This document summarizes a presentation on data science applications in business. It begins with defining data science and the work of data scientists. It then describes two case studies: the first uses clustering and prediction models to help a sports company with inventory management, while the second aims to reduce flight ticket costs by forecasting price fluctuations and choosing the lowest price date. The document provides details on the data, models, and results for each case study. It concludes with information about the presenter.
Introduction to Data Science - Week 4 - Tools and Technologies in Data ScienceFerdin Joe John Joseph PhD
This document discusses tools and technologies used in data science. It covers popular programming languages like Python, R, Java and C++. It also discusses databases, data analytics tools, APIs, servers, and frameworks. Specific tools mentioned include Hadoop, Spark, Tableau, IBM SPSS, SAS, and Excel. The document provides brief descriptions and examples of how these various tools are used in data science.
Data scientists are in high demand due to a shortage projected between 140,000-190,000 by 2018. Data scientists love data and have an investigative mindset, using data to find patterns and create data-driven products. They have strong programming, statistics, and machine learning skills. Universities and online courses provide data science education, while conferences and meetups help data scientists network and stay informed of new developments in the field. Open questions remain around how important domain expertise is and whether data scientists will eventually be replaced by software.
This document outlines the course structure and content for a Data Science course. The 5 modules cover: 1) introductions to data science concepts and statistical inference using R; 2) exploratory data analysis and machine learning algorithms; 3) feature generation/selection and additional machine learning algorithms; 4) recommendation systems and dimensionality reduction; 5) mining social network graphs and data visualization. The course aims to teach students to define data science fundamentals, demonstrate the data science process, explain necessary machine learning algorithms, illustrate data analysis techniques, and follow ethics in data visualization.
Data science applications and use cases were discussed. Examples included using data science in business for tasks like car design and insurance, in healthcare for reducing readmissions and improving care, and in urban planning to address challenges in growing cities. Cancer research was highlighted as an area using big data analytics and machine learning to identify patterns linked to cancer. Healthcare examples included using genetic data at Stanford Medicine for precision health. Data science was applied to political elections through Obama's targeted social media campaigns. Finally, the growing field of internet of things was noted as an area that will produce huge volumes of data for analysis.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
This document summarizes a presentation on data science applications in business. It begins with defining data science and the work of data scientists. It then describes two case studies: the first uses clustering and prediction models to help a sports company with inventory management, while the second aims to reduce flight ticket costs by forecasting price fluctuations and choosing the lowest price date. The document provides details on the data, models, and results for each case study. It concludes with information about the presenter.
Introduction to Data Science - Week 4 - Tools and Technologies in Data ScienceFerdin Joe John Joseph PhD
This document discusses tools and technologies used in data science. It covers popular programming languages like Python, R, Java and C++. It also discusses databases, data analytics tools, APIs, servers, and frameworks. Specific tools mentioned include Hadoop, Spark, Tableau, IBM SPSS, SAS, and Excel. The document provides brief descriptions and examples of how these various tools are used in data science.
Data scientists are in high demand due to a shortage projected between 140,000-190,000 by 2018. Data scientists love data and have an investigative mindset, using data to find patterns and create data-driven products. They have strong programming, statistics, and machine learning skills. Universities and online courses provide data science education, while conferences and meetups help data scientists network and stay informed of new developments in the field. Open questions remain around how important domain expertise is and whether data scientists will eventually be replaced by software.
This document outlines the course structure and content for a Data Science course. The 5 modules cover: 1) introductions to data science concepts and statistical inference using R; 2) exploratory data analysis and machine learning algorithms; 3) feature generation/selection and additional machine learning algorithms; 4) recommendation systems and dimensionality reduction; 5) mining social network graphs and data visualization. The course aims to teach students to define data science fundamentals, demonstrate the data science process, explain necessary machine learning algorithms, illustrate data analysis techniques, and follow ethics in data visualization.
