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. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks. You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset.
課程信息
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您將學到的內容有
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.
您將獲得的技能
K-Means ClusteringMachine LearningProgramming in Python
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 7 小時
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.
9 個視頻 （總計 22 分鐘）, 4 個測驗
9 個視頻
Introduction to Data Science2分鐘
What is Data?1分鐘
Types of Data1分鐘
Machine Learning3分鐘
Supervised vs Unsupervised Learning2分鐘
K-Means Clustering4分鐘
Preparing your Data1分鐘
A Real World Dataset53
4 個練習
Types of Data – Review Information15分鐘
Supervised vs Unsupervised – Review Information15分鐘
K-Means Clustering – Review Information30分鐘
Week 1 Summative Assessment40分鐘
週
2完成時間為 4 小時
Week 2: Means and Deviations in Mathematics and Python
11 個視頻 （總計 37 分鐘）, 4 個閱讀材料, 11 個測驗
11 個視頻
2.1 – Introduction to Mathematical Concepts of Data Clustering1分鐘
2.2 – Mean of One Dimensional Lists2分鐘
2.3 – Variance and Standard Deviation3分鐘
2.4 Jupyter Notebooks6分鐘
2.5 Variables4分鐘
2.6 Lists4分鐘
2.7 Computing the Mean3分鐘
2.8 Better Lists: NumPy3分鐘
2.9 Computing the Standard Deviation6分鐘
Week 2 Conclusion31
4 個閱讀材料
Population vs Sample, Bias10分鐘
Variability, Standard Deviation and Bias10分鐘
Python Style Guide10分鐘
Numpy and Array Creation20分鐘
10 個練習
Population vs Sample – Review Information5分鐘
Mean of One Dimensional Lists – Review Information3分鐘
Variance and Standard Deviation – Review Information4分鐘
Jupyter Notebooks – Review Information20分鐘
Variables – Review Information10分鐘
Lists – Review Information10分鐘
Computing the Mean – Review Information10分鐘
Better Lists – Review Information10分鐘
Computing the Standard Deviation – Review Information10分鐘
Week 2 Summative Assessment40分鐘
週
3完成時間為 4 小時
Week 3: Moving from One to Two Dimensional Data
16 個視頻 （總計 53 分鐘）, 10 個閱讀材料, 15 個測驗
16 個視頻
3.1 Multidimensional Data Points and Features2分鐘
3.2 Multidimensional Mean2分鐘
3.3 Dispersion: Multidimensional Variables3分鐘
3.4 Distance Metrics5分鐘
3.5 Normalisation1分鐘
3.6 Outliers1分鐘
3.7 Basic Plotting2分鐘
3.7a Storing 2D Coordinates in a Single Data Structure6分鐘
3.8 Multidimensional Mean4分鐘
3.9 Adding Graphical Overlays5分鐘
3.10 Calculating the Distance to the Mean3分鐘
3.11 List Comprehension3分鐘
3.12 Normalisation in Python5分鐘
3.13 Outliers and Plotting Normalised Data2分鐘
Week 3 Conclusion30
10 個閱讀材料
Multidimensional Data Points and Features Recap10分鐘
Multidimensional Mean Recap10分鐘
Multidimensional Variables Recap10分鐘
Distance Metrics Recap10分鐘
Normalisation Recap10分鐘
Note on Matplotlib10分鐘
Matplotlib Scatter Plot Documentation20分鐘
Matplotlib Patches Documentation10分鐘
List Comprehension Documentation20分鐘
3.12 Errata10分鐘
15 個練習
Multidimensional Data Points and Features – Review Information3分鐘
Multidimensional Mean – Review Information3分鐘
Dispersion: Multidimensional Variables – Review Information5分鐘
Distance Metrics – Review Information6分鐘
Normalisation – Review Information3分鐘
Outliers – Review Information4分鐘
Basic Plotting – Review Information5分鐘
Storing 2D Coordinates – Review Information4分鐘
Multidimensional Mean – Review Information4分鐘
Adding Graphical Overlays – Review Information6分鐘
Calculating Distance – Review Information6分鐘
List Comprehension – Review Information4分鐘
Normalisation in Python – Review Information4分鐘
Outliers – Review Information2分鐘
Week 3 Summative Assessment25分鐘
週
4完成時間為 5 小時
Week 4: Introducing Pandas and Using K-Means to Analyse Data
8 個視頻 （總計 37 分鐘）, 6 個閱讀材料, 8 個測驗
8 個視頻
4.1: Using the Pandas Library to Read csv Files5分鐘
4.1a: Sorting and Filtering Data Using Pandas8分鐘
4.1b: Labelling Points on a Graph4分鐘
4.1c: Labelling all the Points on a Graph3分鐘
4.2: Eyeballing the Data5分鐘
4.3: Using K-Means to Interpret the Data8分鐘
Week 4: Conclusion35
6 個閱讀材料
Week 4 Code Resources5分鐘
Pandas Read_CSV Function15分鐘
More Pandas Library Documentation10分鐘
The Pyplot Text Function10分鐘
For Loops in Python10分鐘
Documentation for sklearn.cluster.KMeans10分鐘
7 個練習
Using the Pandas Library to Read csv Files – Review Information5分鐘
Sorting and Filtering Data Using Pandas – Review Information10分鐘
Labelling Points on a Graph – Review Information5分鐘
Labelling all the Points on a Graph – Review Information5分鐘
Eyeballing the Data – Review Information5分鐘
Using K-Means to Interpret the Data – Review Information5分鐘
Week 4 Summative Assessment40分鐘
4.7
7 個審閱來自Foundations of Data Science: K-Means Clustering in Python的熱門評論
184/5000\n\nConferences 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.
This course is at right level for a beginner (python and analytics) while going into details around K means clustering
講師
Dr Matthew Yee-King
LecturerComputing Department, Goldsmiths, University of London
Dr Betty Fyn-Sydney
Lecturer in MathematicsDepartment of Computing, Goldsmiths, University of London
Dr Jamie A Ward
Lecturer in Computer ScienceDepartment of Computing, Goldsmiths, University of London
Dr Larisa Soldatova
Reader in Data ScienceDepartment of Computing, Goldsmiths, University of London
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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
關於 伦敦大学金匠学院
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.
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