Web scraping (web harvesting or web data extraction) is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser.
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Web scraping is a technique for gathering data or information on web pages. You could revisit your favorite web site every time it updates for new information. Or you could write a web scraper to have it do it for you!
It is a method to extract data from a website that does not have an API or we want to extract a LOT of data which we can not do through an API due to rate limiting.
Through web scraping we can extract any data which we can see while browsing the web
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
Web scraping (web harvesting or web data extraction) is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser.
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Web scraping is a technique for gathering data or information on web pages. You could revisit your favorite web site every time it updates for new information. Or you could write a web scraper to have it do it for you!
It is a method to extract data from a website that does not have an API or we want to extract a LOT of data which we can not do through an API due to rate limiting.
Through web scraping we can extract any data which we can see while browsing the web
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
Text Mining is an Important part of data mining and it is used nowadays on a large scale. This mining technique is used to find patterns in text data collected from many online sources , and to gain some interestings insights from the patterns observed. Since text is basically everywhere on the internet, it becomes quite difficult to get the data in structured format, which is why text mining plays a huge role. It uses NLP(Natural Language Processing Techniques) to automate the text mining and this concept is used in Machine Learning.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
The process of data cleaning involves the process of transformation of data from a raw format to a format that is compatible with your and use case.
Read More: https://expressanalytics.com/blog/growing-importance-of-data-cleaning/
Data Wrangling and Visualization Using PythonMOHITKUMAR1379
Python is open source and has so many libraries for data wrangling and visualization that makes life of data scientists easier. For data wrangling pandas is used as it represent tabular data and it has other function to parse data from different sources, data cleaning, handling missing values, merging data sets etc. To visualize data, low level matplotlib can be used. But it is a base package for other high level packages such as seaborn, that draw well customized plot in just one line of code. Python has dash framework that is used to make interactive web application using python code without javascript and html. These dash application can be published on any server as well as on clouds like google cloud but freely on heroku cloud.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
What is Web Scraping and What is it Used For? | Definition and Examples EXPLAINED
For More details Visit - https://hirinfotech.com
About Web scraping for Beginners - Introduction, Definition, Application and Best Practice in Deep Explained
What is Web Scraping or Crawling? and What it is used for? Complete introduction video.
Web Scraping is widely used today from small organizations to Fortune 500 companies. A wide range of applications of web scraping a few of them are listed here.
1. Lead Generation and Marketing Purpose
2. Product and Brand Monitoring
3. Brand or Product Market Reputation Analysis
4. Opening Mining and Sentimental Analysis
5. Gathering data for machine learning
6. Competitor Analysis
7. Finance and Stock Market Data analysis
8. Price Comparison for Product or Service
9. Building a product catalog
10. Fueling Job boards with Job listings
11. MAP compliance monitoring
12. Social media Monitor and Analysis
13. Content and News monitoring
14. Scrape search engine results for SEO monitoring
15. Business-specific application
------------
Basics of web scraping using python
Python Scraping Library
Presentation given at the Consorcio Madrono conference on Data Management Plans in Horizon 2020 http://www.consorciomadrono.es/info/web/blogs/formacion/217.php
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
Text Mining is an Important part of data mining and it is used nowadays on a large scale. This mining technique is used to find patterns in text data collected from many online sources , and to gain some interestings insights from the patterns observed. Since text is basically everywhere on the internet, it becomes quite difficult to get the data in structured format, which is why text mining plays a huge role. It uses NLP(Natural Language Processing Techniques) to automate the text mining and this concept is used in Machine Learning.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
The process of data cleaning involves the process of transformation of data from a raw format to a format that is compatible with your and use case.
Read More: https://expressanalytics.com/blog/growing-importance-of-data-cleaning/
Data Wrangling and Visualization Using PythonMOHITKUMAR1379
Python is open source and has so many libraries for data wrangling and visualization that makes life of data scientists easier. For data wrangling pandas is used as it represent tabular data and it has other function to parse data from different sources, data cleaning, handling missing values, merging data sets etc. To visualize data, low level matplotlib can be used. But it is a base package for other high level packages such as seaborn, that draw well customized plot in just one line of code. Python has dash framework that is used to make interactive web application using python code without javascript and html. These dash application can be published on any server as well as on clouds like google cloud but freely on heroku cloud.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
What is Web Scraping and What is it Used For? | Definition and Examples EXPLAINED
For More details Visit - https://hirinfotech.com
About Web scraping for Beginners - Introduction, Definition, Application and Best Practice in Deep Explained
What is Web Scraping or Crawling? and What it is used for? Complete introduction video.
