Webinar: Mastering Python - An Excellent tool for Web Scraping and Data Analysis
9 likes8,466 views
The document outlines a Python course for big data analytics, emphasizing its ease of use for beginners and robust libraries like Pydoop and SciPy. It covers practical applications such as web scraping, data analysis in healthcare, e-commerce, and modeling with Python, as well as the integration of Hadoop for large datasets. Additionally, it highlights Python's role in data science through its diverse libraries suitable for various data-related tasks.
![Demo: Zombie Invasion Model
This is a lighthearted example, a system of ODEs(Ordinary differential equations) can be used to model a "zombie
invasion", using the equations specified by Philip Munz.
The system is given as:
dS/dt = P - B*S*Z - d*S
dZ/dt = B*S*Z + G*R - A*S*Z
dR/dt = d*S + A*S*Z - G*R
Where:
S: the number of susceptible victims
Z: the number of zombies
R: the number of people "killed”
P: the population birth rate
d: the chance of a natural death
B: the chance the "zombie disease" is transmitted (an alive person becomes a zombie)
G: the chance a dead person is resurrected into a zombie
A: the chance a zombie is totally destroyed
There are three scenarios given in the program to show how Zombie Apocalypse vary with different initial
conditions.
This involves solving a system of first order ODEs given by: dy/dt = f(y, t) Where y = [S, Z, R].
Slide 19 www.edureka.co/python](https://image.slidesharecdn.com/pythonwebinar1-141113233113-conversion-gate01/85/Webinar-Mastering-Python-An-Excellent-tool-for-Web-Scraping-and-Data-Analysis-19-320.jpg)
Webinar: Mastering Python - An Excellent tool for Web Scraping and Data Analysis
- 1. Python For BIG DATA ANALYTICS View Mastering Python course details at http://www.edureka.co/python For Queries: Post on Twitter @edurekaIN: #askEdureka Post on Facebook /edurekaIN For more details please contact us: US : 1800 275 9730 (toll free) INDIA : +91 88808 62004 Email Us : sales@edureka.co
- 2. Objectives At the end of this module, you will be able to Understand Python Understand Web Scrapping example using Python Understand PyDoop: Python API for Hadoop Implement Word Count example in Pydoop Integrate Data Science with Python Implement Zombie Invasion modeling using Python Slide 2 www.edureka.co/python
- 3. Why Python? Python is a great language for the beginner programmers since it is easy-to-learn and easy-to-maintain. Python’s biggest strength is that the bulk of it’s library is portable. It also supports GUI Programming and can be used to create Applications portable on Mac, Windows and Unix X-Windows system. With libraries like PyDoop and SciPy, it’s a dream come true for Big Data Analytics. Slide 3 www.edureka.co/python
- 4. Growing Interest in Python Slide 4 www.edureka.co/python
- 5. Demo: Web Scraping using Python This example demonstrates how to scrape basic financial data from IMDB webpage We shall use open source web scraping framework for Python called Beautiful Soup to crawl and extract data from webpages Scraping is used for a wide range of purposes, from data mining to monitoring and automated testing Slide 5 www.edureka.co/python
- 6. Demo: Collecting Tweets using Python This example demonstrates how to extract historical tweets for a particular brand like “nike” or “apple” We shall make a REST API call to twitter to extract tweets This data can be further used to perform sentiment analysis for a particular brand on Twitter Slide 6 www.edureka.co/python
- 7. Big Data Lots of Data (Terabytes or Petabytes) Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization cloud tools statistics No SQL Big Data compression support database storage analize information mobile processing terabytes Slide 7 www.edureka.co/python
- 8. Un-Structured Data is Exploding Complex, Unstructured Relational 2500 exabytes of new information in 2012 with internet as primary driver Digital universe grew by 62% last year to 800K petabytes and will grow to 1.2 “zettabytes” this year Slide 8 www.edureka.co/python
- 9. Big Data Scenarios : Hospital Care Hospitals are analyzing medical data and patient records to predict those patients that are likely to seek readmission within a few months of discharge. The hospital can then intervene in hopes of preventing another costly hospital stay Medical diagnostics company analyzes millions of lines of data to develop first non-intrusive test for predicting coronary artery disease. To do so, researchers at the company analyzed over 100 million gene samples to ultimately identify the 23 primary predictive genes for coronary artery disease Slide 9 www.edureka.co/python
- 10. Big Data Scenarios : Amazon.com Amazon has an unrivalled bank of data on online consumer purchasing behaviour that it can mine from its 152 million customer accounts Amazon also uses Big Data to monitor, track and secure its 1.5 billion items in its retail store that are laying around it 200 fulfilment centres around the world. Amazon stores the product catalogue data in S3 S3 can write, read and delete objects up to 5 TB of data each. The catalogue stored in S3 receives more than 50 million updates a week and every 30 minutes all data received is crunched and reported back to the different warehouses and the website http://wp.streetwise.co/wp-content/uploads/2012/08/Amazon-Recommendations.png Slide 10 www.edureka.co/python
