Search results “Phd data mining”
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-networking/
Views: 4892 PhDprojects. org
15 Hot Trending PHD Research Topics in Data Mining 2018
15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 5988 PhD Assistance
Get latest data mining Thesis Topics for master and phd
Get latest data mining thesis topics for master and phd in e2matrix . if you want proper guidance and thesis topics and projects and topics so you can contact to e2matrix . we are provides you proper guidance about your work . so you can't delay and do fast contact to e2matrix . call now : + 91 9041262727, e-mail: [email protected] Just click in the following link and Read more about cloud computing thesis topics http://www.e2matrix.com/blog/2018/07/04/data-mining-research-guidance-and-thesis-topics/ Thanks & Regards E2Matrix http://www.e2matrix.com/ Follow us on Social Media https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/?ref=settings https://twitter.com/e2matrix_lab https://www.instagram.com/e2matrixresearch/
An Introduction to the PhD in Data Science at NYU
The PhD in Data Science at New York University's Center for Data Science provides high-ability students with the knowledge and skill set to succeed in academia and industrial research settings. If you are ready to push the boundaries of the field of data science using machine learning and artificial intelligence, then we encourage you to apply.
Views: 22274 NYU Data Science
SMU PhD Research: Using Data Mining to Enhance Consumers’ Online Shopping Experience.
Have a glimpse of the type of research undertaken by Singapore Management University’s PhD in Information Systems candidate. With the guidance and support of his supervisor, Assistant Professor Hady W. Lauw, Maksim manages to navigate his way around obstacles to create a software that could potentially simplify and enhance consumers’ online shopping experience. This software is part of the research that Maksim is working on, and it could possibly create a bigger impact when applied within a larger application. Maksim’s dedication and commitment in his research and work in the area of “Data Mining and Artificial Intelligence” has earned him the SMU Presidential Doctoral Fellowship. His research papers have also been selected and published in top-tier publications. For more information, please visit https://smu.sg/phd-informationsystems.
Novel Data Mining Methods for Virtual Screening - PhD Defense
The Defense of PhD degree in Computer Science in King Abdullah University of Science and Technology (KAUST). Abstract: Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a confident conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by the big expansion in data and its dimensionality are inevitable. In this thesis, I address the main challenges that come when data mining techniques are used for virtual screening. In order to achieve an efficient virtual screening using data mining, I start by addressing the problem of feature selection and provide analysis of best ways to describe a chemical compound for an enhanced screening performance. High-throughput screening (HTS) assays data used for virtual screening are characterized by a great class imbalance. To handle this problem of class imbalance, I suggest using a novel algorithm called DRAMOTE to narrow down promising candidate chemicals aimed at interaction with specific molecular targets before they are experimentally evaluated. Existing works are mostly proposed for small-scale virtual screening based on making use of few thousands of interactions. Thus, I propose enabling large-scale (or big) virtual screening through learning millions of interaction while exploiting any relevant dependency for a better accuracy. A novel solution called DRABAL that incorporates structure learning of a Bayesian Network as a step to model dependency between the HTS assays, is showed to achieve significant improvements over existing state-of-the-art approaches.
Views: 489 Othman Soufan
Big Data Finance: PhD Thesis in Three Minutes
In this video, I briefly explain my PhD research work which I have been doing at the University of Zurich, Department of Banking and Finance, as a part of the Marie Curie program BigDataFinance: http://bigdatafinance.eu/ You can find more information on the research presented in our publicly available papers: "Agent-Based Model in Directional-Change Intrinsic Time" https://ssrn.com/abstract=3240456 "Instantaneous Volatility Seasonality of Bitcoin in Directional-Change Intrinsic Time" https://ssrn.com/abstract=3243797 This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675044. Content editor and post-production: Alisa Petrova, https://www.youtube.com/channel/UCqJEd9EPcxf-4c13JCK1p9w
PhD research topic in big data
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/network-security-research-topics/
Views: 6394 PhDprojects. org
PhD research topic in Cryptography
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-data-mining/
Views: 786 PhDprojects. org
Tentative steps towards mining PhD theses
Sara Gould, Development Manager, British Library Sara will talk about the Library’s recent participation in a national project to mine chemical compounds from the pages of PhD theses, describe some of the challenges in accessing theses for Text and Data Mining, and invite participants to ‘have a go’ at mining theses for new research purposes.
