization.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consisted of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. Santanu Dutta successfully completed the online, non-credit specialization in data science.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization includes 10 courses covering topics like R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone. It provides an overview of the full data science pipeline and teaches skills for working with large datasets, statistical analysis, machine learning, and communicating results. This certificate does not confer academic credit but verifies completion of the online, non-credit specialization.
This document is a certificate from Johns Hopkins University recognizing an individual's completion of their Data Science Specialization. The specialization consisted of 10 courses covering the data science pipeline from R programming to machine learning to developing data products, as well as a capstone project. It provided participants with the concepts and tools needed to work with large data sets and create reproducible and visual analyses using statistical and machine learning techniques.
This document certifies that Omayma Said has successfully completed the Data Science Specialization from Johns Hopkins University on Coursera. The specialization covers the concepts and tools for the entire data science pipeline, including using data science tools, analyzing complex problems, managing large datasets, deploying statistics, creating visualizations, building machine learning models, publishing analyses, and developing data products. The specialization does not provide academic credit but verifies completion on Coursera.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consisted of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The certificate verifies that Asmi Ariv successfully completed the online, non-credit specialization in data science.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consists of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The certificate verifies that Mei Chiao Lin successfully completed the online, non-credit specialization in data science.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consists of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The certificate verifies that Gintautas Buzorius successfully completed the online, non-credit specialization in data science.
This document certifies that LUCA VIGNALI has successfully completed the Data Science Specialization from Johns Hopkins University on Coursera. The specialization covers the concepts and tools for the entire data science pipeline, including using data science tools, analyzing complex problems, managing large datasets, deploying statistics, creating visualizations, building machine learning models, publishing analyses, and developing data products. The certificate does not confer academic credit but verifies completion of the online, non-credit specialization.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consisted of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. Santanu Dutta successfully completed the online, non-credit specialization in data science.
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization includes 10 courses covering topics like R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone. It provides an overview of the full data science pipeline and teaches skills for working with large datasets, statistical analysis, machine learning, and communicating results. This certificate does not confer academic credit but verifies completion of the online, non-credit specialization.
This document is a certificate from Johns Hopkins University recognizing an individual's completion of their Data Science Specialization. The specialization consisted of 10 courses covering the data science pipeline from R programming to machine learning to developing data products, as well as a capstone project. It provided participants with the concepts and tools needed to work with large data sets and create reproducible and visual analyses using statistical and machine learning techniques.
This document is a certificate from Johns Hopkins University recognizing the completion of their online Data Science Specialization. The specialization consisted of 10 courses covering the data science pipeline from R programming to machine learning to developing data products, as well as a capstone project. It provides students with the concepts and tools needed to think analytically about problems, manage large data, perform statistical analysis, create visualizations, build machine learning models, publish analyses, and develop data products.
This document is a certificate from Johns Hopkins University recognizing the completion of their online Data Science Specialization by Sudhir Kudikala. The specialization consisted of 10 courses covering the data science pipeline from R programming to machine learning to developing data products, as well as a capstone project. It provides students with the concepts and tools needed to think analytically about problems, manage large data, perform statistical analysis, create visualizations, and build machine learning models.
The document is a certificate confirming that Fernando Sebastian Gonzalez Prada successfully completed the Data Science Specialization through Coursera. The specialization consisted of 10 courses covering the data science pipeline and tools, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The specialization trained participants to use data science tools, analyze complex problems, manage large datasets, apply statistical principles, create visualizations, build machine learning models, publish analyses, and develop data products.
The document is a certificate confirming that Rodrigo Euclides Carneiro successfully completed the Data Science Specialization through Coursera. The specialization consisted of 10 courses covering the data science pipeline and tools, including R programming, data cleaning, analysis, machine learning, and a capstone project. It was offered through the Johns Hopkins Bloomberg School of Public Health but does not confer an academic degree.
This document is a certificate of completion for a Data Science Specialization from Johns Hopkins University completed by Patrick Casimir. The specialization consisted of 10 courses covering topics like R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The specialization provided participants with the concepts and tools to work through an entire data science pipeline from data management to machine learning to publishing results.