Data science applications and use cases were discussed. Examples included using data science in business for tasks like car design and insurance, in healthcare for reducing readmissions and improving care, and in urban planning to address challenges in growing cities. Cancer research was highlighted as an area using big data analytics and machine learning to identify patterns linked to cancer. Healthcare examples included using genetic data at Stanford Medicine for precision health. Data science was applied to political elections through Obama's targeted social media campaigns. Finally, the growing field of internet of things was noted as an area that will produce huge volumes of data for analysis.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
The document provides an overview of data science through an introduction by Sreejith C, a data scientist. It defines data science as discovering unknown information from data, obtaining predictive insights, creating impactful data products, and communicating business stories from data. A data scientist's work includes tasks like authoring data processing pipelines, performing analyses, and communicating results. The document also demonstrates a loan prediction problem using machine learning algorithms like logistic regression, decision trees, and random forests in Python.
Adding Open Data Value to 'Closed Data' ProblemsSimon Price
Drawing on cutting edge examples from the University of Bristol and the City of Bristol, Simon will discuss innovative applications of data science that derive business value from open data through enriching and integrating with confidential 'closed data'. He also highlights recent technological advances that are enabling open data science on highly sensitive closed data.
This document provides an overview of machine learning. It begins by defining machine learning as a field of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. The document then discusses where data comes from, different types of data, and what data analytics is. It also explains how machine learning is related to data analytics and describes some common assumptions and architectures of machine learning models. Finally, it gives examples of how machine learning is used and provides an overview of supervised vs. unsupervised learning approaches.
COM 578 Empirical Methods in Machine Learning and Data Miningbutest
This document provides an overview and summary of the COM 578 Empirical Methods in Machine Learning and Data Mining course. It outlines the course topics, grading structure, office hours, homework assignments, final project, and textbooks. Key topics covered in the course include decision trees, k-nearest neighbor, neural networks, support vectors, association rules, clustering, and boosting/bagging. The final project involves applying machine learning techniques to train models on two different datasets.
The document outlines a data science roadmap that covers fundamental concepts, statistics, programming, machine learning, text mining, data visualization, big data, data ingestion, data munging, and tools. It provides the percentage of time that should be spent on each topic, and lists specific techniques in each area, such as linear regression, decision trees, and MapReduce in big data.
This document provides an overview of programming in Python for data science. It discusses Python's history and timeline, its versatile capabilities across different programming paradigms, and its simple and clear syntax. Key features that make Python popular for data science are highlighted, such as its comprehensive standard library and support for numeric, scientific, and GUI programming. The document also compares Python 2 and 3, describes different ways to run Python programs, and lists popular Python packages for data science. Overall, it serves as an introduction to Python for newcomers and outlines its relevance and widespread adoption in the field of data science.
The document discusses the steps involved in the data science life cycle (DSLC). It describes the main steps as business understanding, data acquisition and understanding, modeling, deployment, and customer acceptance. It provides details on several of these steps, including business understanding, data acquisition and understanding, data modeling, and initial data exploration. The goal is to clearly outline the typical process and considerations for a data science project from defining the problem to exploring the available data.
Studying Data Science At University In The United KingdomThomas Lancaster
Data Science as emerging as a valued choice of qualification and subsequent career. The skills of Data Scientists and Data Analysts are in demand, with many jobs looking for the mix of programmatic and analytical skills needed for success in this field. This presentation provides an overview of the types of courses available for people looking to specialise in Data Science and considers how interested parties can best prepare themselves for a career in this area.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
This document provides an introduction to data science. It discusses that data science uses computer science, statistics, machine learning, visualization, and human-computer interaction to collect, clean, analyze, visualize, and interact with data to create data products. It also describes the data science lifecycle as involving discovery, data preparation, model planning, model building, operationalizing models, and communicating results. Finally, it lists some common tools used in data science like Python, R, SQL, and Tableau.
1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management.
2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data.
3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
This document summarizes a seminar on machine learning using big data. It discusses the history of data storage and traditional databases. It then introduces machine learning and the types of learning, including supervised and unsupervised learning. Specific algorithms for each type are covered such as k-means clustering for unsupervised and naive Bayes for supervised. Case studies on applications like Amazon product recommendations are presented. The document concludes by discussing tools for machine learning and future applications as more connected devices generate extensive data.