Analyzing and modeling complex and big data | Professor Maria Fasli | TEDxUniversityofEssex
This talk was given at a local TEDx event, produced independently of the TED Conferences. The amount of information that we are creating is increasing at an incredible speed. But how are we going to manage it? Professor Maria Fasli is based in the School of Computer Science and Electronic Engineering at the University of Essex. She obtained her BSc from the Department of Informatics of T.E.I. Thessaloniki (Greece). She received her PhD from the University of Essex in 2000 having worked under the supervision of Ray Turner in axiomatic systems for intelligent agents. She has previously worked in the area of data mining and machine learning. Her current research interests lie in agents and multi-agent systems and in particular formal theories for reasoning agents, group formation and social order as well as the applications of agent technology to e-commerce. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 143986 TEDx Talks
#FixCopyright:  Copyright & Research - Text & Data Mining (TDM) Explained
Read our blog post analysing the European Commission's (EC) text and data mining (TDM) exception and providing recommendations on how to improve it: http://bit.ly/2cE60sp Copy (short for Copyright) explains what text and data mining (TDM) is all about, and what hurdles researchers are currently facing. We also have a blog post on the TDM bits in the EC's Impact Assessment accompanying the proposal: http://bit.ly/2du9sYe Read more about the EC's copyright reform proposals in general: http://bit.ly/2cvAh0a
Views: 3436 FixCopyright
Data Mining in the Medical Field
Video about data mining in the medical field. Made by Aditya Jariwala, Alex Truitt, Tongfei Zhang, and Yishi Xu for Purdue COM 21700 final project, Spring 2017.
Views: 4207 Aditya Jariwala
Biomedical Big Data Revolution | Dr. Stefan Bekiranov | TEDxRVA
Find a cure for cancer from the comfort of your living room while in your PJs. It’s more possible today than it was a short time ago. We are currently undergoing a revolution in the field of biomedical research that will enable tailoring preventative strategies and therapies directly for each patient--Precision medicine. Systems Biologist, Stefan Bekiranov talks about what’s driving this revolution and how researchers are finding potential cures to diseases such as cancer at a faster rate than ever before. This talk was given at a local TEDx event, produced independently of the TED Conferences. It was filmed and edited by Tijo Media at the Carpenter Theatre at Dominion Arts Center in Richmond, VA. #medicalresearch #UVA #biomedical #bigdata #cancer #research #medicine After receiving his Bachelor of Science in Electrical Engineering from UCLA, Dr. Stefan Bekiranov worked as a microwave engineer at Raytheon Electromagnetic Systems Division in Santa Barbara. He received his PhD in theoretical condensed matter physics from the University of California at Santa Barbara and went on to do postdoctoral research in statistical/condensed matter physics at the University of Maryland. After that, Dr. Bekiranov conducted more postdoctoral research in computational biology at The Rockefeller University. He pioneered the analysis of high-resolution genomic tiling array data as a Bioinformatics Staff Scientist at Affymetrix. He is now an Associate Professor at the University of Virginia School of Medicine working in the fields of epigenomics and systems biology and has published over 50 papers in peer-reviewed journals. The ultimate goal of his work is to arrive at improved therapeutic targets to treat and hopefully, one day, cure cancer. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 18439 TEDx Talks
Project Financial data mining
kangaroo insurance model prediction
Views: 51 shangqu liu
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-contextaware-computing/
Views: 751 PhDprojects. org
PhD in data mining- Talaash Research Consultants
Talaash Research Consultants was established in the year 2010, with the purpose of offering consultation services to research scholars and guiding them towards successful completion of their research work. Talaash Research Consultants owns the pride of being the WORLD’S FIRST RESEARCH CONSULTANCY PURELY RUN BY WOMEN. Talaash Research Consultants has a team of female professionals with expertise in engineering, arts and management disciplines, thereby catering the needs of all the research scholars irrespective of the discipline to which they belong. With more than 5000+ satisfied clients from all over the world, Talaash continues to be a pioneer in terms of offering educational consultation services to the society at affordable prices. Talaash Research Consultants offer end to end services to research scholars in terms of doing their research work thereby being a ONE STOP SOLUTION FOR ALL PhD RELATED REQUIREMENTS. Talaash Research Consultants has the ability to offer consultation services to clients irrespective of their academic discipline because it has a huge number of committed and dedicated expert in various academic disciplines working in-house
Views: 8 Talaash Admin
DATA MINING: Predicting Tipping Points: By Dr. Philip Gordon, PhD
