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.
ロンドン大学（University of London）
このコースについて
41,608 最近の表示
学習内容
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.
習得するスキル
K-Means ClusteringMachine LearningProgramming in Python
シラバス - 本コースの学習内容
週
17時間で修了
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件のビデオ （合計22分）, 4 quizzes
9件のビデオ
Introduction to Data Science2 分
What is Data?1 分
Types of Data1 分
Machine Learning3 分
Supervised vs Unsupervised Learning2 分
K-Means Clustering4 分
Preparing your Data1 分
A Real World Dataset53
4の練習問題
Types of Data – Review Information15 分
Supervised vs Unsupervised – Review Information15 分
K-Means Clustering – Review Information30 分
Week 1 Summative Assessment40 分
週
24時間で修了
Week 2: Means and Deviations in Mathematics and Python
11件のビデオ （合計37分）, 4 readings, 11 quizzes
11件のビデオ
2.1 – Introduction to Mathematical Concepts of Data Clustering1 分
2.2 – Mean of One Dimensional Lists2 分
2.3 – Variance and Standard Deviation3 分
2.4 Jupyter Notebooks6 分
2.5 Variables4 分
2.6 Lists4 分
2.7 Computing the Mean3 分
2.8 Better Lists: NumPy3 分
2.9 Computing the Standard Deviation6 分
Week 2 Conclusion31
4件の学習用教材
Population vs Sample, Bias10 分
Variability, Standard Deviation and Bias10 分
Python Style Guide10 分
Numpy and Array Creation20 分
10の練習問題
Population vs Sample – Review Information5 分
Mean of One Dimensional Lists – Review Information3 分
Variance and Standard Deviation – Review Information4 分
Jupyter Notebooks – Review Information20 分
Variables – Review Information10 分
Lists – Review Information10 分
Computing the Mean – Review Information10 分
Better Lists – Review Information10 分
Computing the Standard Deviation – Review Information10 分
Week 2 Summative Assessment40 分
週
34時間で修了
Week 3: Moving from One to Two Dimensional Data
16件のビデオ （合計53分）, 10 readings, 15 quizzes
16件のビデオ
3.1 Multidimensional Data Points and Features2 分
3.2 Multidimensional Mean2 分
3.3 Dispersion: Multidimensional Variables3 分
3.4 Distance Metrics5 分
3.5 Normalisation1 分
3.6 Outliers1 分
3.7 Basic Plotting2 分
3.7a Storing 2D Coordinates in a Single Data Structure6 分
3.8 Multidimensional Mean4 分
3.9 Adding Graphical Overlays5 分
3.10 Calculating the Distance to the Mean3 分
3.11 List Comprehension3 分
3.12 Normalisation in Python5 分
3.13 Outliers and Plotting Normalised Data2 分
Week 3 Conclusion30
10件の学習用教材
Multidimensional Data Points and Features Recap10 分
Multidimensional Mean Recap10 分
Multidimensional Variables Recap10 分
Distance Metrics Recap10 分
Normalisation Recap10 分
Note on Matplotlib10 分
Matplotlib Scatter Plot Documentation20 分
Matplotlib Patches Documentation10 分
List Comprehension Documentation20 分
3.12 Errata10 分
15の練習問題
Multidimensional Data Points and Features – Review Information3 分
Multidimensional Mean – Review Information3 分
Dispersion: Multidimensional Variables – Review Information5 分
Distance Metrics – Review Information6 分
Normalisation – Review Information3 分
Outliers – Review Information4 分
Basic Plotting – Review Information5 分
Storing 2D Coordinates – Review Information4 分
Multidimensional Mean – Review Information4 分
Adding Graphical Overlays – Review Information6 分
Calculating Distance – Review Information6 分
List Comprehension – Review Information4 分
Normalisation in Python – Review Information4 分
Outliers – Review Information2 分
Week 3 Summative Assessment25 分
週
45時間で修了
Week 4: Introducing Pandas and Using K-Means to Analyse Data
8件のビデオ （合計37分）, 6 readings, 8 quizzes
8件のビデオ
4.1: Using the Pandas Library to Read csv Files5 分
4.1a: Sorting and Filtering Data Using Pandas8 分
4.1b: Labelling Points on a Graph4 分
4.1c: Labelling all the Points on a Graph3 分
4.2: Eyeballing the Data5 分
4.3: Using K-Means to Interpret the Data8 分
Week 4: Conclusion35
6件の学習用教材
Week 4 Code Resources5 分
Pandas Read_CSV Function15 分
More Pandas Library Documentation10 分
The Pyplot Text Function10 分
For Loops in Python10 分
Documentation for sklearn.cluster.KMeans10 分
7の練習問題
Using the Pandas Library to Read csv Files – Review Information5 分
Sorting and Filtering Data Using Pandas – Review Information10 分
Labelling Points on a Graph – Review Information5 分
Labelling all the Points on a Graph – Review Information5 分
Eyeballing the Data – Review Information5 分
Using K-Means to Interpret the Data – Review Information5 分
Week 4 Summative Assessment40 分
4.7
7件のレビューFoundations of Data Science: K-Means Clustering in Python からの人気レビュー
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
ロンドン大学（University of London）について
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
ロンドン大学ゴールドスミス・カレッジ（Goldsmiths, University of London）について
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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