The document is a certificate from Johns Hopkins University recognizing that Russell Robbins successfully completed the Data Science Specialization through Coursera. The specialization consisted of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The specialization provided participants with skills in using data science tools, analyzing complex problems, managing large datasets, applying statistical principles, creating visualizations, building and evaluating machine learning models, and publishing reproducible analyses.
The document is a certificate from Johns Hopkins University congratulating Frank Hasbani for successfully completing the Data Science Specialization online program. The specialization consisted of 10 courses covering the concepts and tools for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The specialization trained participants in using data science tools, analyzing complex problems, managing large datasets, applying statistical principles, creating visualizations, building and evaluating machine learning models, and publishing reproducible analyses.
Poornima N. Swamy is seeking a challenging position in biotechnology to utilize her 2.5 years of experience as a network support engineer and her M.Sc. in Biotechnology. She has skills in techniques like SDS-PAGE, DNA isolation, ligation, and transformation. She is pursuing a PG diploma in clinical research and has expertise in areas like clinical trials, clinical data management, pharmacovigilance, and medical writing. She has experience working as a trainee network support engineer and science faculty.
The document summarizes the development of a new system for managing limited submission research funding opportunities at the University of Washington. It describes limited submissions as research funding restricted by sponsors in terms of the number of proposals an institution can submit. It outlines two phases of development: phase 1 involved maintaining underlying data in a new mechanism, while phase 2 added a graphical user interface and data enhancements. Testing showed the new system met performance and user expectations, resulting in a successful replacement of the old administrative interface and campus site.
Jacob Bradley is a computer science student at John Carroll University with relevant coursework and experience in software development, design patterns, bioinformatics, and big data. He has worked on multiple internships at Cleveland Clinic developing software solutions utilizing technologies like C#, JavaScript, Java, MVC, and Solr. Bradley mentors junior interns, reviews code for maintainability and design standards, and has received awards for attending conferences and hackathons related to healthcare IT.
Electronic platforms like Survey Monkey, Qualtrics, and Question Pro can be useful tools for assessment, baseline studies, and simple information gathering, but have limitations for complex longitudinal studies, monitoring, and evaluation. While offering friendly interfaces for survey design and data collection, they provide only basic statistical analysis capabilities and require exporting data to other tools like SPSS or Excel for more robust analysis. These platforms reduce costs compared to printed surveys and make accessing internet-connected samples easier, but are not full monitoring and evaluation systems and may not be suitable for following indicators over time in complex, high-impact studies. Researchers should carefully consider the platform's functions and licensing options to determine what level of data collection, analysis and reporting it can adequately support for
Jessica Topolewski seeks a career in biotechnology sales and has extensive experience in vaccine development and laboratory techniques. She has a Bachelor's degree in Biology from the University of Georgia and has worked as a Research Technician at UGA's Tripp Lab focusing on high-throughput screening and maintaining cell cultures. Her qualifications include knowledge of molecular biology, immunology, and laboratory equipment as well as strong communication, time management, and presentation skills.
Eric B. Hollingsworth has over 15 years of experience working with large datasets and quantitative analysis. He currently works as a Senior Quantitative Analyst at Google, where he leads analyses of user behavior data across Google products and mentors new team members. Previously, he developed simulation and optimization models while working for Cornell University and Columbia University to improve public health responses. He is currently pursuing an M.S. in Management Science and Engineering from Stanford University.
Tajinder Singh is a recent college graduate seeking a career where he can apply his skills in IT, science, and the laboratory. He has a background in chemistry, biology, microbiology, and environmental science. His experience includes conducting water quality testing and using various laboratory instruments and techniques. He also has several years of experience in helpdesk, customer service, and technical support roles.
This document is a resume for Wenjun Sun, who is pursuing a PhD in Industrial Engineering at the University of Wisconsin-Madison with a focus on human factors and ergonomics. It outlines her education, including an expected PhD from UW-Madison in August 2015, as well as relevant experience in areas such as user research, usability testing, and interactive design projects. Her objective is to obtain a position related to human-computer interaction, user experience, and other areas involving human factors engineering.