Tipping Points as evidenced in global events are, in many ways, influenced by media. DATA MINING for predicting and analyzing world events. This just released, ground-breaking book: DATA MINING: PREDICTING TIPPING POINTS by Dr. Philip Gordon, PhD, details three case studies which were selected on the basis of common Tipping Point Attributes: Each involved media contagiousness and stickiness during their development and, each arrived at a "dramatic moment in time", which could only be characterized by the phenomenon of Tipping Points. Three recent case studies explore the leading edge technologies of DATA MINING and the theory of TIPPING POINTS: The first case study, the 2008 Presidential Campaign of Barack Obama was chosen to examine a narrower scope and timeframe for the application of the analysis. In contrast to the second case study, the International Financial Crisis of 2007--2010, which involves a broader data study period to identify trends and more complex issues. The third study, Climate Change was included as consideration because the data mining research and analysis revealed critical relationships between Media Impact and Global Events. As the issue of Climate Change is still evolving, Dr. Gordon provides a Data Mining and Tipping Point Theory methodology for analyzing and predicting our planets' most pressing Global Tipping Points. Review Comments: "The genius of the formulation of DATA MINING: PREDICTING TIPPING POINTS is that it takes explicit account of the role of social media and the internet at facilitating bifurcations and promoting dynamical instability. In effect, we have trimmed a few feet of tail off the kite. As a reader, I was informed and educated as to the factors which conspire to influence stability / instability in complex social systems. ...the book does a good job of making sense of past bifurcations and dynamical instabilities, namely political instability, our perception of global climate change, and international economic crises...my compliments on a truly insightful Media Tipping Points." -Prof. Dr. (med.) Peter S. Geissler, A.B., B.S., M.S., M.Phil., Ph.D. (Yale) M.A., M.Eng., M.S., Ph.D., M.S., M.D., M.Phil.(Cantab) "A truly fascinating book that (teaches) a whole new way of thinking about major events and how the media can influence them. - Being a political junkie I was heavily into the media coverage of the 2008 Obama election and the global financial meltdown both via TV and the blogosphere. I now find myself looking for the tipping points and stickiness factors as other key events unfold. Usually, I have trouble reading theoretical books but this one was an easy read and if you want supporting data then the references are there. This could become a solid reference for those in the media who truly want to understand what they are reporting. Highly recommended and I look forward to Dr. Gordon's ongoing analysis of (future) events." -Dr. Ralph Moorhouse, Ph.D. Political junkie, Expert: natural polymers for industries "The application of Data Mining and Tipping Point Theory to media and global events, particularly the financial crisis and climate change, is a fascinating one." -Dr. Serge Besanger, PhD Expert, International Monetary Fund "...very interesting application (of the Tipping Point Theory)...potential opportunity for predicting other global events, i.e.: Egyptian crisis and perhaps, even terrorism activities." -Dr. Adam AJLANI, PhD Professor, Sciences Politic and Political Consultant, France TV1
Views: 275 BlueMatrixCatalog
PhD thesis : data analysis
http://thefreeschool.education Free peer tuition online at: http://chat.thefreeschool.education The Free School. Supporting graduate research scholars.
Basic AI terms Every PhD students should know - PhD Assistance
Artificial Intelligence is the buzzword now. The AI-enhanced smartphones and self-driven cars add more hype to the excitement in the world of artificial intelligence. Now let’s see some of the powerful terms in the world of artificial intelligence. Algorithms Can Artificial Intelligence survive without a set of instructions? These powerful rules help AI to learn on its own. Chatbots Well, the idea of conversation between human users and chat robots has risen. These are generally used interface for computer programs that consists of AI capabilities. Machine Learning The very essence of artificial intelligence is machine learning. It is even sometimes substituted with artificial intelligence. In machine learning, AI uses algorithms to carry out artificial intelligence functions. Deep Learning “Why” is a question that is mostly asked in any field of research. Deep learning, the subset of machine learning, applies specialized algorithms to comprehend complex structures of different data. Data mining When patterns are found within large sets of data for the purpose of extracting useful details from it, then it is called data mining. Neural Network These networks are formed to have similar aspects with that of the human nervous system and brain. There are stages of learning so that AI can solve difficult problems. Natural Language Processing Well, doesn’t the thought of AI interpreting human communication sound interesting? Natural Language Processing, an advanced neural network, is meant for this. Need Assistance for Your PhD Research & any queries or guidance feel free to contact us at : www.phdassistance.com mail us : [email protected] Call : +91 8754446690
Views: 245 PhD Assistance
Educational Data Mining: Predict the Future, Change the Future
Teachers College is proud to introduce the 2012-13 Julius and Rosa Sachs Distinguished Lecturer Professor Ryan Baker, Columbia University. Ryan Shaun Joazeiro de Baker is Visiting Associate Professor in the Department of Human Development. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University, and was a post-doctoral fellow in the Learning Sciences at the University of Nottingham. He earned his Bachelor's Degree (Sc.B.) in Computer Science from Brown University. Dr. Baker has been Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute. He previously served as the first Technical Director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding President of the International Educational Data Mining Society, and as Associate Editor of the Journal of Educational Data Mining. His research combines educational data mining, learning analytics and quantitative field observation methods in order to better understand how students respond to educational software, and how these responses impact their learning. He studies these issues within intelligent tutors, simulations, and educational games. In recent years, he and his colleagues have developed strategies to make inferences in real-time about students' motivation, meta-cognition, affect, and robust learning.