Session 01 designing and scoping a data science projectbodaceacat
This document provides an overview of the first session in a data science training series. It discusses designing and scoping a data science project. Key points include: defining data science and the data science process; describing the roles of problem owners and competitors; reviewing examples of data science competitions from Kaggle, DrivenData, and DataKind; and providing guidance on writing an effective problem statement by specifying the context, needs, vision, and intended outcomes of a project. The document also briefly covers data science ethics considerations like ensuring privacy and minimizing risks. Exercises are included to help participants practice asking interesting questions, identifying relevant data sources, and designing communications for target audiences.
The document provides an overview of data science applications and use cases. It defines data science as using computer science, statistics, machine learning and other techniques to analyze data and create data products to help businesses make better decisions. It discusses big data challenges, the differences between data science and software engineering, and key areas of data science competence including data analytics, engineering, domain expertise and data management. Finally, it outlines several common data science applications and use cases such as recommender systems, credit scoring, dynamic pricing, customer churn analysis and fraud detection with examples of how each works and real world cases.
Introduction to Computational StatisticsSetia Pramana
This document outlines courses in computational statistics that utilize various statistical software packages like R, SPSS, and Excel. The courses cover topics ranging from data preparation and visualization to statistical modeling techniques like linear regression, resampling methods, and hypothesis testing. They emphasize hands-on practice over theory, involve group projects, and provide reference materials for further learning.
This document discusses uncertainty in big data analytics. It begins by providing background on big data, defining the common "5 V's" characteristics of big data - volume, variety, velocity, veracity, and value. It then discusses uncertainty, which exists in big data due to noise, incompleteness, and inconsistency in data. The document surveys techniques for big data analytics and how uncertainty impacts machine learning, natural language processing, and other artificial intelligence approaches. It identifies challenges that uncertainty presents and strategies for mitigating uncertainty in big data analytics.
Data science uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. It unifies statistics, data analysis, machine learning and related methods. Data science is important for business as it can turn ideas from science fiction into reality and help make predictions and decisions using predictive analytics, machine learning and analyzing vast amounts of business data. Data science projects involve tasks like data cleaning, exploratory analysis, visualization, machine learning and communication. Data science education is evolving to produce professionals with skills in computer science, information science, and statistics.
Here are some key points to consider when designing visuals:
- Who is your audience? What information do they need?
- What insights or messages do you want to convey?
- Consider different visualisation types and choose those best suited to your data and goals
- Use visual hierarchy, layout and formatting to guide the eye and message
- Iteratively sketch, test and refine your designs with your intended users
- Balance simplicity and clarity with including all necessary information
The design process is iterative. Start broadly and refine based on testing with intended users. Focus on conveying the most important insights as simply as possible.
Data Science - An emerging Stream of Science with its Spreading Reach & ImpactDr. Sunil Kr. Pandey
This is my presentation on the Topic "Data Science - An emerging Stream of Science with its Spreading Reach & Impact". I have compiled and collected different statistics and data from different sources. This may be useful for students and those who might be interested in this field of Study.
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
The document provides an overview of data science through an introduction by Sreejith C, a data scientist. It defines data science as discovering unknown information from data, obtaining predictive insights, creating impactful data products, and communicating business stories from data. A data scientist's work includes tasks like authoring data processing pipelines, performing analyses, and communicating results. The document also demonstrates a loan prediction problem using machine learning algorithms like logistic regression, decision trees, and random forests in Python.
Adding Open Data Value to 'Closed Data' ProblemsSimon Price
Drawing on cutting edge examples from the University of Bristol and the City of Bristol, Simon will discuss innovative applications of data science that derive business value from open data through enriching and integrating with confidential 'closed data'. He also highlights recent technological advances that are enabling open data science on highly sensitive closed data.
This document provides an overview of machine learning. It begins by defining machine learning as a field of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. The document then discusses where data comes from, different types of data, and what data analytics is. It also explains how machine learning is related to data analytics and describes some common assumptions and architectures of machine learning models. Finally, it gives examples of how machine learning is used and provides an overview of supervised vs. unsupervised learning approaches.