This document is a resume for Steven Jervier. It summarizes his education, including an expected M.S. in Computer Information Systems from Boston University in 2015 and a B.S. in Information Technology from Northeastern University in 2011. It also lists his computer skills and knowledge in programming languages, web technologies, databases, operating systems, and electronic health records. Finally, it provides details on his relevant coursework and projects in areas like Java, databases, and health informatics, as well as his work experience in automotive sales and customer service.
Going Virtual: Evolving Real-World Evidence (RWE) Study DesignCovance
Site-based observational research has many challenges. Considerable time and cost is invested in identifying suitable sites and investigators, and then managing them to ensure recruitment and research are conducted in line with the protocol and local review board requirements. Furthermore, there is considerable burden to subjects - they may have to travel a distance to research sites to attend lengthy appointments, resulting in out-of-pocket expenses, lost time and inconvenience.
HI ,
Are you a Ph.D. Student?
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This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consists of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The certificate verifies that Mei Chiao Lin successfully completed the online, non-credit specialization in data science.
The document is a certificate confirming that SPYRIDON PARASKEVAS successfully completed the online Data Science Specialization through Coursera. The specialization consisted of 10 courses covering the data science pipeline and tools, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. It was offered through the Departments of Biostatistics at Johns Hopkins Bloomberg School of Public Health.
Poornima N. Swamy is seeking a challenging position in biotechnology to utilize her 2.5 years of experience as a network support engineer and her M.Sc. in Biotechnology. She has skills in techniques like SDS-PAGE, DNA isolation, ligation, and transformation. She has also pursued additional diplomas in clinical research and has technical competencies in areas like clinical trials, clinical data management, pharmacovigilance, and medical writing. She has experience working as a trainee network support engineer and science faculty.
This document provides information about the HST.921 course on Information Technology in the Healthcare System of the Future offered at Harvard and MIT in spring 2009. The course aims to empower students to critically analyze current or future healthcare problems and develop novel IT solutions. It includes weekly lectures, tutorials/labs, and a group project. Students work in multidisciplinary teams on design, business, marketing, or clinical trial tracks. Past projects addressed topics like social media, serious games, telehealth, and disease management technologies. The course is open to students from various Harvard and MIT programs for credit.
This document is a certificate from Johns Hopkins University recognizing the completion of their online Data Science Specialization. The specialization consisted of 10 courses covering the data science pipeline from R programming to machine learning to developing data products, as well as a capstone project. It provides students with the concepts and tools needed to think analytically about problems, manage large data, perform statistical analysis, create visualizations, build machine learning models, publish analyses, and develop data products.
This document is a certificate from Johns Hopkins University recognizing the completion of their online Data Science Specialization by Sudhir Kudikala. The specialization consisted of 10 courses covering the data science pipeline from R programming to machine learning to developing data products, as well as a capstone project. It provides students with the concepts and tools needed to think analytically about problems, manage large data, perform statistical analysis, create visualizations, and build machine learning models.
The document is a certificate confirming that Fernando Sebastian Gonzalez Prada successfully completed the Data Science Specialization through Coursera. The specialization consisted of 10 courses covering the data science pipeline and tools, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The specialization trained participants to use data science tools, analyze complex problems, manage large datasets, apply statistical principles, create visualizations, build machine learning models, publish analyses, and develop data products.
The document is a certificate confirming that Rodrigo Euclides Carneiro successfully completed the Data Science Specialization through Coursera. The specialization consisted of 10 courses covering the data science pipeline and tools, including R programming, data cleaning, analysis, machine learning, and a capstone project. It was offered through the Johns Hopkins Bloomberg School of Public Health but does not confer an academic degree.
This document is a certificate of completion for a Data Science Specialization from Johns Hopkins University completed by Patrick Casimir. The specialization consisted of 10 courses covering topics like R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The specialization provided participants with the concepts and tools to work through an entire data science pipeline from data management to machine learning to publishing results.