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-dependable-secure-computing/
Views: 773 PhDprojects. org
Data Analyst vs Data Engineer vs Data Scientist | Data Analytics Masters Program | Edureka
** Data Analytics Masters' Program: https://www.edureka.co/masters-program/data-analyst-certification ** ** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Analyst vs Data Engineer vs Data Scientist" will help you understand the various similarities and differences between them. Also, you will get a complete roadmap along with the skills required to get into a data-related career. Below topics are covered in this video: 1:05 - Who is data analyst, data engineer and data scientist? 2:32 - Roadmap 3:48 - Required skill-sets 5:34 - Roles and Responsibilities 7:16 - Salary Perspective ------------------------------------- Data Science Training Playlist: https://goo.gl/Jg1pJJ Blog Series: https://goo.gl/H2pf8V Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #dataanalystvsdataengineervsdatascientist #DataScience #DataScienceCertificationTraining ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Data Science Training and Certification, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Views: 42209 edureka!
Choosing a PhD in Computer Science: Hoda Eldardiry
Hoda Eldardiry (PhD Purdue) talks about her work on predictive analytics, using machine learning and data mining at Palo Alto Research Center (PARC). This video was designed in conjunction with award-winning producer Patrick Sammon (co-producer of “Codebreaker”) to explain the benefits of pursuing a PhD in CS. This video showcases a young researcher with a PhD who is now working in industry as they talk about what compelled them to pursue a doctorate and how they are using their advanced training in their work. While many undergraduates understand that a PhD is needed for a position in academia, this video demonstrates how a PhD can be useful in industry as well.
Hemant Purohit PhD Defense 23 Jun 2015
Hemant Purohit Dissertation Defense “MINING BEHAVIOR OF CITIZEN SENSOR COMMUNITIES TO IMPROVE COOPERATION WITH ORGANIZATIONAL ACTORS” Ph.D. Committee: Drs. Amit Sheth, Advisor, TK Prasad, Guozhu Dong, Valerie Shalin, Psychology, Srinivasan Parthasarathy, CSE, Ohio State University, and Patrick Meier, QCRI ABSTRACT: Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award. Additionally, the intent classification technology for identifying seeking-offering of help during a crisis was integrated by the crisis-mapping pioneer Ushahidi’s project, CrisisNET for broader impact. Slideshare: http://www.slideshare.net/knoesis/hemant-purohit-phd-defense-mining-citizen-sensor-communities-for-cooperation-with-organizations NSF SOCS project on organizational sensemaking during emergencies: http://knoesis.org/projects/socs Thesis Webpage: http://www.knoesis.org/aboutus/thesis_defense#video_hemant
Views: 1079 Knoesis Center
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 66092 Well Academy
Cosma Shalizi - Why Economics Needs Data Mining
Cosma Shalizi urges economists to stop doing what they are doing: Fitting large complex models to a small set of highly correlated time series data. Once you add enough variables, parameters, bells and whistles, your model can fit past data very well, and yet fail miserably in the future. Shalizi tells us how to separate the wheat from the chaff, how to compensate for overfitting and prevent models from memorizing noise. He introduces techniques from data mining and machine learning to economics -- this is new economic thinking.
Views: 11664 New Economic Thinking
PhD research topic in Image Mining
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-cloud-computing/
Views: 926 PhDprojects. org
Data Science @Stanford- Bonnie Berger, PhD
Bonnie Berger, PhD, head of the Computational and Biology group at MIT's Computer Science and Artificial Intelligence Laboratory will discuss applying mathematical techniques to problems in molecular biology.
