Trabalho de conclusão de Ciência de Dados Aplicada (Coursera)

Offered by IBM,
Trabalho de conclusão de Ciência de Dados Aplicada (Coursera)

Este curso do projeto de conclusão mostrará um pouco do que os cientistas de dados passam na vida real ao trabalhar com dados. Você aprenderá sobre dados de localização e diferentes provedores de dados de localização, como o Foursquare. Você aprenderá como fazer chamadas de API RESTful para a API do Foursquare a fim de recuperar dados sobre locais em diferentes bairros do mundo. Você também aprenderá como usar a criatividade quando os dados não estiverem disponíveis na hora, coletando dados da Web e analisando o código HTML.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

Você utilizará Python e sua biblioteca do Pandas para manipular dados, o que ajudará você a refinar suas habilidades para explorar e analisar dados.
Por fim, você deverá usar a biblioteca Folium para obter excelentes mapas de dados geoespaciais e para comunicar seus resultados e descobertas.
Se optar por fazer este curso e obter o certificado de conclusão de curso do Coursera, você também poderá ganhar um selo digital da IBM.

Syllabus

WEEK 1
Introdução
Neste módulo, você aprenderá sobre o escopo deste curso de conclusão e o contexto do projeto em que estará trabalhando. Você aprenderá sobre diferentes provedores de dados de localização e o que geralmente compõe esses dados. Por fim, será necessário enviar um link para um novo repositório em sua conta do Github dedicado a este curso.

WEEK 2
API do Foursquare
Neste módulo, você aprenderá em detalhes sobre o Foursquare, que é o provedor de dados de localização que usaremos neste curso, e sua API. Basicamente, você aprenderá como criar uma conta de desenvolvedor do Foursquare e como usar suas credenciais para pesquisar locais próximos de um tipo específico, explorar um local específico e pesquisar locais populares em uma localização.

WEEK 3
Segmentação e clustering de bairros
Neste módulo, você aprenderá sobre cluster k-means, que é uma forma de aprendizado não supervisionado. Em seguida, você usará o clustering e a API do Foursquare para segmentar e agrupar os bairros na cidade de Nova York. Além disso, você aprenderá como extrair dados de sites e analisar código HTML usando o pacote Python Beautifulsoup, e converter dados em um dataframe do Pandas.

WEEK 4
A Batalha dos Bairros
Neste módulo, você começará a trabalhar no projeto de conclusão. Você definirá um problema com clareza e discutirá os dados que usará para resolvê-lo.

WEEK 5
A Batalha dos Bairros (continuação)
Neste módulo, você executará todo o trabalho restante para terminar o seu projeto de conclusão. Você enviará um relatório do seu projeto para uma avaliação de um colega.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Case studies in business analytics with ACCENTURE (Coursera) Coursera
ESSEC Business School

Case studies in business analytics with ACCENTURE (Coursera)

This course is RESTRICTED TO LEARNERS ENROLLED IN Strategic Business Analytics SPECIALIZATION as a preparation to the capstone project. During the first two MOOCs, we focused on specific techniques for specific applications. Instead, with this third MOOC, we provide you with different examples to open your mind to different applications from different industries and sectors. The objective is to give you an helicopter overview on what's happening in this field. You will see how the tools presented in the two previous courses of the Specialization are used in real life projects.

Nov 16th 2026
3 Weeks
Big Data Science with the BD2K-LINCS Data Coordination and Integration Center (Coursera) Coursera
Icahn School of Medicine at Mount Sinai

Big Data Science with the BD2K-LINCS Data Coordination and Integration Center (Coursera)

In this course we briefly introduce the DCIC and the various Centers that collect data for LINCS. We then cover metadata and how metadata is linked to ontologies. We then present data processing and normalization methods to clean and harmonize LINCS data. This follow discussions about how data is served as RESTful APIs. Most importantly, the course covers computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning. Finally, we introduce crowdsourcing/citizen-science projects where students can work together in teams to extract expression signatures from public databases and then query such collections of signatures against LINCS data for predicting small molecules as potential therapeutics.

Nov 16th 2026
5-12 Weeks
Machine Learning for Accounting with Python (Coursera) Coursera
University of Illinois at Urbana-Champaign

Machine Learning for Accounting with Python (Coursera)

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.

Nov 16th 2026
5-12 Weeks
Precision Medicine (Coursera) Coursera
University of Geneva

Precision Medicine (Coursera)

This course will provide you with the key knowledge and tools to understand the fundamentals and practical implications of precision medicine, its opportunities and challenges. It will address precision-medicine era diagnostics, treatment selection, genetic counseling, public health interventions, and biomedical research. It will also deal with data science and ethical issues. From genomic analysis and genetic counseling to cancer biomarkers, from risk assessment of chronic diseases to the understanding of gene-environment interactions, from pharmacogenomics to multi-omics data integration, experts will walk you through the many aspects of precision medicine, from the bench to the bedside and to population health.

Nov 16th 2026
5-12 Weeks
Machine Learning: Concepts and Applications (Coursera) Coursera
University of Chicago

Machine Learning: Concepts and Applications (Coursera)

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.

Nov 16th 2026
5-12 Weeks
Exam Prep AI-102: Microsoft Azure AI Engineer Associate (Coursera) Coursera
Whizlabs

Exam Prep AI-102: Microsoft Azure AI Engineer Associate (Coursera)

The AI-102: Designing and Implementing a Microsoft Azure AI Solution certification exam tests the candidate’s experience and knowledge of the AI solutions that make the most of Azure Cognitive Services and Azure services. In addition, the exam also tests the candidate's ability to implement this knowledge by participating in all phases of AI solutions development—from defining requirements, and design to development, deployment, integration, maintenance, performance tuning, and monitoring.

Nov 16th 2026
5-12 Weeks
Problem Solving, Python Programming, and Video Games (Coursera) Coursera
University of Alberta

Problem Solving, Python Programming, and Video Games (Coursera)

This course is an introduction to computer science and programming in Python. Important computer science concepts such as problem solving (computational thinking), problem decomposition, algorithms, abstraction, and software quality are emphasized throughout. The Python programming language and video games are used to demonstrate computer science concepts in a concrete and fun manner. However, a learner can take the knowledge and skills from this course and apply them to non-game problems, other programming languages, and other computer science courses.

Nov 16th 2026
5-12 Weeks
Bayesian Statistics: Mixture Models (Coursera) Coursera
University of California, Santa Cruz

Bayesian Statistics: Mixture Models (Coursera)

Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application.

Nov 16th 2026
5-12 Weeks
Data Science Ethics (Coursera) Coursera
University of Michigan

Data Science Ethics (Coursera)

What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values.

Nov 16th 2026
4 Weeks
Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Nov 16th 2026
5-12 Weeks
Programação para todos (Conceitos básicos de Python) (Coursera) Coursera
University of Michigan

Programação para todos (Conceitos básicos de Python) (Coursera)

Este curso tem como objetivo ensinar a todos os conceitos básicos de programação de computadores usando Python. Abordamos o básico de como criar um programa a partir de uma série de instruções simples em Python. O curso não tem pré-requisitos e evita tudo, exceto a matemática mais simples. Qualquer pessoa com experiência moderada em computadores deve ser capaz de dominar os materiais deste curso. Este curso abordará os capítulos 1 ao 5 do livro “Python para Todos”.

Nov 16th 2026
5-12 Weeks