Informações sobre o curso
4.0
106 classificações
26 avaliações
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Nível intermediário

Nível intermediário

Horas para completar

Aprox. 14 horas para completar

Sugerido: 4 weeks of study, 3-6 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês

Habilidades que você terá

Python ProgrammingStatistical AnalysisSentiment AnalysisR Programming
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Nível intermediário

Nível intermediário

Horas para completar

Aprox. 14 horas para completar

Sugerido: 4 weeks of study, 3-6 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
3 horas para concluir

Introduction to Data Analytics

In this first unit of the course, several concepts related to social media data and data analytics are introduced. We start by first discussing two kinds of data - structured and unstructured. Then look at how structured data, the primary focus of this course, is analyzed and what one could gain by doing such analysis. Finally, we briefly cover some of the visualizations for exploring and presenting data.Make sure to go through the material for this unit in the sequence it's provided. First, watch the four short videos, then take the practice test, followed by the two quizzes. Finally, read the documents about installation and configuration of Python and R. This is very important - before proceeding to the next units, make sure you have installed necessary tools, and also learned how to install new packages/libraries for them. The course expects students to have programming experience in Python and R....
Reading
4 videos (Total 33 min), 4 leituras, 2 testes
Video4 videos
Video-2: Structured vs. Unstructured Data10min
Video-3: Analyzing Structured Data10min
Video-4: Visualization of Data8min
Reading4 leituras
Anaconda Installation20min
Python installation, configuration, and usage30min
R installation30min
R/RStudio Setup Guide (on Windows)20min
Quiz2 exercícios práticos
Quiz-115min
Quiz-215min
Semana
2
Horas para completar
4 horas para concluir

Collecting and Extracting Social Media Data

In this unit we will see how to collect data from Twitter and YouTube. The unit will start with an introduction to Python programming. Then we will use a Python script, with a little editing, to extract data from Twitter. A similar exercise will then be done with YouTube. In both the cases, we will also see how to create developer accounts and what information to obtain to use the data collection APIs. Once again, make sure to go item-by-item in the order provided. Before beginning this unit, ensure that you have all the right tools (Python, R, Anaconda) ready and configured. The lessons depend on them and also your ability to install required packages....
Reading
4 videos (Total 47 min), 6 leituras, 3 testes
Video4 videos
Video-2: Introduction to Python Programming16min
Video-3: Using Python to Extract Data from Twitter15min
Video-4: Using Python to Extract Data from YouTube11min
Reading6 leituras
Errata: please read this first1min
Python Packages Installation5min
(Optional) Introduction to Python for Econometrics, Statistics and Data Analysis30min
Script: twitter_search.py0
Twitter libraries10min
Script: youtube_search.py0
Quiz2 exercícios práticos
Python Programming Exercise2min
YouTube data download using Python6min
Semana
3
Horas para completar
4 horas para concluir

Data Analysis, Visualization, and Exploration

In this unit, we will focus on analyzing and visualizing the data from various social media services. We will first use the data collected before from YouTube to do various statistics analyses such as correlation and regression. We will then introduce R - a platform for doing statistical analysis. Using R, then we will analyze a much larger dataset obtained from Yelp. Make sure you have covered the material in the previous units before proceeding with this. That means, having all the tools (Anaconda, Python, and R) as well as various packages installed. We will also need new packages this time, so make sure you know how to install them to your Python or R. If needed, please review some basic concepts in statistics - specifically, correlation and regression - before or during working on this unit....
Reading
4 videos (Total 87 min), 8 leituras, 2 testes
Video4 videos
Video-2: Analyzing Social Media Data Using Python26min
Video-3: Introduction to R26min
Video-4: Social Media Data Analysis with R32min
Reading8 leituras
Script: twitter_process.py0
Data: iqsize.csv0
R Installation Guide10min
Installing R Packages5min
Statistical Analysis with R10min
Read this first2min
Scripts for converting json to csv2min
Data Visualization with ggplot2 (R) - Cheat Sheet10min
Quiz1 exercício prático
Statistical Analysis with Twitter Data6min
Semana
4
Horas para completar
3 horas para concluir

Case Studies

In the final unit of this course, we will work on two case studies - both using Twitter and focusing on unstructured data (in this case, text). The first case study will involve doing sentiment analysis with Python. The second case study will take us through basic text mining application using R. We wrap up the unit with a conclusion of what we did in this course and where to go next for further learning and exploration....
Reading
4 videos (Total 47 min), 4 leituras, 2 testes
Video4 videos
Video-2: Sentiment Analysis with Twitter Data21min
Video-3: Text Mining of Twitter Data15min
Video-4: Conclusion6min
Reading4 leituras
Script: twitter_sentiments.py0
NLTK10min
Script: text_mining_twitter.r0
An Introduction to Network Analysis with R and statnet10min
Quiz1 exercício prático
Sentiment Analysis with Twitter6min

Instrutores

Avatar

Chirag Shah

Associate Professor
Information and Computer Science

Sobre Rutgers the State University of New Jersey

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