Views: 7175 Stanford
How to become a Data Analyst in India - Course and career
This video discuss How to become a data analyst in India. For more videos on Jobs &Careers :https://www.youtube.com/channel/UCEFTTJFLp4GipA7BLZNTXvA?view_as=subscriber For aptitude classes :https://www.youtube.com/watch?v=lxm6ez2cx6Y&list=PLjLhUHPsqNYnM1DmZhIbtd9wNhPO1HGPT Every business collects data such as sales figures, market research, logistics, or transportation costs. A data analyst's job is to take that data and analyse it to help companies make better business decisions. Some examples of a data analyst basic job functions include: 1) estimating market shares; 2) establishing a price of new materials for the market; 3) reducing transportation costs; 4) timing of sales and 5) figuring out when to hire or reduce the workforce.Data analysts are responsible for collecting, manipulating, and analyzing data. How To Get There? By obtaining a university degree, learning important analytical skills, and gaining valuable work experience, you can become a successful data analyst. A bachelor's degree is needed for most entry-level jobs, and a master's degree will be needed for many upper-level jobs. To become an initial level data analyst, you’ll have to earn a degree in a subject such as mathematics, statistics, economics, marketing, finance, or computer science. Higher level data analyst jobs may require a master’s or doctoral degree, and they usually guarantee higher pay. Individuals looking for data analyst jobs must be knowledgeable in computer programs such as Microsoft Excel, Microsoft Access, SharePoint, and SQL databases. Data analysts also must have good communication skills, as they must have an open line of communication with the companies with which they work. Lets see some of the Best courses on Analytics offered in India. 1. Advanced Analytics for Management – IIM This program enables practitioners, managers, and decision-makers to use advanced analytics for better decision-making 2. Analytics Essentials – IIIT, Bangalore “Analytics Essentials”is a 3 months week-end program by International Institute of Information Technology Bangalore (IIITB)providing a foundational certification course in Business Analytics 3. Business Analytics and Intelligence (BAI) – IIM Bangalore This course provides in-depth knowledge of handling data and Business Analytics’ tools that can be used for fact-based decision-making. The participants will be able to analyse and solve problems from different industries such as manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace etc. 4. Certificate Program in Business Analytics – ISB, Hyderabad A combination of classroom and Technology aided learning platform, .Participants will typically be on campus for a 5 day schedule of classroom learning every alternate month for a span of 12 months, which would ideally be planned to include a weekend. 5. Data Analysis Online courses – SRM University SRM University offers part time online courses in data analysis in collaboration with Coursera, edX, Udacity. 6. Executive Program in Business Analytics – IIM Calcutta This executive 1 year long distance program is designed to expose participants to the tools and techniques of analytics. The program covers topics such as Data Mining, Soft Computing, Design of Experiments, Survey Sampling, Statistical Inference, Investment Management, Financial Modelling, Advanced marketing Research etc. 7. Executive Program in Business Analytics and Business Intelligence – IIM Ranchi Course duration is 3 months. Classes will be conducted by eminent professors and industry experts in the weekends in Mumbai/ Kolkata /Delhi /Bengaluru and in addition to these, there will be one-week learning in IIM Ranchi. 8. Jigsaw Academy courses Jigsaw Academy provides some online analytics courses.Their courses include; Foundation Course in Analytics Data Science Certification Human Resources (HR) Analytics Course Big Data Analytics Using Hadoop and R Advanced Certification in Retail Analytics Advanced Course in Financial Analytics Analytics with R Great Lakes PG Course in Business Analytics 9. M. Tech. Computer Science and Engineering with Specialization in Big Data Analytics – VIT VIT offers full time course in Big Data analysis to promote an academic career for further research in theoretical as well as applied aspects of Big Data Analytics 10. M.Tech (Database Systems) – SRM University SRM University offers a two year full time course in database systems where the students are exposed to theoretical concepts complemented by related practical experiments. 11. M.Tech Computer Engineering and Predictive Analytics – Crescent Engineering College Salary The Salary of Data analysts depends on job responsibilities. An entry-level data analyst with basic technical tools might be looking at anything from Rs. 5 lakhs to 12 lakhs per year. A senior data analyst with the skills of a data scientist can command a high price. #dataanalyst #careeroptions #datascience
Data Scientist Vs Data Analyst
