Cluster size selection

Crystal manages operations for a meal delivery service in Houston, Texas. She has latitude and longitude data for meals requests in the past month. She'd like to create several new "delivery hubs" where demand has been greatest. In order to do this, she's asked a machine learning scientist to use a clustering algorithm to divide the orders into groups based on their location.

The machine learning scientist isn't sure how many clusters to make. Take a look at the clustering algorithm's results using different amount of clusters.

What is the most suitable number of clusters?

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We'll start the course by defining what data science is. We'll cover the data science workflow and how data science is applied to real-world problems. We'll finish the chapter by learning about different roles within the data science field.

Exercise 1: What is data science?Exercise 2: Data science everywhere!Exercise 3: Importance of data science Exercise 4: Applications of data science Exercise 5: Daily dose of data science Exercise 6: The science of smart assistants Exercise 7: Data science roles and tools Exercise 8: Selecting the right candidate Exercise 9: A data scientist role

Now that we understand the data science workflow, we'll dive deeper into the first step: data collection and storage. We'll learn about the different data sources you can draw from, what that data looks like, how to store the data once it's collected, and how a data pipeline can automate the process.

Exercise 1: Data sources Exercise 2: Sorting data sources Exercise 3: Asthma frequencies Exercise 4: Data types Exercise 5: Classifying data types Exercise 6: Net promoter score Exercise 7: Activity tracker Exercise 8: Data storage and retrieval Exercise 9: Cloud platforms Exercise 10: Querying a database Exercise 11: Which type of database?Exercise 12: Data Pipelines Exercise 13: Data pipeline characteristics Exercise 14: Extract Transform Load

Data preparation is fundamental: data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This chapter will show you how to diagnose problems in your data, deal with missing values and outliers. You will then learn about visualization, another essential tool to both explore your data and convey your findings.

Exercise 1: Data preparation Exercise 2: The truth is out there Exercise 3: Are you prepared?Exercise 4: Exploratory Data Analysis Exercise 5: Numerical EDA Exercise 6: Visual EDA Exercise 7: Visualization Exercise 8: Interactive dashboards Exercise 9: Improving a dashboard

In this final chapter, we'll discuss experimentation and prediction! Beginning with experiments, we'll cover A/B testing, and move on to time series forecasting where we'll learn about predicting future events. Finally, we'll end with machine learning, looking at supervised learning, and clustering.

Exercise 1: Test A/B Exercise 2: Creare un flusso di lavoro per i test A/B Exercise 3: Significatività statistica Exercise 4: Intermediate results Exercise 5: Previsione di serie temporali Exercise 6: Classificazione dei dati di serie temporali Exercise 7: Interpret a time series plot Exercise 8: Machine Learning supervisionato Exercise 9: Quando utilizzare l'apprendimento supervisionato Exercise 10: Features and labels Exercise 11: Valutazione del modello Exercise 12: Clustering Exercise 13: Supervisionato vs. Non supervisionato Exercise 14: Cluster size selection

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Exercise 15: Congratulazioni!