COM 578 Empirical Methods in Machine Learning and Data Miningbutest
This document provides an overview and summary of the COM 578 Empirical Methods in Machine Learning and Data Mining course. It outlines the course topics, grading structure, office hours, homework assignments, final project, and textbooks. Key topics covered in the course include decision trees, k-nearest neighbor, neural networks, support vectors, association rules, clustering, and boosting/bagging. The final project involves applying machine learning techniques to train models on two different datasets.
The document outlines a data science roadmap that covers fundamental concepts, statistics, programming, machine learning, text mining, data visualization, big data, data ingestion, data munging, and tools. It provides the percentage of time that should be spent on each topic, and lists specific techniques in each area, such as linear regression, decision trees, and MapReduce in big data.
This document provides an overview of programming in Python for data science. It discusses Python's history and timeline, its versatile capabilities across different programming paradigms, and its simple and clear syntax. Key features that make Python popular for data science are highlighted, such as its comprehensive standard library and support for numeric, scientific, and GUI programming. The document also compares Python 2 and 3, describes different ways to run Python programs, and lists popular Python packages for data science. Overall, it serves as an introduction to Python for newcomers and outlines its relevance and widespread adoption in the field of data science.
The document discusses the steps involved in the data science life cycle (DSLC). It describes the main steps as business understanding, data acquisition and understanding, modeling, deployment, and customer acceptance. It provides details on several of these steps, including business understanding, data acquisition and understanding, data modeling, and initial data exploration. The goal is to clearly outline the typical process and considerations for a data science project from defining the problem to exploring the available data.
Studying Data Science At University In The United KingdomThomas Lancaster
Data Science as emerging as a valued choice of qualification and subsequent career. The skills of Data Scientists and Data Analysts are in demand, with many jobs looking for the mix of programmatic and analytical skills needed for success in this field. This presentation provides an overview of the types of courses available for people looking to specialise in Data Science and considers how interested parties can best prepare themselves for a career in this area.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
This document provides an introduction to data science. It discusses that data science uses computer science, statistics, machine learning, visualization, and human-computer interaction to collect, clean, analyze, visualize, and interact with data to create data products. It also describes the data science lifecycle as involving discovery, data preparation, model planning, model building, operationalizing models, and communicating results. Finally, it lists some common tools used in data science like Python, R, SQL, and Tableau.
1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management.
2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data.
3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
This document summarizes a seminar on machine learning using big data. It discusses the history of data storage and traditional databases. It then introduces machine learning and the types of learning, including supervised and unsupervised learning. Specific algorithms for each type are covered such as k-means clustering for unsupervised and naive Bayes for supervised. Case studies on applications like Amazon product recommendations are presented. The document concludes by discussing tools for machine learning and future applications as more connected devices generate extensive data.
Session 01 designing and scoping a data science projectbodaceacat
This document provides an overview of the first session in a data science training series. It discusses designing and scoping a data science project. Key points include: defining data science and the data science process; describing the roles of problem owners and competitors; reviewing examples of data science competitions from Kaggle, DrivenData, and DataKind; and providing guidance on writing an effective problem statement by specifying the context, needs, vision, and intended outcomes of a project. The document also briefly covers data science ethics considerations like ensuring privacy and minimizing risks. Exercises are included to help participants practice asking interesting questions, identifying relevant data sources, and designing communications for target audiences.
The document provides an overview of data science applications and use cases. It defines data science as using computer science, statistics, machine learning and other techniques to analyze data and create data products to help businesses make better decisions. It discusses big data challenges, the differences between data science and software engineering, and key areas of data science competence including data analytics, engineering, domain expertise and data management. Finally, it outlines several common data science applications and use cases such as recommender systems, credit scoring, dynamic pricing, customer churn analysis and fraud detection with examples of how each works and real world cases.