Web Scraping is widely used today from small organizations to Fortune 500 companies. A wide range of applications of web scraping a few of them are listed here.
1. Lead Generation and Marketing Purpose
2. Product and Brand Monitoring
3. Brand or Product Market Reputation Analysis
4. Opening Mining and Sentimental Analysis
5. Gathering data for machine learning
6. Competitor Analysis
7. Finance and Stock Market Data analysis
8. Price Comparison for Product or Service
9. Building a product catalog
10. Fueling Job boards with Job listings
11. MAP compliance monitoring
12. Social media Monitor and Analysis
13. Content and News monitoring
14. Scrape search engine results for SEO monitoring
15. Business-specific application
------------
Basics of web scraping using python
Python Scraping Library
Presentation given at the Consorcio Madrono conference on Data Management Plans in Horizon 2020 http://www.consorciomadrono.es/info/web/blogs/formacion/217.php
Big Data Analysis : Deciphering the haystack Srinath Perera
A primary outcome of Bigdata is to derive useful and actionable insights from large or challenges data collections. The goal is to run the transformations from data, to information, to knowledge, and finally to insights. This includes calculating simple analytics like Mean, Max, and Median, to derive overall understanding about data by building models, and finally to derive predictions from data. Some cases we can afford to wait to collect and processes them, while in other cases we need to know the outputs right away. MapReduce has been the defacto standard for data processing, and we will start our discussion from there. However, that is only one side of the problem. There are other technologies like Apache Spark and Apache Drill graining ground, and also realtime processing technologies like Stream Processing and Complex Event Processing. Finally there are lot of work on porting decision technologies like Machine learning into big data landscape. This talk discusses big data processing in general and look at each of those different technologies comparing and contrasting them.
Data Wranglers DC December meetup: http://www.meetup.com/Data-Wranglers-DC/events/151563622/
There's a lot of data sitting on websites just waiting to be combined with data you have sitting on your servers. During this talk, Robert Dempsey will show you how to create a dataset using Python by scraping websites for the data you want.
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...Denodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/Bvmvc9
Data prep and data blending are terms that have come to prominence over the last year or two. On the surface, they appear to offer functionality similar to data virtualization…but there are important differences!
In this session, you will learn:
• How data virtualization complements or contrasts technologies such as data prep and data blending
• Pros and cons of functionality provided by data prep, data catalog and data blending tools
• When and how to use these different technologies to be most effective
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Python Web Scraper for ACM and Google Scholar.pptxASIMKHAN840563
The Python Web Scraper for ACM and Google Scholar is a powerful tool designed to automate the process of data extraction from two prominent platforms in the academic and research community. By leveraging web scraping techniques, this scraper enables users to efficiently gather and analyze a wide range of information, including research papers, conference proceedings, and academic publications.
HKU Data Curation MLIM7350 Student Project: Data Curation Workshopl_ernest
HKU Data Curation course MLIM7350 student final project - a 30 minute data curation workshop for researchers. Topics covered concept of data curation, tools for data management and data repository options.
This presentation was given in one of the DSATL Mettups in March 2018 in partnership with Southern Data Science Conference 2018 (www.southerndatascience.com)
Research Data Management: An Introductory Webinar from OpenAIRE and EUDATTony Ross-Hellauer
OpenAIRE and EUDAT co-present this webinar which aims to introduce researchers and others to the concept of research data management (RDM). As well as presenting the benefits of taking an active approach to research data management – including increased speed and ease of access, efficiency (fund once, reuse many times), and improved quality and transparency of research – the webinar will advise on strategies for successful RDM, resources to help manage data effectively, choosing where to store and deposit data, the EC H2020 Open Data Pilot and the basics of data management, stewardship and archiving.
Webinar recording available: http://www.instantpresenter.com/eifl/EB57D6888147
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Excel 2010 brought with it two new features which extend the usefulness of pivot tables: the slicer and the timeline. They are really useful, among other use cases, when you want to easily monitor indicators in your data. Join our fellow Sheena Opulencia-Calub to learn more about this.
