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.

Che cos'è la data science o scienza dei dati?

Data science everywhere!

L’importanza della data science

Applicazioni della data science

Dose giornaliera di data science

The science of smart assistants

Ruoli e strumenti della data science

Selezionare il candidato giusto

Un ruolo da data scientist

Introduction to Data Science

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.

Data sources

Sorting data sources

Asthma frequencies

Data types

Classifying data types

Net promoter score

Activity tracker

Data storage and retrieval

Cloud platforms

Querying a database

Which type of database?

Data Pipelines

Data pipeline characteristics

Extract Transform Load

Data Collection and Storage

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.

Data preparation

The truth is out there

Are you prepared?

Exploratory Data Analysis

Numerical EDA

Visual EDA

Visualization

Interactive dashboards

Improving a dashboard

Preparation, Exploration, and Visualization

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.

A/B Testing

Creating an A/B testing workflow

Statistical significance

Intermediate results

Time series forecasting

Classifying time series data

Interpret a time series plot

Supervised machine learning

When to use supervised learning

Features and labels

Model Evaluation

Clustering

Supervised vs. unsupervised

Cluster size selection

Congratulations!

Experimentation and Prediction

Course Glossary

Cos'è la data science o scienza dei dati, perché è così popolare e perché l'Harvard Business Review l'ha definita "il lavoro più sexy del XXI secolo"? In questo corso non tecnico, ti verrà spiegato tutto ciò che hai sempre avuto paura di chiedere su questo campo entusiasmante e in rapida crescita, senza dover scrivere una sola riga di codice. Attraverso esercizi pratici, imparerai a conoscere i diversi ruoli dei data scientist, argomenti fondamentali come test A/B, analisi delle serie temporali e machine learning, e come i data scientist estraggono conoscenze ed informazioni da dati reali. Non farti intimidire dai termini tecnici. Inizia a imparare, acquisisci competenze in questo campo in forte crescita, e scopri perché la data science è accessibile a tutti!

Scopri la data science, la sua popolarità e impara con esercizi pratici. Nessun codice richiesto.

Comprendere la Data Science

Un'introduzione alla data science senza necessità di programmare.

Comprendere gli argomenti dei dati

The science of smart assistants

Comprendere la Data Science

Esercizio pratico interattivo