Introduction to Computational StatisticsSetia Pramana
This document outlines courses in computational statistics that utilize various statistical software packages like R, SPSS, and Excel. The courses cover topics ranging from data preparation and visualization to statistical modeling techniques like linear regression, resampling methods, and hypothesis testing. They emphasize hands-on practice over theory, involve group projects, and provide reference materials for further learning.
This document discusses uncertainty in big data analytics. It begins by providing background on big data, defining the common "5 V's" characteristics of big data - volume, variety, velocity, veracity, and value. It then discusses uncertainty, which exists in big data due to noise, incompleteness, and inconsistency in data. The document surveys techniques for big data analytics and how uncertainty impacts machine learning, natural language processing, and other artificial intelligence approaches. It identifies challenges that uncertainty presents and strategies for mitigating uncertainty in big data analytics.
Data science uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. It unifies statistics, data analysis, machine learning and related methods. Data science is important for business as it can turn ideas from science fiction into reality and help make predictions and decisions using predictive analytics, machine learning and analyzing vast amounts of business data. Data science projects involve tasks like data cleaning, exploratory analysis, visualization, machine learning and communication. Data science education is evolving to produce professionals with skills in computer science, information science, and statistics.
Here are some key points to consider when designing visuals:
- Who is your audience? What information do they need?
- What insights or messages do you want to convey?
- Consider different visualisation types and choose those best suited to your data and goals
- Use visual hierarchy, layout and formatting to guide the eye and message
- Iteratively sketch, test and refine your designs with your intended users
- Balance simplicity and clarity with including all necessary information
The design process is iterative. Start broadly and refine based on testing with intended users. Focus on conveying the most important insights as simply as possible.
Data Science - An emerging Stream of Science with its Spreading Reach & ImpactDr. Sunil Kr. Pandey
This is my presentation on the Topic "Data Science - An emerging Stream of Science with its Spreading Reach & Impact". I have compiled and collected different statistics and data from different sources. This may be useful for students and those who might be interested in this field of Study.
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
A New Paradigm on Analytic-Driven Information and Automation V2.pdfArmyTrilidiaDevegaSK
The document proposes an end-to-end methodology for developing analytic-driven information and automation systems based on big data, data science, and artificial intelligence. The methodology involves 6 steps: 1) collecting data from multiple sources, 2) preprocessing the data, 3) extracting features from the data, 4) clustering and interpreting the data, 5) designing applications, and 6) implementing and evaluating the systems. It then provides an example of applying this methodology to develop an early warning system for monitoring higher education institutions in Indonesia. The system would collect data from various sources, analyze it using machine learning techniques, predict and prescribe interventions for student groups.
An expanding and expansive view of computing researchNAVER Engineering
My recent service for five years as the Assistant Director of the US National Science Foundation leading the Directorate of Computer and Information Science and Engineering has afforded me a broad view of computing research and education. The field of computing is in the midst of another “golden age” and is also at another nexus point – a point of change – where future research directions, and new ways in which research will be done, are coming into focus.
In this talk we will discuss these current and future CS research topics and trends, placing them in the context of the longer-term evolution of our field. We will also discuss computer science education (at several levels), as well as the forces that promise to disrupt not just computer science education, but higher education more broadly.
This document provides an overview of a presentation on advanced analytics, big data, and being a data scientist. The presentation agenda includes an introduction to data science, why the presenter became a data scientist, definitions of data science, data science skillsets, the data science process for one-off projects versus production pipelines, various data science tools, and a question and answer section. The document outlines each section in detail with examples.
Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from data. It unifies statistics, data analysis, machine learning and related methods. Data science is the future of artificial intelligence and can add value to businesses by turning ideas seen in movies into reality. It involves working with large data sets and machine learning. Data science is primarily used for decisions, predictions, and machine learning by uncovering findings from data. Data science and technology delivers methods for solving data-intensive problems ranging from research to software deployment. Feature engineering is selecting or generating useful columns for modeling. Data cleaning takes up most of a data scientist's time along with exploratory analysis, visualization, machine learning, and communication. Data science education
Real-time applications of Data Science.pptxshalini s
This document provides an overview of data science through discussing big data challenges, defining data science, contrasting it with other fields, and presenting case studies. It explains that data science uses theories from fields like computer science, mathematics, and statistics to analyze large, complex data sets and help organizations make better decisions. Example applications discussed include using data science in healthcare to improve patient care, in elections to micro-target voters, and in cities to address urban challenges through data-driven solutions.