In this video I want to talk about the differences between a data scientist and a data analyst. is data science a viable career and if so should you try to become a data scientist or a data analyst. ► Full Playlist Exploring All Things Data Science ( http://bit.ly/2mB4G0N ) ► Top 4 Best Laptops for the Data Industry ( https://youtu.be/Vtk50Um_yxA ) ► Data Scientist Masters Certification ( http://bit.ly/2yCbsac ) ► Get the Best Certified Tutorials on Data Analytics... http://jobsinthefuture.com/index.php/2017/10/13/data-scientist-vs-data-analytics-what-is-the-big-data-difference/ Questions: - What is the best career path for a data scientist? - How do I become a data analyst? - What is the difference between a data scientist and a data analyst? - Is data science the same as data analytics? - Is data science a viable career path? - Is data analytics a viable career path? Jobs related to data science are booming right now with the tech industry growing at a rapid pace, but there is a lot of confusion between the Role of a Data Scientist and a Data Analyst... I am going to QUICKLY breakdown the difference for you so that you can get started right away with your career in the Data Analytics industry! First of all what is data analytics... Data analytics is the extraction a large, large, large amounts of data that are stored within a data base. This data comes from a multiplicity of places all over the world via website traffic, in-store and online purchases, social media activity, traffic patterns, etc, etc, etc.... the list could go on and on. Basically everything we do is being collected to be used as data to advertise to us, keep us safer when we are driving, or help us find the restaurant we want to eat at. Now to The Role of Data Scientist - The IT Rock Star! Data Scientists are the top professionals in their industry. They usually hold a Masters Degree in some relative Computer Science degree or even a PhD. They understand, very well, data from a business point of view and he/she can make accurate prediction of the data to advise clients on their next big business move! Data scientists have a solid foundation of computer applications, modeling, statistics and math! Highly Advanced in coding (Python, MySQL, R, JavaScript, etc... Ability to do high levels of math quickly Fantastic Statistical Analysis Abilities Great Communication Skill: Written and Oral And they have a brilliant Knack for communicating between the IT world and the Business Professionals. Starting Salary: $115,000 The Role of Data Analyst A Data Analyst is very important in the world of data science. They are in charge of collecting, organizing, and and obtaining statistical information from a large amount of data sets (Data sets are very large pools of data that must be searched in order to find the data that is relevant to a specific study). They are also the ones responsible for formulating all of their findings into an accurate report of powerpoint presentation to give to their client or internal team. Strong Understanding of Hadoop Based Analytics (Program to help extract data from large data sets and the analyze the data) Familiar with data analytics in a business setting Must have data storing a retrieval skills Proficiency in decision making Have the ability to transform data into understandable presentation Starting Salary: $60,000 ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 106604 Ben G Kaiser
Putri Wikie Novianti, PhD - Menjadi Growth Scientist & Memanfaatkan Insight Big Data  | BukaTalks
Growth Scientist adalah posisi yang belum begitu familiar di Indonesia. Selama bekerja di Bukalapak, Putri, berusaha untuk menumbuhkan North Star Metric (NSM). NSM adalah metriks yang dipantau sehari-hari dari segi pertumbuhannya, guna menjadi acuan pertumbuhan perusahaan (Bukalapak). BukaTalks edisi How To Kickstart Your Career in Data, Putri Wikie Novianti (Growth Scientist Bukalapak) akan menjelaskan lebih mendalam apa itu dan bagaimana cara kerja Growth Scientis dan pembagian NST yang berada di Bukalapak Yuk, tonton video BukaTalks-nya sekarang juga! ----------------------------------------------------------------------------------------------------- BukaTalks : BukaTalks adalah sebuah inisiatif dari Bukalapak untuk membangun wadah bagi para kreator dan inovator untuk berbagi cerita inspiratif bagi kaum muda, dalam format diskusi yang diadakan rutin setiap bulannya. Tema BukaTalks dibuat beragam, mulai dari teknologi hingga kesenian, dengan menghadirkan topik dan narasumber yang dapat membuka mata penonton dan memberikan perspektif baru. ---------------------------------------------------------------------------------------------------- Subscribe: http://bl.id/subs-bukalapak BukaTalks - How to Kick-start Your Career in Data: https://www.youtube.com/playlist?list=PLzMtIVEHDtNraShVIQ0Ez-WBywJCy_h47 Video #BukaTalks lainnya: https://www.youtube.com/watch?v=YfCUBLzDG04&list=PLzMtIVEHDtNqirMVMEh63Or3g3nkMRbkr Website: https://www.bukalapak.com/ Download Aplikasi Bukalapak di sini iOS: https://itunes.apple.com/id/app/bukalapak-jual-beli-online/id1003169137?l=id Google Play: https://play.google.com/store/apps/details?id=com.bukalapak.android Like / Follow Social Media Bukalapak: Facebook - https://www.facebook.com/Bukalapak Twitter - https://www.twitter.com/Bukalapak Instagram - https://instagram.com/bukalapak Google Plus - https://plus.google.com/+bukalapakdotcom Forum Komunitas - https://komunitas.bukalapak.com Stack Overflow - https://stackoverflow.com/jobs/companies/bukalapak LinkedIn - https://www.linkedin.com/company/pt-bukalapak-com
Views: 5261 Bukalapak
Talks@12: Data Science & Medicine
Innovations in ways to compile, assess and act on the ever-increasing quantities of health data are changing the practice and police of medicine. Statisticians Laura Hatfield and Sherri Rose will discuss recent methodological advances and the impact of big data on human health. Speakers: Laura Hatfield, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Sherri Rose, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Like Harvard Medical School on Facebook: https://goo.gl/4dwXyZ Follow on Twitter: https://goo.gl/GbrmQM Follow on Instagram: https://goo.gl/s1w4up Follow on LinkedIn: https://goo.gl/04vRgY Website: https://hms.harvard.edu/
Phd viva-voce  presentation on work life balance part-I
PhD Viva-voce Presentation on the title "A Study on Women Employees’ Attitude about Work Life Balance with special reference to Banking, Healthcare and IT/ITES Sectors in Kanchipuram District,Tamilnadu", by Dr.A.VANITHA, Assistant professor, SCSVMV University, Enathur, Kanchipuram-631561,Tamilnadu, India.
