Interactive dashboards

Dashboards are great, interactive dashboards are even better. Find here a dashboard made for tracking a website's activity. It consists of (A) average page visit length segmented by age, (B) the number of visits today, (C) the monthly active users over time, and (D) the source of traffic segmented by month.

If you hover over or tap certain elements of the bar and line graphs, you'll see some interactivity that shows more detail about the selected data. You can always reset any plot to its original state by double-clicking it.

Using this interactivity, how much website traffic in March was from search engines?

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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: Préparation des données Exercise 2: Aux frontières du réel Exercise 3: Êtes-vous prêt ?Exercise 4: Analyse exploratoire des données (EDA)Exercise 5: EDA numérique Exercise 6: Visual EDA Exercise 7: Visualisation Exercise 8: Interactive dashboards

Exercice en cours

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: A/B Testing Exercise 2: Creating an A/B testing workflow Exercise 3: Statistical significance Exercise 4: Intermediate results Exercise 5: Time series forecasting Exercise 6: Classifying time series data Exercise 7: Interpret a time series plot Exercise 8: Supervised machine learning Exercise 9: When to use supervised learning Exercise 10: Features and labels Exercise 11: Model Evaluation Exercise 12: Clustering Exercise 13: Supervised vs. unsupervised Exercise 14: Cluster size selection Exercise 15: Congratulations!