Big Data and Computer Science EducationJames Hendler
- The document discusses the Rensselaer Institute for Data Exploration and Applications (IDEA) and its work in applying data science across various domains like healthcare, business, and the sciences.
- It outlines graduate projects in IDEA that involve collaborations with other Rensselaer research centers and applying data exploration tools.
- It also discusses changes made to Rensselaer's computer science and information technology curriculum to incorporate more training in data analytics, data science challenges, and working with large, unstructured datasets. This includes new concentrations in data science and information dominance.
Data Science and AI in Biomedicine: The World has ChangedPhilip Bourne
This document discusses the changing landscape of data science and AI in biomedicine. Some key points:
- We are at a tipping point where data science is becoming a driver of biomedical research rather than just a tool. Biomedical researchers need to become data scientists.
- Data science is interdisciplinary and touches every field due to the rise of digital data. It requires openness, translation of findings, and consideration of responsibilities like algorithmic bias.
- Advances like AlphaFold2 show the power of large collaborative efforts combining data, computing resources, engineering, and domain expertise. This points to the need for public-private partnerships and new models of open data sharing.
- The definition of
The document provides an overview of the data mining concepts and techniques course offered at the University of Illinois at Urbana-Champaign. It discusses the motivation for data mining due to abundant data collection and the need for knowledge discovery. It also describes common data mining functionalities like classification, clustering, association rule mining and the most popular algorithms used.
Data science involves using industrial research techniques on a company's own data to develop advanced algorithms that provide a competitive advantage. Data engineering is a specialized form of software engineering focused on handling and processing data using skills in areas like structured and unstructured data storage, machine learning platforms, and predictive APIs. While data science and business intelligence overlap in using data analysis, statistics, and visualization, data science has a more scientific approach focused on the future rather than the past. Data-focused jobs are in high demand across many industries, especially technology, but some roles may become automated, increasing the value of skills like research and communication. Education options for these fields include academic programs, boot camps, and online classes.
The Insight Data Science Fellows Program is a 6-week postdoctoral training fellowship that teaches scientists industry skills in data science. The program is held in Silicon Valley and New York City and bridges the gap between academia and careers in data science. Fellows learn tools and techniques from mentors at companies and work on projects to gain skills and interview at mentor companies for jobs in data science.
Students will be able to work as
research assistants in academia and industry.
Entrepreneurship: Students can start their own
data science consulting firms or startups.
Higher Education: Students will be well
prepared for advanced degrees in Data Science,
Computer Science, Statistics or related fields.
Data Science in 2016: Moving up by Paco Nathan at Big Data Spain 2015Big Data Spain
This document discusses trends in data science in 2016, including how data science is moving into new use cases such as medicine, politics, government, and neuroscience. It also covers trends in hardware, generalized libraries, leveraging workflows, and frameworks that could enable a big leap ahead. The document discusses learning trends like MOOCs, inverted classrooms, collaborative learning, and how O'Reilly Media is embracing Jupyter notebooks. It also covers measuring distance between learners and subject communities, and the importance of both people and automation working together.
intro to data science Clustering and visualization of data science subfields ...jybufgofasfbkpoovh
This document provides an introduction to the field of data science. It defines data science as an interdisciplinary field that uses scientific methods and processes to extract knowledge and insights from large amounts of structured and unstructured data. The document discusses what data science is, why it has grown in importance recently due to massive data collection and computing power, and what skills and roles are involved in data science work. It also presents models of the data science process and team composition.
Una breve introduzione alla data science e al machine learning con un'enfasi sugli scenari applicativi, da quelli tradizionali a quelli più innovativi. La overview copre la definizione di base di data science, una overview del machine learning e esempi su scenari tradizionali, Recommender systems e Social Network Analysis, IoT e Deep Learning
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
5. Shopping:
How does Amazon
forecast how many
items it needs to store
in its warehouses?