The document is a certificate from Johns Hopkins University recognizing that Russell Robbins successfully completed the Data Science Specialization through Coursera. The specialization consisted of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The specialization provided participants with skills in using data science tools, analyzing complex problems, managing large datasets, applying statistical principles, creating visualizations, building and evaluating machine learning models, and publishing reproducible analyses.
The document is a certificate from Johns Hopkins University congratulating Frank Hasbani for successfully completing the Data Science Specialization online program. The specialization consisted of 10 courses covering the concepts and tools for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The specialization trained participants in using data science tools, analyzing complex problems, managing large datasets, applying statistical principles, creating visualizations, building and evaluating machine learning models, and publishing reproducible analyses.
Poornima N. Swamy is seeking a challenging position in biotechnology to utilize her 2.5 years of experience as a network support engineer and her M.Sc. in Biotechnology. She has skills in techniques like SDS-PAGE, DNA isolation, ligation, and transformation. She is pursuing a PG diploma in clinical research and has expertise in areas like clinical trials, clinical data management, pharmacovigilance, and medical writing. She has experience working as a trainee network support engineer and science faculty.
The document summarizes the development of a new system for managing limited submission research funding opportunities at the University of Washington. It describes limited submissions as research funding restricted by sponsors in terms of the number of proposals an institution can submit. It outlines two phases of development: phase 1 involved maintaining underlying data in a new mechanism, while phase 2 added a graphical user interface and data enhancements. Testing showed the new system met performance and user expectations, resulting in a successful replacement of the old administrative interface and campus site.
Jacob Bradley is a computer science student at John Carroll University with relevant coursework and experience in software development, design patterns, bioinformatics, and big data. He has worked on multiple internships at Cleveland Clinic developing software solutions utilizing technologies like C#, JavaScript, Java, MVC, and Solr. Bradley mentors junior interns, reviews code for maintainability and design standards, and has received awards for attending conferences and hackathons related to healthcare IT.
Electronic platforms like Survey Monkey, Qualtrics, and Question Pro can be useful tools for assessment, baseline studies, and simple information gathering, but have limitations for complex longitudinal studies, monitoring, and evaluation. While offering friendly interfaces for survey design and data collection, they provide only basic statistical analysis capabilities and require exporting data to other tools like SPSS or Excel for more robust analysis. These platforms reduce costs compared to printed surveys and make accessing internet-connected samples easier, but are not full monitoring and evaluation systems and may not be suitable for following indicators over time in complex, high-impact studies. Researchers should carefully consider the platform's functions and licensing options to determine what level of data collection, analysis and reporting it can adequately support for
Jessica Topolewski seeks a career in biotechnology sales and has extensive experience in vaccine development and laboratory techniques. She has a Bachelor's degree in Biology from the University of Georgia and has worked as a Research Technician at UGA's Tripp Lab focusing on high-throughput screening and maintaining cell cultures. Her qualifications include knowledge of molecular biology, immunology, and laboratory equipment as well as strong communication, time management, and presentation skills.
Eric B. Hollingsworth has over 15 years of experience working with large datasets and quantitative analysis. He currently works as a Senior Quantitative Analyst at Google, where he leads analyses of user behavior data across Google products and mentors new team members. Previously, he developed simulation and optimization models while working for Cornell University and Columbia University to improve public health responses. He is currently pursuing an M.S. in Management Science and Engineering from Stanford University.
Tajinder Singh is a recent college graduate seeking a career where he can apply his skills in IT, science, and the laboratory. He has a background in chemistry, biology, microbiology, and environmental science. His experience includes conducting water quality testing and using various laboratory instruments and techniques. He also has several years of experience in helpdesk, customer service, and technical support roles.
This document is a resume for Wenjun Sun, who is pursuing a PhD in Industrial Engineering at the University of Wisconsin-Madison with a focus on human factors and ergonomics. It outlines her education, including an expected PhD from UW-Madison in August 2015, as well as relevant experience in areas such as user research, usability testing, and interactive design projects. Her objective is to obtain a position related to human-computer interaction, user experience, and other areas involving human factors engineering.
