De wetenschap achter slimme assistenten

Is het je opgevallen dat Alexa, Siri of Google Assistant slimmer lijken te worden naarmate je ze vaker gebruikt? Het zijn niet alleen spraakgestuurde gadgets – ze leren van je gewoonten en gebruiken data science om hun antwoorden steeds beter te maken.

Van herinneringen instellen tot slimme apparaten bedienen: ze verbeteren continu om te voorspellen wat je nodig hebt.

Gebruik het interactieve paneel om vier manieren te vergelijken waarop een systeem kan proberen te verbeteren en beantwoord daarna de vraag hieronder.

Welke data science-techniek helpt virtuele assistenten slimmer te worden bij elke interactie?

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Data Science begrijpen

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Data Science begrijpen

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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: Wat is data science?Exercise 2: Data science is overal!Exercise 3: Belang van data science Exercise 4: Toepassingen van data science Exercise 5: Dagelijkse dosis data science Exercise 6: De wetenschap achter slimme assistenten

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Exercise 7: Rollen en tools in data science Exercise 8: De juiste kandidaat kiezen Exercise 9: De rol van een data scientist

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: 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!