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.
课程信息
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您将学到的内容有
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.
您将获得的技能
K-Means ClusteringMachine LearningProgramming in Python
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 7 小时
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 个视频 （总计 22 分钟）, 4 个测验
9 个视频
Introduction to Data Science2分钟
What is Data?1分钟
Types of Data1分钟
Machine Learning3分钟
Supervised vs Unsupervised Learning2分钟
K-Means Clustering4分钟
Preparing your Data1分钟
A Real World Dataset53
4 个练习
Types of Data – Review Information15分钟
Supervised vs Unsupervised – Review Information15分钟
K-Means Clustering – Review Information30分钟
Week 1 Summative Assessment40分钟
周
2完成时间为 4 小时
Week 2: Means and Deviations in Mathematics and Python
11 个视频 （总计 37 分钟）, 4 个阅读材料, 11 个测验
11 个视频
2.1 – Introduction to Mathematical Concepts of Data Clustering1分钟
2.2 – Mean of One Dimensional Lists2分钟
2.3 – Variance and Standard Deviation3分钟
2.4 Jupyter Notebooks6分钟
2.5 Variables4分钟
2.6 Lists4分钟
2.7 Computing the Mean3分钟
2.8 Better Lists: NumPy3分钟
2.9 Computing the Standard Deviation6分钟
Week 2 Conclusion31
4 个阅读材料
Population vs Sample, Bias10分钟
Variability, Standard Deviation and Bias10分钟
Python Style Guide10分钟
Numpy and Array Creation20分钟
10 个练习
Population vs Sample – Review Information5分钟
Mean of One Dimensional Lists – Review Information3分钟
Variance and Standard Deviation – Review Information4分钟
Jupyter Notebooks – Review Information20分钟
Variables – Review Information10分钟
Lists – Review Information10分钟
Computing the Mean – Review Information10分钟
Better Lists – Review Information10分钟
Computing the Standard Deviation – Review Information10分钟
Week 2 Summative Assessment40分钟
周
3完成时间为 4 小时
Week 3: Moving from One to Two Dimensional Data
16 个视频 （总计 53 分钟）, 10 个阅读材料, 15 个测验
16 个视频
3.1 Multidimensional Data Points and Features2分钟
3.2 Multidimensional Mean2分钟
3.3 Dispersion: Multidimensional Variables3分钟
3.4 Distance Metrics5分钟
3.5 Normalisation1分钟
3.6 Outliers1分钟
3.7 Basic Plotting2分钟
3.7a Storing 2D Coordinates in a Single Data Structure6分钟
3.8 Multidimensional Mean4分钟
3.9 Adding Graphical Overlays5分钟
3.10 Calculating the Distance to the Mean3分钟
3.11 List Comprehension3分钟
3.12 Normalisation in Python5分钟
3.13 Outliers and Plotting Normalised Data2分钟
Week 3 Conclusion30
10 个阅读材料
Multidimensional Data Points and Features Recap10分钟
Multidimensional Mean Recap10分钟
Multidimensional Variables Recap10分钟
Distance Metrics Recap10分钟
Normalisation Recap10分钟
Note on Matplotlib10分钟
Matplotlib Scatter Plot Documentation20分钟
Matplotlib Patches Documentation10分钟
List Comprehension Documentation20分钟
3.12 Errata10分钟
15 个练习
Multidimensional Data Points and Features – Review Information3分钟
Multidimensional Mean – Review Information3分钟
Dispersion: Multidimensional Variables – Review Information5分钟
Distance Metrics – Review Information6分钟
Normalisation – Review Information3分钟
Outliers – Review Information4分钟
Basic Plotting – Review Information5分钟
Storing 2D Coordinates – Review Information4分钟
Multidimensional Mean – Review Information4分钟
Adding Graphical Overlays – Review Information6分钟
Calculating Distance – Review Information6分钟
List Comprehension – Review Information4分钟
Normalisation in Python – Review Information4分钟
Outliers – Review Information2分钟
Week 3 Summative Assessment25分钟
周
4完成时间为 5 小时
Week 4: Introducing Pandas and Using K-Means to Analyse Data
8 个视频 （总计 37 分钟）, 6 个阅读材料, 8 个测验
8 个视频
4.1: Using the Pandas Library to Read csv Files5分钟
4.1a: Sorting and Filtering Data Using Pandas8分钟
4.1b: Labelling Points on a Graph4分钟
4.1c: Labelling all the Points on a Graph3分钟
4.2: Eyeballing the Data5分钟
4.3: Using K-Means to Interpret the Data8分钟
Week 4: Conclusion35
6 个阅读材料
Week 4 Code Resources5分钟
Pandas Read_CSV Function15分钟
More Pandas Library Documentation10分钟
The Pyplot Text Function10分钟
For Loops in Python10分钟
Documentation for sklearn.cluster.KMeans10分钟
7 个练习
Using the Pandas Library to Read csv Files – Review Information5分钟
Sorting and Filtering Data Using Pandas – Review Information10分钟
Labelling Points on a Graph – Review Information5分钟
Labelling all the Points on a Graph – Review Information5分钟
Eyeballing the Data – Review Information5分钟
Using K-Means to Interpret the Data – Review Information5分钟
Week 4 Summative Assessment40分钟
4.8
3 个审阅来自Foundations of Data Science: K-Means Clustering in Python的热门评论
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
讲师
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
关于 伦敦大学金匠学院
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