This document is a resume for Steven Jervier. It summarizes his education, including an expected M.S. in Computer Information Systems from Boston University in 2015 and a B.S. in Information Technology from Northeastern University in 2011. It also lists his computer skills and knowledge in programming languages, web technologies, databases, operating systems, and electronic health records. Finally, it provides details on his relevant coursework and projects in areas like Java, databases, and health informatics, as well as his work experience in automotive sales and customer service.
Going Virtual: Evolving Real-World Evidence (RWE) Study DesignCovance
Site-based observational research has many challenges. Considerable time and cost is invested in identifying suitable sites and investigators, and then managing them to ensure recruitment and research are conducted in line with the protocol and local review board requirements. Furthermore, there is considerable burden to subjects - they may have to travel a distance to research sites to attend lengthy appointments, resulting in out-of-pocket expenses, lost time and inconvenience.
HI ,
Are you a Ph.D. Student?
We, Phoenix Focus Solution, recently have introduced a wonderful software which would be helpful for students one who struggling for their thesis report..you can complete your 100% genuine Doctorate reports with this software...
for more details call/whats app +60175617883
or send your email id to send to you more details
This document is a certificate of completion for the Data Science Specialization from Johns Hopkins University on Coursera. The specialization consists of 10 courses covering the concepts and tools needed for data science, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. The certificate verifies that Mei Chiao Lin successfully completed the online, non-credit specialization in data science.
The document is a certificate confirming that SPYRIDON PARASKEVAS successfully completed the online Data Science Specialization through Coursera. The specialization consisted of 10 courses covering the data science pipeline and tools, including R programming, getting and cleaning data, exploratory data analysis, reproducible research, statistical inference, regression models, practical machine learning, developing data products, and a capstone project. It was offered through the Departments of Biostatistics at Johns Hopkins Bloomberg School of Public Health.
Poornima N. Swamy is seeking a challenging position in biotechnology to utilize her 2.5 years of experience as a network support engineer and her M.Sc. in Biotechnology. She has skills in techniques like SDS-PAGE, DNA isolation, ligation, and transformation. She has also pursued additional diplomas in clinical research and has technical competencies in areas like clinical trials, clinical data management, pharmacovigilance, and medical writing. She has experience working as a trainee network support engineer and science faculty.
This document provides information about the HST.921 course on Information Technology in the Healthcare System of the Future offered at Harvard and MIT in spring 2009. The course aims to empower students to critically analyze current or future healthcare problems and develop novel IT solutions. It includes weekly lectures, tutorials/labs, and a group project. Students work in multidisciplinary teams on design, business, marketing, or clinical trial tracks. Past projects addressed topics like social media, serious games, telehealth, and disease management technologies. The course is open to students from various Harvard and MIT programs for credit.
Five Steps for Achieving Learning Analytics SuccessEllen Wagner
The document outlines five steps for achieving success with learning analytics: 1) Start with a focus on desired outcomes, 2) Clearly define what constitutes success, 3) Use common definitions to enable shared understanding, 4) Focus on generating insights from data rather than just collecting data, and 5) Share results and findings with others. It then provides more details on the Predictive Analytics Reporting (PAR) Framework, a collaborative focused on institutional effectiveness and student success using predictive analytics of integrated student data from member institutions.
A New Approach of Analysis of Student Results by using MapReduceIRJET Journal
1) The document proposes using Hadoop and MapReduce to analyze student result data to provide predictive modeling and insights. This can help students, faculty, and administrators improve outcomes.
2) Traditional data analysis methods take a long time when dealing with large datasets. Hadoop can distribute the work across clusters to speed up analysis. MapReduce breaks the work into smaller tasks that can run in parallel.
3) The proposed system would use Hadoop to extract and analyze accident data, then use predictive modeling to forecast times and locations of high accident rates. Encryption would secure the data during network transfer.