From www.formaspace.com
From cdn.wonderfulengineering.com (top), formaspace.com (bottom) and linkedin.com (right)
6. From cimss.ssec.wisc.edu, ipcc.ch, and www.spot-7.com
Climate: How does NASA automatically detect
land changes using satellite image data?
7. Medicine: How can genomics help to
personalize medical recommendations?
Data Matrix:
Rows = genes, Columns = patients
From www.originlab.com
8. Physics: How do you write software to
search for new physics particles?
Large Hadron Collider:
700 Mbytes/second
60 Terabytes/day
20 Petabytes/year
10. Social media: How does
Facebook recognize
people in images?
From Le Cun and Ranzato 2013
11. How?
• All of these applications use Data Science
• These applications are built on
combinations of ideas from:
o Database systems
o Algorithms
o Machine learning
o Probabilistic models
o Statistical forecasting
o Data visualization
o and more…
14. Is the Data Science Major a
good match for you?
• Are you interested in computing?
– Enjoy working with algorithms, programming, machine learning,…
• Do you have a good mathematics background?
– Comfortable with mathematical ideas and concepts?
– Interested in applying mathematical ideas to real-world problems?
• Enthusiastic about analyzing data?
– Enjoy working with data? exploring, visualizing, modeling, understanding
• Seeking a career that has broad and flexible options?
If your answers are YES,
the Data Science Major is for you!
15. (Sample electives
shown in parentheses)
Statistics
Stats 120 ABC: Intro to Prob and Stats
Stats 68: Exploratory Data Analysis
Stats 110-112: Statistical Methods
CS 178: Machine Learning
(Stats 140: Multivariate Statistics)
Computing
ICS 46: Data Structures
IFMTX 43: Intro to Software Engineering
CS 122A: Intro to Data Management
CS 161: Design and Analysis of Algorithms
(CS 131: Parallel and Distributed Computing)
(CS 172: Neural Networks/Deep Learning)
Applications
Stats 170AB: Data Science Capstone Project
INF 143: Information Visualization
(INF 131: Human Computer Interaction)
(CS 121: Information Retrieval)
(CS 122B: Project in Databases/Web
Applications)
(Summer intermships, e.g., junior year)
What classes might I
take in the DS Major?
16. Years 1 and 2 focus on foundational courses in computer science,
mathematics, statistics, including statistical computing
Sample Course of Study in the Major
Fall (12 units) Winter (13 units) Spring (16 units)
ICS 31, Social Analysis of
Computerization (4 units)
Math 2A, Calculus I (4 units)
Writing 39A, Writing and
Rhetoric (4 units)
ICS 32, (4 units)
Math 2B, Calculus II (4 units)
Writing 39B, Critical Reading
and Rhetoric (4 units)
Stats 5, Seminar in DS (1 unit)
ICS 33, Intermediate
Programming (4 units)
Math 2D, Multivariable
Calculus (4 units)
Stats 7, Basic Statistics (4
units)
Writing 39C, Argument and
Research (4 units)
Year 1 Sample Program:
17. Years 1 and 2 focus on foundational courses in computer science,
mathematics, statistics, including statistical computing
Sample Course of Study in the Major
Fall (16 units) Winter (14 units) Spring (16 units)
ICS 6B, Boolean Algebra and
Logic (4 units)
Math 3A, Intro to Linear
Algebra (4 units)
Stats 120A, Intro to Probability
and Statistics I (4 units)
GE III, (4 units)
ICS 45C, C/C++ (4 units)
ICS 51, Intro to Computer
Organization (6 units)
Stats 120B, Intro to Probability
and Statistics II (4 units)
Stats 68, Stat Computing and
Exploratory DA (4 units)
Stats 120C, Intro to Probability
and Statistics III (4 units)
ICS 46, Data Structures (4
units)
ICS 6D, Discrete Mathematics
(4 units)
Year 2 Sample Program:
18. Fall (16 units) Winter (16 units) Spring (16 units)
Stats 110, Statistical Methods for
Data Analysis I (4 units)
CS 161, Design and Analysis of
Algorithms (4 units)
In4matx 43, Introduction to
Software Engineering (4 units)
GE IV/VIII (4 units)
Stats 111, Statistical Methods
for Data Analysis II (4 units)
CS 178, Machine Learning and
Data-Mining (4 units)
ICS 139W, Critical Writing on
Information Technology (4
units)
GE III/VII (4 units)
Stats 112, Statistical Methods
for Data Analysis III (4 units)
CS 122A, Introduction to Data
Management (4 units)
In4matx 143, Information
Visualization (4 units)
GE VI (4 units)
Years 3 and 4 include more emphasis and specialization in data science topics
such as machine learning, databases, visualization, advanced statistics
Year 4: Two-quarter capstone “data-intensive” project, + statistics and CS electives
Sample Course of Study in the Major
Year 3 Sample Program:
20. I’m a current UCI student. What are the
change of major requirements for Data
Science?
