Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government.
제공자:
런던 대학교
골드스미스 대학교
Foundations of Data Science: K-Means Clustering in Python
런던 대학교이 강좌에 대하여
최근 조회 75,766회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계
You will need mathematical and statistical knowledge and skills at least at high-school level.
완료하는 데 약 29시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 중국어 (간체자), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 터키어, 영어, 스페인어
배울 내용
Define and explain the key concepts of data clustering
Demonstrate understanding of the key constructs and features of the Python language.
Implement in Python the principle steps of the K-means algorithm.
Design and execute a whole data clustering workflow and interpret the outputs.
귀하가 습득할 기술
K-Means ClusteringMachine LearningProgramming in Python
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계
You will need mathematical and statistical knowledge and skills at least at high-school level.
완료하는 데 약 29시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 중국어 (간체자), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 터키어, 영어, 스페인어
제공자:
런던 대학교
The University of London is a federal University which includes 18 world leading Colleges. Our distance learning programmes were founded in 1858 and have enriched the lives of thousands of students, delivering high quality University of London degrees wherever our students are across the globe. Our alumni include 7 Nobel Prize winners. Today, we are a global leader in distance and flexible study, offering degree programmes to over 50,000 students in over 180 countries. To find out more about studying for one of our degrees where you are, visit www.london.ac.uk
골드스미스 대학교
Championing research-rich degrees that provoke thought, stretch the imagination and tap into tomorrow’s world, at Goldsmiths we’re asking the questions that matter now in subjects as diverse as the arts and humanities, social sciences, cultural studies, computing, and entrepreneurial business and management. We are a community defined by its people: innovative in spirit, analytical in approach and open to all.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 7시간 필요
Week 1: Foundations of Data Science: K-Means Clustering in Python
This week we will introduce you to the course and to the team who will be guiding you through the course over the next 5 weeks. The aim of this week's material is to gently introduce you to Data Science through some real-world examples of where Data Science is used, and also by highlighting some of the main concepts involved.
완료하는 데 7시간 필요
9개 동영상 (총 22분)
9개의 동영상
Introduction to Data Science2m
What is Data?1m
Types of Data1m
Machine Learning3m
Supervised vs Unsupervised Learning2m
K-Means Clustering4m
Preparing your Data1m
A Real World Dataset53
4개 연습문제
Types of Data – Review Information15m
Supervised vs Unsupervised – Review Information15m
K-Means Clustering – Review Information30m
Week 1 Summative Assessment40m
완료하는 데 4시간 필요
Week 2: Means and Deviations in Mathematics and Python
완료하는 데 4시간 필요
11개 동영상 (총 37분), 4 개의 읽기 자료, 11 개의 테스트
11개의 동영상
2.1 – Introduction to Mathematical Concepts of Data Clustering1m
2.2 – Mean of One Dimensional Lists2m
2.3 – Variance and Standard Deviation3m
2.4 Jupyter Notebooks6m
2.5 Variables4m
2.6 Lists4m
2.7 Computing the Mean3m
2.8 Better Lists: NumPy3m
2.9 Computing the Standard Deviation6m
Week 2 Conclusion31
4개의 읽기 자료
Population vs Sample, Bias10m
Variability, Standard Deviation and Bias10m
Python Style Guide10m
Numpy and Array Creation20m
10개 연습문제
Population vs Sample – Review Information5m
Mean of One Dimensional Lists – Review Information3m
Variance and Standard Deviation – Review Information4m
Jupyter Notebooks – Review Information20m
Variables – Review Information10m
Lists – Review Information10m
Computing the Mean – Review Information10m
Better Lists – Review Information10m
Computing the Standard Deviation – Review Information10m
Week 2 Summative Assessment40m
완료하는 데 8시간 필요
Week 3: Moving from One to Two Dimensional Data
완료하는 데 8시간 필요
16개 동영상 (총 53분), 10 개의 읽기 자료, 15 개의 테스트
16개의 동영상
3.1 Multidimensional Data Points and Features2m
3.2 Multidimensional Mean2m
3.3 Dispersion: Multidimensional Variables3m
3.4 Distance Metrics5m
3.5 Normalisation1m
3.6 Outliers1m
3.7 Basic Plotting2m
3.7a Storing 2D Coordinates in a Single Data Structure6m
3.8 Multidimensional Mean4m
3.9 Adding Graphical Overlays5m
3.10 Calculating the Distance to the Mean3m
3.11 List Comprehension3m
3.12 Normalisation in Python5m
3.13 Outliers and Plotting Normalised Data2m
Week 3 Conclusion30
10개의 읽기 자료
Multidimensional Data Points and Features Recap10m
Multidimensional Mean Recap10m
Multidimensional Variables Recap10m
Distance Metrics Recap10m
Normalisation Recap10m
Note on Matplotlib10m
Matplotlib Scatter Plot Documentation20m
Matplotlib Patches Documentation10m
List Comprehension Documentation20m
3.12 Errata10m
15개 연습문제
Multidimensional Data Points and Features – Review Information3m
Multidimensional Mean – Review Information3m
Dispersion: Multidimensional Variables – Review Information5m
Distance Metrics – Review Information6m
Normalisation – Review Information3m
Outliers – Review Information30m
Basic Plotting – Review Information5m
Storing 2D Coordinates – Review Information30m
Multidimensional Mean – Review Information30m
Adding Graphical Overlays – Review Information30m
Calculating Distance – Review Information30m
List Comprehension – Review Information30m
Normalisation in Python – Review Information30m
Outliers – Review Information30m
Week 3 Summative Assessment25m
완료하는 데 4시간 필요
Week 4: Introducing Pandas and Using K-Means to Analyse Data
완료하는 데 4시간 필요
8개 동영상 (총 37분), 6 개의 읽기 자료, 8 개의 테스트
8개의 동영상
4.1: Using the Pandas Library to Read csv Files5m
4.1a: Sorting and Filtering Data Using Pandas8m
4.1b: Labelling Points on a Graph4m
4.1c: Labelling all the Points on a Graph3m
4.2: Eyeballing the Data5m
4.3: Using K-Means to Interpret the Data8m
Week 4: Conclusion35
6개의 읽기 자료
Week 4 Code Resources5m
Pandas Read_CSV Function15m
More Pandas Library Documentation10m
The Pyplot Text Function10m
For Loops in Python10m
Documentation for sklearn.cluster.KMeans10m
7개 연습문제
Using the Pandas Library to Read csv Files – Review Information5m
Sorting and Filtering Data Using Pandas – Review Information10m
Labelling Points on a Graph – Review Information5m
Labelling all the Points on a Graph – Review Information5m
Eyeballing the Data – Review Information5m
Using K-Means to Interpret the Data – Review Information5m
Week 4 Summative Assessment40m
검토
FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON의 최상위 리뷰
AH 제공2020년 6월 3일
I love this course as it gives me the foundations of learning the Python coding program and relevant statistical methods that used for data analysis. It's really interesting course to attend to.
GR 제공2019년 9월 9일
184/5000 Conferences of very good quality, and the platform for practices is really useful to put the theory into practice. I recommend this course if you want to start in data science.
SS 제공2020년 6월 29일
A well presented and interesting course. It would have been good to have some more complex examples with the thinking behind them - the exploratory bit/intelligent bit of the process.
BO 제공2020년 7월 3일
Highly recommended to anyone who wants to delve into data science. The instruuctors, the universities and Coursera team are well dedicated and the course is of high quality.
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