Enock Mbota has over a decade of experience managing clinical trial data and is seeking a position as a study monitor. He has extensive skills in data management, clinical trials monitoring, database development, and computer programming. Currently he is a data officer for the PROMISE Study at the University of North Carolina Project in Malawi, where his responsibilities include verifying data sources, maintaining data quality, and managing a team of data assistants. He has education and training in clinical research, data management, statistics, and computer science.
Data Science is an interdisciplinary field that utilizes various techniques from statistics, computer science, and domain knowledge to extract insights from structured and unstructured data. It plays a crucial role in decision-making across various industries, including healthcare, finance, and technology.
The document discusses creating and maintaining predictive healthcare solutions through a data science team that builds predictive models using a technology approach based on an open source big data stack and application platform to deliver predictive healthcare applications and care pathways that improve patient outcomes. It outlines the culture and strategy needed for a predictive healthcare organization, including having a chief data scientist lead various teams to execute projects that are reviewed at executive milestones.
Hitting the Sweet Spot with Predictive Analytics (Michael Draugelis)Ashleigh Kades
Speaker Presentation from U.S. News Healthcare of Tomorrow leadership summit, November 2-4, 2016 in Washington, DC. Find out more about this forum at www.usnewshot.com.
Data Science for Beginners: A Step-by-Step IntroductionUncodemy
Data science is a dynamic and rapidly evolving field that has gained immense importance in recent years. It involves the extraction of meaningful insights and knowledge from large and complex datasets. If you are new to data science, this step-by-step introduction will provide you with a solid foundation and explain why pursuing a data science certification course.
Arthur Castleton has extensive experience in biomedical research and engineering. He has worked on projects involving tissue decellularization, stem cell culture, biomaterial characterization, and medical device prototyping at multiple universities and companies. His education includes a Bachelor of Science in Chemical Engineering from Brigham Young University. He also has skills in areas such as programming, CAD, mechanical testing, polymer processing, data analysis, and quality systems. His objective is to make a valuable contribution to the medical field in research and development or engineering.
This document contains certificates from several online courses completed by Haoran Wang through MITx, edX, Coursera, and Udacity. The certificates are signed by professors from prestigious universities like MIT, Stanford, Johns Hopkins, Rice University, and The University of Texas, and indicate that Haoran Wang enrolled in and received a passing grade or completed with distinction courses in topics like computer science, programming, data science, machine learning, and statistics.
This document is a resume for Warner Giovani Alexis, who is seeking career opportunities in healthcare. He has a MS in Health Informatics expected in June 2018 and a BS in Biomedical Informatics. His experience includes research and data analysis work at Hunter College School of Public Health and New York City College of Technology. He has skills in languages like C++, Java, SQL and R and experience with databases, electronic health records, and genomic data analysis.
Bridging the Divide: High Technology in Low-resource Settings -- an update (S...James BonTempo
This document summarizes a project by Jhpiego to introduce electronic learning materials in limited-resource settings in Ethiopia. It describes assessing IT infrastructure at 3 universities, developing sample e-learning courses, and partnering with a firm to create a distributed learning management system. Immediate solutions included training local IT staff and setting up an e-learning lab. Recommendations focused on managing expectations, leveraging local expertise, obtaining stakeholder buy-in, and allowing mistakes to support long-term sustainability. Next steps included integrating e-learning into classrooms and clinical settings and expanding the program.
Joel Zucker is a technical lead and project manager with over 10 years of experience in software development, implementation, and business needs assessment across multiple industries. He has a track record of successfully directing teams through all phases of projects from requirements to development to implementation, on time and under budget. Zucker's experience includes increasing company revenues from $5M to $20M through a client tracking system, coordinating projects with budgets up to $300K, and generating millions in grant funding leading learning technology research.
