About this Course
최근 조회 2,931

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

완료하는 데 약 17시간 필요

권장: 4 weeks of study, 1.5 hours/week...

영어

자막: 영어

배울 내용

  • Check

    What is data science

  • Check

    How data science, machine learning, and data-driven innovation can benefit business outcomes

  • Check

    Foundational concepts and intuitions about machine learning techniques

귀하가 습득할 기술

Data ScienceBusiness AnalyticsDecision-MakingData AnalysisBig Data

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

완료하는 데 약 17시간 필요

권장: 4 weeks of study, 1.5 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 1시간 필요

Introduction to Data-driven Business

This module introduces the course and offers some basic overview of the topics. It presents the crucial concepts related to data science and big data and provides an outlook on how to use them in real world settings for increasing business value.

...
3 videos (Total 13 min), 2 readings, 2 quizzes
3개의 동영상
Data-driven Decision Making for Data-centric Organizations2m
What's Big Data? How Does It Relate to Data Science?6m
2개의 읽기 자료
Data-driven Decision Making for Data-centric Organizations15m
What's Big Data? How Does It Relate to Data Science?15m
2개 연습문제
Data-driven Decision Making for Data-centric Organizations3m
What's Big Data? How Does It Relate to Data Science?10m
2
완료하는 데 2시간 필요

Terminology and Foundational Concepts

In this module, you will learn the foundational concepts of machine learning and data science. You will understand how these techniques can be useful in terms of increased business value for organizations, thanks to the discussion of a very well known success story, namely Netflix, which can be deemed as a completely data-driven business. You will also understand how machine learning is different from programming.

...
3 videos (Total 16 min), 3 readings, 3 quizzes
3개의 동영상
Machine Learning9m
Solving Problems: Programming vs. Machine Learning3m
3개의 읽기 자료
Success Story: Data Science at Netflix30m
Machine Learning slides20m
Solving Problems: Programming vs. Machine Learning10m
3개 연습문제
Success Story: Data Science at Netflix5m
Machine Learning20m
Solving Problems: Programming vs. Machine Learning2m
3
완료하는 데 2시간 필요

Data Science Methods for Business

In this module, you will learn the concepts and intuitions about the basic approaches for data analysis, including linear regression, naive Bayes, decision trees, clustering, and logistic regression. All the methods are presented starting from typical business uses and are covered in an intuitive way through a guided explanation of how the approach works on simple examples.

...
5 videos (Total 25 min), 5 readings, 5 quizzes
5개의 동영상
Classification of User-generated Content to Recommend Restaurants2m
Product Recommendation Using Decision Trees and Random Forests2m
Hiring Employees Using Logistic Regression8m
K-means Clustering6m
5개의 읽기 자료
Linear Regression for Product Price Prediction10m
Classification User-generated Content to Recommend Restaurants10m
Product Recommendation Using Decision Trees and Random Forests10m
Hiring Employees Using Logistic Regression10m
Using k-means for Clustering10m
5개 연습문제
Linear Regression6m
Analysing User-generated Content to Recommend Restaurants4m
Learning to Recommend Products Using Decision Trees and Random Forests2m
Learning to Hire Fresh Graduates Using Logistic Regression6m
K-means Clustering6m
4
완료하는 데 1시간 필요

Challenges and Conclusions

This module summarizes the concepts learned so far and introduces a set of challenges and risks that data-savvy managers must take into account when deciding for a data-driven strategy.

...
2 videos (Total 12 min), 1 reading, 1 quiz
2개의 동영상
Conclusions1m
1개의 읽기 자료
Data Science Challenges20m
1개 연습문제
Data Science Challenges2m

강사

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Enrique Barra

PhD
Departamento de Ingeniería Telemática
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Marco Brambilla

Associate Professor
Politecnico di Milano
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Fabian Garcia Pastor

Head of Professional School
EIT Digital

EIT 디지털 정보

EIT Digital is a pan-European organization whose mission is to foster digital technology innovation and entrepreneurial talent for economic growth and quality of life. By linking education, research and business, EIT Digital empowers digital top talents for the future. EIT Digital provides online and blended Innovation and Entrepreneurship education to raise quality, increase diversity and availability of the top-level content provided by 20 leading technical universities around Europe. The universities deliver a unique blend of the best of technical excellence and entrepreneurial skills and mindset to digital engineers and entrepreneurs at all stages of their careers. The academic partners support Coursera’s bold vision to enable anyone, anywhere, to transform their lives by accessing the world’s best learning experience. This means that EIT Digital gradually shares parts of its entrepreneurial and academic education programmes to demonstrate its excellence and make it accessible to a much wider audience. EIT Digital’s online education portfolio can be used as part of blended education settings, in both Master and Doctorate programmes, and for professionals as a way to update their knowledge. EIT Digital offers an online programme in 'Internet of Things through Embedded Systems'. Achieving all certificates of the online courses and the specialization provides an opportunity to enroll in the on campus program and get a double degree. Please visit https://www.eitdigital.eu/eit-digital-academy/ ...

밀라노 국립건축대학 정보

Politecnico di Milano is a scientific-technological University, which trains engineers, architects and industrial designers. From 2014 Politecnico di Milano started the release of several MOOCs, developed by the service for digital learning METID (Methods and Innovative Technologies for Learning), giving everybody the chance to enhance personal skills....

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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