Week 1: Foundations of Data Science: K-Means Clustering in Python-Welcome and Introduction
Welcome and Introduction
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Week 1: Foundations of Data Science: K-Means Clustering in Python-Week 1 Introduction
Introduction to Data Science
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Week 1: Foundations of Data Science: K-Means Clustering in Python-Data
What is Data?
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Types of Data
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Week 1: Foundations of Data Science: K-Means Clustering in Python-Machine Learning
Machine Learning
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Week 1: Foundations of Data Science: K-Means Clustering in Python-Supervised vs Unsupervised Learning
Supervised vs Unsupervised Learning
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Week 1: Foundations of Data Science: K-Means Clustering in Python-Clustering with K-Means
K-Means Clustering
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Preparing your Data
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Week 1: Foundations of Data Science: K-Means Clustering in Python-Week 1 Outro and Assessment
A Real World Dataset
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Week 2: Means and Deviations in Mathematics and Python-Week 2 Introduction
2.0: Week 2 Introduction
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Week 2: Means and Deviations in Mathematics and Python-Mean, Variance and Standard Deviation
2.1 – Introduction to Mathematical Concepts of Data Clustering
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2.2 – Mean of One Dimensional Lists
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Population vs Sample, Bias
2.3 – Variance and Standard Deviation
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Variability, Standard Deviation and Bias
Week 2: Means and Deviations in Mathematics and Python-Development Environment: Jupyter Notebooks
2.4 Jupyter Notebooks
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Week 2: Means and Deviations in Mathematics and Python-Computing Basic Statistics in Python
2.5 Variables
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Python Style Guide
2.6 Lists
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2.7 Computing the Mean
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2.8 Better Lists: NumPy
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Numpy and Array Creation
2.9 Computing the Standard Deviation
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Week 2: Means and Deviations in Mathematics and Python-Week 2 Outro and Assessment
Week 2 Conclusion
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Week 3: Moving from One to Two Dimensional Data-Week 3 Introduction
Week 3 Introduction
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Week 3: Moving from One to Two Dimensional Data-Mathematics for Multidimensional Data
3.1 Multidimensional Data Points and Features
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Multidimensional Data Points and Features Recap
3.2 Multidimensional Mean
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Multidimensional Mean Recap
3.3 Dispersion: Multidimensional Variables
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Multidimensional Variables Recap
3.4 Distance Metrics
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Distance Metrics Recap
3.5 Normalisation
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Normalisation Recap
3.6 Outliers
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Week 3: Moving from One to Two Dimensional Data-Working with Multidimensional Data in Python
3.7 Basic Plotting
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Note on Matplotlib
Matplotlib Scatter Plot Documentation
3.7a Storing 2D Coordinates in a Single Data Structure
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3.8 Multidimensional Mean
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3.9 Adding Graphical Overlays
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Matplotlib Patches Documentation
3.10 Calculating the Distance to the Mean
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3.11 List Comprehension
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List Comprehension Documentation
3.12 Normalisation in Python
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3.12 Errata
3.13 Outliers and Plotting Normalised Data
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Week 3: Moving from One to Two Dimensional Data-Week 3 Outro and Assessment
Week 3 Conclusion
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Week 4: Introducing Pandas and Using K-Means to Analyse Data-Week 4 Introduction
Week 4 Introduction
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Week 4 Code Resources
Week 4: Introducing Pandas and Using K-Means to Analyse Data-Using the Pandas Library to Read, Sort and Filter Data
4.1: Using the Pandas Library to Read csv Files
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Pandas Read_CSV Function
4.1a: Sorting and Filtering Data Using Pandas
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More Pandas Library Documentation
Week 4: Introducing Pandas and Using K-Means to Analyse Data-Plotting and Labelling the Data
4.1b: Labelling Points on a Graph
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The Pyplot Text Function
4.1c: Labelling all the Points on a Graph
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For Loops in Python
Week 4: Introducing Pandas and Using K-Means to Analyse Data-Interpreting the Data
4.2: Eyeballing the Data
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4.3: Using K-Means to Interpret the Data
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Documentation for sklearn.cluster.KMeans
Week 4: Introducing Pandas and Using K-Means to Analyse Data-Week 4 Outro and Assessment
Week 4: Conclusion
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Week 5: A Data Clustering Project-Welcome and Introduction
Introduction to Week 5
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Week 5: A Data Clustering Project-Understanding Your Task
5.1 Can a Machine Detect Fake Notes?
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5.2 Working for a Client
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Week 5: A Data Clustering Project-Organising Your Work on a Data Science Project
5.3 How to Organize Work on Your Project
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5.4 Dealing With Difficulties
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Week 5: A Data Clustering Project-Doing the Project
5.5 No Data no Data Science: Introduction of the Dataset
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Week 5 Code Resource – the Dataset for our Project
5.6 Modelling
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Week 5: A Data Clustering Project-Presenting the Results of Your Project
5.7 Presenting the Project Results
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Saving plt.scatter Outputs as Figures
Week 5: A Data Clustering Project-Week 5 Outro and Assessment
Additional Recommended Reading for Week 5
5.8 Concluding Remarks
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