The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.
제공자:
EIT 디지털
밀라노 국립건축대학
Data Science for Business Innovation
EIT 디지털
About this Course
최근 조회 6,247회
배울 내용
What is data science
How data science, machine learning, and data-driven innovation can benefit business outcomes
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 8시간 필요
권장: 4 weeks of study, 1.5 hours/week
영어
자막: 영어
제공자:
EIT 디지털
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.
밀라노 국립건축대학
Politecnico di Milano is a scientific-technological University, which trains engineers, architects and industrial designers.
강의 계획 - 이 강좌에서 배울 내용
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.
완료하는 데 1시간 필요
3개 동영상 (총 13분), 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
Big Data and Data Science10m
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.
완료하는 데 2시간 필요
3개 동영상 (총 16분), 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 Learning30m
완료하는 데 3시간 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.
완료하는 데 3시간 필요
5개 동영상 (총 25분), 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 Regression30m
Naive Bayes30m
Decision Trees and Random Forests30m
Logistic Regression30m
K-means Clustering6m
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.
완료하는 데 1시간 필요
2개 동영상 (총 12분), 1 reading, 1 quiz
1개의 읽기 자료
Data Science Challenges20m
1개 연습문제
Data Science Challenges30m
검토
3.9
DATA SCIENCE FOR BUSINESS INNOVATION의 최상위 리뷰
GS 제공Jan 19, 2020
A very well done course that showcases the main technologies applied in different scenarios. It was a good introduction to the world of applied data science.
TG 제공Dec 14, 2019
There are sometimes language interpretations that make it really hard to pass the quizes.
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