Views: 61830 Vanitha Allavaram
Wisdom of the Crowd or Tyranny of the Mob? Data-Mining Electronic Medical Records
Wisdom of the Crowd or Tyranny of the Mob? Data-Mining Electronic Medical Records for Clinical Decision Support - Jonathan H Chen MD, PhD - Stanford Medicine Grand Rounds - February 2016 Edited to remove Epic screenshots
Views: 184 Jonathan Chen
Choosing a PhD in Computer Science: Tiffany Chen
Tiffany Chen (PhD Stanford) talks about her work in bioinformatics at Cytobank. This video was designed in conjunction with award-winning producer Patrick Sammon (co-producer of “Codebreaker”) to explain the benefits of pursuing a PhD in CS. This video showcases a young researcher with a PhD who is now working in industry as they talk about what compelled them to pursue a doctorate and how they are using their advanced training in their work. While many undergraduates understand that a PhD is needed for a position in academia, this video demonstrates how a PhD can be useful in industry as well.
Finish a PhD in Data Science
How did I finish my PhD and have a full-time job at the same time? I have started recording some of my conversations with future data scientists. This is the result.
Views: 371 Learn Data Science
Arturo, PhD Data Science Intern
Arturo shares more about his experiences as a PhD Data Science Intern at Target, including applying skills acquired within the industry and academia to problem solve and tackle forecasting challenges in order to positively impact our guests.
Views: 1921 Target
The Future of Data Science - Data Science @ Stanford
Data science holds the potential to impact our lives and how we work dramatically. Despite its promise, many questions about data science remain. How real is this emerging discipline? What opportunities and challenges does it present? How can Stanford nurture data science in research and education? Watch the video and hear some of Stanford's thought leaders debate the answers to these questions.
Views: 97245 Stanford
How I Started a Career in Machine Learning - No PHD Required
Learn more about AWS DeepRacer at – https://amzn.to/2ZvMuIi  Alex Schultz shares his story of how he became a Machine Learning Developer through hands-on learning with AWS DeepLens and AWS DeepRacer. Find AWS DeepRacer on Twitter: #AWSDeepRacer Follow AWS DeepRacer on Instagram: https://www.instagram.com/awsdeepracer/
Views: 1511 Amazon Web Services
PhD Public Defence - Tingting Liu
Demystification of Hidden Markov Models: on Identifiability, Learnability and Applicability Hidden Markov Models (HMMs) applied in machine learning and data mining fields has been an active research topic in computer science over the past decades, ranging from theoretical to very practical. Recognized by its competitive advantages in temporal pattern recognition, HMMs have been widely and successfully applied in diverse application fields such as speech recognition, hand-writing and text recognition, biosciences, machine maintenance and image processing, etc. Despite their success, a deep understanding is still lacking. HMMs are being used as black boxes, without understanding which parameter configurations make models unique. Such models cannot be mimicked by other models and therefore are the ones we should be able to learn from data. Despite some theoretical barriers, there has been significant process on the practical side, albeit often without a sound understanding about the underlying mathematical principles. This is a common problem of black-box solutions with most of the state-of-the-art statistical modelling techniques. This dissertation contributes to this research domain by introducing numerous novel concepts that helps to better understand the theory of the identification of HMMs. Based on an analysis of how information passes through the model, deep insights are provided to demystify HMMs with respect to identifiability and learnability. Apart from the progress on the theoretical side, we propose a novel initialization approach for the traditional Baum-Welch (BW) algorithm, called the Segmentation-Clustering and Transient (SCT) based learning method, that improves both the efficiency and the effectiveness in learning HMMs compared to the classical BW approach which often uses random guess of model parameters. Finally, a validation framework is proposed to evaluate the reliability and robustness of predictors built on the learned models. Empirical assessments conducted in this dissertation indicate that the presented research offers a better understanding in HMMs as well as providing competitive results compared to prior state-of-the-art work.