DATA MINING FOR STUDENTS’ EMPLOYABILITY PREDICTIONCSEIJJournal
This study has been undertaken to predict the student employability.Assessing student employability
provides a method of integrating student abilities and employer business requirements, which is becoming
an increasingly important concern for academic institutions. Improving student evaluation techniques for
employability can help students to have a better understanding of business organizations and find the right
one for them. The data for the training classification models is gathered through a survey in which students
are asked to fill out a questionnaire in which they may indicate their abilities and academic achievement.
This information may be used to determine their competency in a variety of skill categories, including soft
skills, problem-solving skills and technical abilities and so on.The goal of this research is to use data
mining to predict student employability by considering different factors such as skills that the students have
gained during their diploma level and time duration with respect to the knowledge they have captured
when they expect the placement at the end of graduation. Further during this research most specific skills
with relevant to each job category also was identified. In this research for the prediction of the student
employability different data mining models such as such as KNN, Naive Bayer’s, and Decision Tree were
evaluated and out of that best model also was identified for this institute's student’s employability
prediction.So, in this research classification and association techniques were used and evaluated.
This document provides a summary of a clinical data manager with over 3 years of experience managing clinical trials from set up through close out. They have a Master's degree in Clinical Research and strong skills in clinical data management, regulatory guidelines, and database management tools.
This document contains certificates from several courses and programs completed by Ming Hui Goh, including:
1) An IBM Data Science Professional Certificate completed through Coursera, where Goh learned data science tools, Python, SQL, data analysis, visualization, and machine learning through courses and a capstone project.
2) A program on tackling big data challenges through edX in collaboration with MIT, covering the topic over 20 hours.
3) An AIIM BPM Master certificate awarded for completing the AIIM BPM Master Program.
4) A congratulatory notice for being shortlisted in the Telr Data Mining Challenge 2014.
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)Rebecca Bilbro
To honor ten years of PyData London, join Dr. Rebecca Bilbro as she takes us back in time to reflect on a little over ten years working as a data scientist. One of the many renegade PhDs who joined the fledgling field of data science of the 2010's, Rebecca will share lessons learned the hard way, often from watching data science projects go sideways and learning to fix broken things. Through the lens of these canon events, she'll identify some of the anti-patterns and red flags she's learned to steer around.
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
Do People Really Know Their Fertility Intentions? Correspondence between Sel...Xiao Xu
Fertility intention data from surveys often serve as a crucial component in modeling fertility behaviors. Yet, the persistent gap between stated intentions and actual fertility decisions, coupled with the prevalence of uncertain responses, has cast doubt on the overall utility of intentions and sparked controversies about their nature. In this study, we use survey data from a representative sample of Dutch women. With the help of open-ended questions (OEQs) on fertility and Natural Language Processing (NLP) methods, we are able to conduct an in-depth analysis of fertility narratives. Specifically, we annotate the (expert) perceived fertility intentions of respondents and compare them to their self-reported intentions from the survey. Through this analysis, we aim to reveal the disparities between self-reported intentions and the narratives. Furthermore, by applying neural topic modeling methods, we could uncover which topics and characteristics are more prevalent among respondents who exhibit a significant discrepancy between their stated intentions and their probable future behavior, as reflected in their narratives.
1. 10 Courses
The Data Scientist’s Toolbox
R Programming
Getting and Cleaning Data
Exploratory Data Analysis
Reproducible Research
Statistical Inference
Regression Models
Practical Machine Learning
Developing Data Products
Data Science Capstone
Jeff Leek, PhD; Roger
Peng, PhD; Brian Caffo,
PhD
Department of
Biostatistics
Johns Hopkins
Bloomberg School of
Public Health
03/14/2019
MOHAMMAD ABUARAR
has successfully completed the online, non-credit Specialization
Data Science
The Data Science Specialization covers the concepts and tools for
an entire data science pipeline. Successful participants learn how
to use the tools of the trade, think analytically about complex
problems, manage large data sets, deploy statistical principles,
create visualizations, build and evaluate machine learning
algorithms, publish reproducible analyses, and develop data
products. This certificate does not confer academic credit toward
a degree or official status at the Johns Hopkins University.
Verify this certificate at:
coursera.org/verify/specialization/R9XPYDFCPT4K