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With climate change growing as a political topic the past few years, more eyes have been turning toward Extractives Data. But before trying to uncover the dark secrets extractives business, follow this skillshare by out Ecuadorian fellow Julio Lopez to make sure you understand some key points about extractives data.
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Join our Nigerian fellow Nkechi Okwuone to learn about the why and the how of building a community, as illustrated by her experience as an open data project manager in the Edo, Nigeria. Community building in Nigeria (or similar regions) presents its own sets of challenges, so tune in to see how to address them.
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What is R and how useful is it for datajournalism? Isn't Excel enough? And how do you use R anyway?
This new School of Data skillshare by David Opoku will help you understand how R fits into the data pipeline and introduce you to the basics of using the software.
School of Data Fellow Codrina Maria Ilie gave this skillshare as part of our community series.
In this slideshare, you will learn why maps are useful visualization tools as well as what doesn't work with maps. And Codrina shares some tool examples. Be sure to check the detailed notes.
About School of Data:
http://schoolofdata.org/
About Open Knowledge: Okfn.org
( Presented via G+ On Friday October 10, 2014 )
Data Visualization & Design with School of DataSchool of Data
We all know data presentation (visualization) plays a large part in our School of Data workshops as a fundamental aspect of the data pipeline. But how do you know that, beyond using D3 or the latest dataviz app, you are helping people actually communicate visually?
The guest of this skillshare was Code for South Africa/School of Data Fellow, Hannah Williams
Schoolofdata.org
Okfn.org
http://code4sa.org/
Date: Thursday (Sept. 25, 2014)
www.hannahwilliams.co.za
hello@hannahwilliams.co.za
Who are your partners and stakeholders?
This Network Mapping presentation was given on Wednesday, September 10, 2014
Nisha Thompson, School of Data Fellow
Datameet.org
See the accompanying video - https://www.youtube.com/watch?v=7ntDuwhLaYM
To learn more about http://schoolofdata.org/
Join the community: https://lists.okfn.org/mailman/listinfo/school-of-data
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
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🔑 Key findings include:
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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
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4. This should be useful to ...
● Non-tech-savvy data journalists
● Advanced data journalists
● Web developers & data publishers
● School of Data fellows
● Open Data enthusiasts
6. Data Scraping: what is it ?
scrape [ verb ˈskrāp ]
: to remove from a surface by usually repeated strokes of an edged instrument
: to collect by or as if by scraping —often used with up or together <scrape up the
price of a ticket>
- Merriam Webster
“The transformation of unstructured data on the web, typically in HTML format, into
structured data that can be stored and analyzed in a central local database or
spreadsheet.”
- Wikipedia (web scraping)
7. When should you scrape data ?
● PDF Data
● HTML data
Machine-readable data
9. Cases when you can scrape
● Create a dataset for a data workshop
● Create a database for a data -driven app
● Create a data visualisation for a story
11. Best Practices For Scrapers
1. Scraping is not scary!
a. Use existing tools
2. Use a modern and friendly browser
a. Chrome, Firefox, Opera, Safari
b. Avoid Internet Explorer
3. Map out the process
a. Where does scraping fit in?
12. Best Practices For Data Publishers
1. Have a consistent structure
a. Websites
b. PDFs
2. Always think about your data end users
a. Before, during & after publishing
13. Steps
1. Map out the process/pipeline for your data project
2. Identify your data source (website, PDF, API?)
3. Decide on storage format for your scraped data
a. CSV file, Spreadsheet, Google docs
b. Database
4. Select scraping tool
5. Verify and Clean data
16. Tools: Scraping Apps
1. Point and click
a. Scraper Google Chrome extension
b. ScraperWiki (Classic version)
c. Import.io, Kimono Labs, Webscraper.io
d. Tabula (PDF)
2. Programming (Python libraries)
a. Beautiful Soup
b. Pattern (PDF and HTML)
c. Scrapy
18. Resources - Readings and Tools
1. Five data scraping tools for would-be data journalists
2. Making data on the web useful: scraping
3. Liberating HTML Data Tables
4. BeautifulSoup
5. Pattern
6. Scrapy
7. Datahub
8. Import.io
9. Kimono
10. Webscraper.io
11. Tabula