Views: 40 Tingting LIU
Intro to Julia for data science
Join us on July 25 (10AM PDT/1PM EDT/19:00CET/10:30PM IST) for a tutorial with Huda Nassar! Huda is a PhD candidate at Purdue and author of `MatrixNetworks.jl`. In this tutorial, she will show how to work with your data in Julia, including data processing, algorithms, and visualizations You can follow along and interact with tutorial materials without installing anything at juliabox.com. See you on the 25th! Visit http://julialang.org/ to download Julia.
Views: 23070 The Julia Language
How to Become a Data Scientist in 2017? | Data Scientist Career | Data Science Future
About the Webinar : Agenda of this session will include answers to the following questions: 1. Why is it the best time to take up Data Science as a career? 2. How can you take the first step in Data Science? (After all, first step is always the hardest!) 3. How can you become better and progress fast? 4. How is life after becoming a Data Scientist? About the Host : Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his research focused on numerical weather and climate prediction. Slides: https://www.slideshare.net/HackerEarth/how-to-become-a-data-scientist-72258005 Questions Answered: 1. How can a college fresher (say, studying in sophomore or final year) become a data scientist ? What projects can they do ? What skills should they focus on ? How to start applying for jobs ? 2. How can an experienced professional make a career shift into data science ? Let's say, someone has 3 years of experience in Java, and he now wants to become a data scientist. Or, let's say, someone knows Hive, Pig, Flume, Hadoop, what could be natural career progression for him ? 3. How is a Machine Learning Engineer different from Data Scientist ? 4. How is a Statistician different from a Data Scientist ? 5. How is a Data Engineer different from a Data Scientist ? 6. What is the relationship between data science and machine learning ? 7. What are the most commonly used ML algorithms in industry today, so that students can master them first ? 8. What is the future of Data Scientist job ? Will it survive after 5 - 10 years or get automated? 9. Can data science be used in building geological applications ? If yes, what would be the starting point ? More webinars and updates : https://goo.gl/MEAALs Subscribe our channel for More Updates : https://goo.gl/suzeTB About us: HackerEarth is the most comprehensive developer assessment software that helps companies to accurately measure the skills of developers during the recruiting process. More than 500 companies across the globe use HackerEarth to improve the quality of their engineering hires and reduce the time spent by recruiters on screening candidates. Over the years, we have also built a thriving community of 2.5M+ developers that come to HackerEarth to participate in hackathons and coding challenges to assess their skills and compete in the community.
Views: 275106 HackerEarth
Machine learning in plant breeding - Alencar Xavier's PhD defense
Increasingly, new sources of data are being incorporated into plant breeding pipelines. Enormous amounts of data from field phenomics and genotyping technologies places data mining and analysis into a completely different level that is challenging from practical and theoretical standpoints. Intelligent decision-making relies on our capability of extracting from data useful information that may help us to achieve our goals more efficiently. Many plant breeders, agronomists and geneticists perform analyses without knowing relevant underlying assumptions, strengths or pitfalls of the employed methods. The study endeavors to assess statistical learning properties and plant breeding applications of supervised and unsupervised machine learning techniques. A soybean nested association panel (aka. SoyNAM) was the base-population for experiments designed in situ and in silico. We used mixed models and Markov random fields to evaluate phenotypic-genotypic-environmental associations among traits and learning properties of genome-wide prediction methods. Alternative methods for analyses were proposed. PRESENTER: http://alenxav.wix.com/home
Views: 1043 Alencar Xavier
NLP & EHR Data Mining: Hua Xu
After viewing the video, please take a moment to complete an evaluation of the presentation. https://www.surveymonkey.com/s/M8VT9BP Hua Xu talks about his research interests including Natural language processing (NLP) and Electronic Health Records (EHR) Data Mining. Hua Xu, Ph.D. SBMI Associate Professor Director, Center for Computational Biomedicine
Views: 1298 UTHealth SBMI
Data mining from social media for discovering trends in prescription medication abuse
Abeed Sarker, PHD, Research Associate , Informatics, Health Language Processing Lab,The University of Pennsylvania, presents at the Healthcare Informatics Presentation Series, hosted by The Department of Biomedical and Health Informatics at Children's Hospital of Philadelphia.
Views: 29 CHOP DBHi
Aggregating research papers from publishers systems to support text and data mining
Nancy Pontica Zeeba TV (http://zeeba.tv) is part of the River Valley group of Companies. http://www.rivervalleytechnologies.com/
Views: 43 Zeeba TV