- 11. Netflix uses 1 petabyte to store the videos for streaming BitTorrent Sync has transferred over 30 petabytes of data since its pre-alpha release in January 2013 The 2009 movie Avatar is reported to have taken over 1 petabyte of local storage at Weta Digital for the rendering of the 3D CGI effects One petabyte of average MP3-encoded songs (for mobile, roughly one megabyte per minute), would require 2000 years to play Big Data Scenarios: NetFlix http://smhttp.23575.nexcesscdn.net/80ABE1/sbmedia/blog/wp-content/uploads/2013/03/netflix-in-asia.png Slide 11 www.edureka.co/python
- 12. IBM’s Definition IBM’s Definition – Big Data Characteristics http://www-01.ibm.com/software/data/bigdata/ Web logs Audios Images Videos Sensor Data Volume Velocity Variety Slide 12 www.edureka.co/python
- 13. Hadoop for Big Data Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of commodity computers using a simple programming model It is an Open-source Data Management with scale-out storage & distributed processing Slide 13 www.edureka.co/python
- 14. Hadoop and MapReduce Hadoop is a system for large scale data processing It has two main components: HDFS – Hadoop Distributed File System (Storage) » Distributed across “nodes” » Natively redundant » NameNode tracks locations MapReduce (Processing) » Splits a task across processors » “near” the data & assembles results » Self-Healing, High Bandwidth » Clustered storage » Job Tracker manages the Task Trackers Key Value Map-Reduce Slide 14 www.edureka.co/python
- 15. PyDoop – Hadoop with Python Python can be used to write Hadoop MapReduce programs and applications to access HDFS API for Hadoop with PyDoop package PyDoop package provides a Python API for Hadoop MapReduce and HDFS PyDoop has several advantages over Hadoop’s built-in solutions for Python programming, i.e., Hadoop Streaming and Jython One of the biggest advantage of PyDoop is it’s HDFS API. This allows you to connect to an HDFS installation, read and write files, and get information on files, directories and global file system properties The MapReduce API of PyDoop allows you to solve many complex problems with minimal programming efforts. Advance MapReduce concepts such as ‘Counters’ and ‘Record Readers’ can be implemented in Python using PyDoop Slide 15 www.edureka.co/python
- 16. Demo: Word Count using Hadoop Streaming API The example shows the simple word count application written in Python We shall use Hadoop Streaming APIs to run MapReduce code written in Python Word Count application can be used to index text documents/files for a given “search query” Slide 16 www.edureka.co/python
- 17. Python and Data Science The day-to-day tasks of a data scientist involves many interrelated but different activities such as accessing and manipulating data, computing statistics and , creating visual reports on that data, building predictive and explanatory models, evaluating these models on additional data, integrating models into production systems, etc. Python is an excellent choice for Data Scientist to do his day-to-day activities as it provides libraries to do all these things Python has a diverse range of open source libraries for just about everything that a Data Scientist does in his day-to-day work Python and most of its libraries are both open source and free Slide 17 www.edureka.co/python
- 18. SciPy.org SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. NumPy Base N-dimensional array package IPython Enhanced Interactive Console SciPy library Base N-dimensional array package Sympy Symbolic mathematics Matplotlib Comprehensive 2D Plotting pandas Data structures and analysis Slide 18 www.edureka.co/python
- 19. Demo: Zombie Invasion Model This is a lighthearted example, a system of ODEs(Ordinary differential equations) can be used to model a "zombie invasion", using the equations specified by Philip Munz. The system is given as: dS/dt = P - B*S*Z - d*S dZ/dt = B*S*Z + G*R - A*S*Z dR/dt = d*S + A*S*Z - G*R Where: S: the number of susceptible victims Z: the number of zombies R: the number of people "killed” P: the population birth rate d: the chance of a natural death B: the chance the "zombie disease" is transmitted (an alive person becomes a zombie) G: the chance a dead person is resurrected into a zombie A: the chance a zombie is totally destroyed There are three scenarios given in the program to show how Zombie Apocalypse vary with different initial conditions. This involves solving a system of first order ODEs given by: dy/dt = f(y, t) Where y = [S, Z, R]. Slide 19 www.edureka.co/python
- 20. Questions Slide 20 Twitter @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions www.edureka.co/python
- 21. Slide 21 Course Url