• Cumulative UC GPA: 2.7 or higher.
• 3.0 or higher average GPA and no grade lower than a C for ICS 31, ICS 32, and one
of the following: Math 2A, Math 2B, Math 2D, ICS 6B, or ICS 6D.
• Students with more than 60 units will be reviewed on a case-by-case basis and may
not be admitted to the major.
• Students will not be able to complete the degree in Data Science prior to Spring
2018.
If you are a freshman, contact Neha Rawal (neha@ics.uci.edu) to inquire
about getting a waiver to change into the major
More generally, you can talk to ICS Student Affairs Office Counselor if you
are interested in changing your major to Data Science
21. What can I do with a
Data Science Major?
• Careers in “Data-Oriented” Companies and Organizations
– Computing/internet companies: Google, Amazon, Facebook, IBM,….
– Engineering companies: Intel, Samsung, Boeing, ….
– Finance/insurance companies
– Medical/pharmaceutical companies
– Government/national labs: NASA, NIST, DoD, ….
– Many many more……
• Option to specialize with a Graduate Degrees (MS or PhD)
– Computer Science: specialize in a topic such as machine learning,
databases, etc
– Statistics: specialize in a statistical topic, e.g., computational statistics
– MS/PhD degrees lead to a wide variety of careers
22. Are there jobs for Data Scientists?
“The United States alone faces a shortage of 140,000 to 190,000 people with deep analytical
skills as well as 1.5 million managers and analysts to analyze big data and make decisions
based on their findings. The shortage of talent is just beginning.”
(McKinsey Global Institute Study on Big Data, 2011)
23. Are there (currently) jobs for Data
Scientists?
Glassdoor.com currently ranks Data Scientist as the #1 job in a America, based on
number of job openings (1,736), median base salary ($116,840), and career
opportunity.
Source (August 21, 2016) : https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm
24. Do I need a Data Science
degree to do Data Science?
• Technically no……many people currently are “data scientists” with
backgrounds in quantitative degrees that are not data science
– Some with statistics, some with computer science, some with a
combination
– Some with other quantitative degrees
• Advantages of the DS major
– Puts you on the “fast track” to becoming a Data Scientist
– Ensures that you will know the fundamentals of both
• Computing
• Statistics
– Provides you with skills that are likely to have lasting value (as
technology changes)
25. What are other degree options?
• Computer Science with a Statistics minor?
– More classes in “systems” aspects of computer science
– Fewer classes in statistics
– No capstone data science project class
• Another degree like Math or Economics with a Statistics minor?
– Far fewer classes in computer science
– Fewer classes in statistics
– No capstone data science project class
• Statistics undergraduate degree (e.g., at another UC)?
– More classes in mathematics and statistics
– Far fewer classes in computer science
– No capstone data science project class
26. Want to learn more?
Visit us online!
For additional information on the Data
science major at UCI, please visit:
http://www.stat.uci.edu/ugrad/datascience.php
For additional information on applying
to UCI, please visit:
http://admissions.uci.edu/