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Learn Python - Full Course for Beginners
 
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This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗If you liked this video, Mike accepts donations on his website: accept donations on my website: https://www.mikedane.com/contribute/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 4302506 freeCodeCamp.org
Jordi Torrents - Analyzing code contributions to the CPython project using NetworkX and Matplotlib
 
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Filmed at PyData Barcelona 2017 https://pydata.org/barcelona2017/schedule/presentation/15/ www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 238 PyData
BioPython: Sequence Analysis (Part 1)
 
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Presented for the course COMP 364 at McGill University. Material: https://nbviewer.jupyter.org/github/cgoliver/Notebooks/blob/master/COMP_364/L25/L25.ipynb Webpage:http://cs.mcgill.ca/~cgonza11/COMP_364/
Views: 5568 Carlos G. Oliver
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 936842 David Langer
Data Science Tutorial | Data Science for Beginners | Data Science with Python Tutorial | Simplilearn
 
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This Data Science Tutorial will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist. This Data Science tutorial will cover the following topics: 1. What is Data Science? ( 00:43 ) 2. Who is a Data Scientist? ( 02:02 ) 3. What does a Data Scientist do? ( 02:25 ) To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/V4Zn8i Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning 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. A 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’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package 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 Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-bTTxei-Data-Sciene-Tutorial-jNeUBWrrRsQ&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 33153 Simplilearn
Let's learn D3.js - D3 for data visualization (full course)
 
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This course teaches you how to visualize data in the browser using D3.js. Watch it here or check out the interactive version at Scrimba, where you’ll be able to play with the code as well: https://scrimba.com/g/gd3js D3.js is the most popular data visualization library for the web. It allows you to make sense of your data through a powerful API of methods. Throughout this course you'll learn the most important features of the library while building four different visualizations. Course content: Introduction (0:00) Selection and manipulation (1:59) Data loading and binding (5:25) Creating a simple bar chart (7:29) Creating labels (11:16) Scales (13:22) Axes (15:18) SVG elements (17:41) Creating a pie chart (20:32) Line charts (21:55) You can follow the course creator Sohaib Nehal on Twitter here: https://twitter.com/Sohaib_Nehal -- Learn to code for free and get a developer job: https://www.freecodecamp.com Read hundreds of articles on programming: https://medium.freecodecamp.com And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 53678 freeCodeCamp.org
download torrents with Python and Scrapy
 
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download torrents with Python and Scrapy
Views: 1706 shefali Mahadevan
Data Science from Scratch by Joel Grus: Review | Learn python, data science and machine learning
 
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This is a review of Data Science from Scratch by Joel Grus. This book will teach you the methods used for data science and machine learning. First it will show you the basics of the python language, then how to visualize data with matplotlib. It moves on to probability and statistics and then machine learning methods. After explaining the methods it starts to build up functions in python that will apply what has been learnt. It's an excellent introduction to Data Science. You can buy the book here:- https://amzn.to/2KIXzyo (USA) https://amzn.to/2FSIuqs (UK) (Affiliate links)
Views: 9624 Python Programmer
Data analysis in Python with pandas
 
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Wes McKinney The tutorial will give a hands-on introduction to manipulating and analyzing large and small structured data sets in Python using the pandas library. While the focus will be on learning the nuts and bolts of the library's features, I als
Views: 297628 Next Day Video
Complete Python: Go from zero to hero in Python
 
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Social Network for Developers ☞ https://morioh.com Aiodex’s Referral Program  will give you 20% -80% commission from their transaction fee for 7 years. The value will be calculated starting from the date the member you invite sign up ☞ http://vrl.to/c4099b4d9f Next Generation Shorten Platform: Optimal choice to make a profit and analyze traffic sources on the network. Shorten URLs and Earn Big Money ☞ https://viralroll.com/ Get Free 15 Geek ☞ https://geekcash.org/ Developers Chat Channel ☞ https://discord.gg/KAe3AnN Playlists Video Tutorial ☞ http://dev.edupioneer.net/f086e182ab Learn to code for free and get a developer job ☞ https://codequs.com/ Complete Python Bootcamp: Go from zero to hero in Python ☞ http://deal.codetrick.net/p/rkbzrt_Sl Complete Python Masterclass ☞ http://deal.codetrick.net/p/SkCfL7xbe The Python Bible™ | Everything You Need to Program in Python ☞ http://deal.codetrick.net/p/Skc18mgbl Learning Python for Data Analysis and Visualization ☞ http://deal.codetrick.net/p/HywrG7e-l Python for Financial Analysis and Algorithmic Trading ☞ http://deal.codetrick.net/p/BkBWKHZtb Python A-Z™: Python For Data Science With Real Exercises! ☞ http://deal.codetrick.net/p/HkzuOBrEg Are you brand new to coding? Want to see how fun and easy it can be? Watch engaging experts Susan Ibach and Christopher Harrison for an entertaining introduction to programming with Python. Susan and Christopher offer a step-by-step walk-through, from a basic idea to translating that idea into code, and everything in between. Don't worry about making mistakes! Python uses simple syntax, has an easy learning curve, and is a very forgiving language. Gain a new skill or complete a task by the end of each module, and, by the end of the course, you will be programming in Python! You also learn basic principles which can make it easier for you to learn other programming languages in the future. Don't miss this opportunity to go beyond the if statement! NOTE: To get the most out of this Python training course, before the session, be sure to download these free tools: Visual Studio Community and Python Tools for Visual Studio. If you're a student, you have access to Visual Studio Professional 2013, for free, through DreamSpark. Instructor | Susan Ibach - Microsoft Canada Technical Evangelist; Christopher Harrison -Microsoft Content Development Manager Getting Started Explore applications of Python language, and create a "Hello world" application for Python in Visual Studio, as you learn the benefits of knowing Python. Get help setting up your computer, so you can start coding. Displaying Text Get an introduction to the print statement, comments, and basic formatting, so you can display and format text to a user. String Variables Learn about the input statement, string variables, and manipulate strings, so you can prompt a user for input, store values in a string, and use string functions to manipulate string values. Storing Numbers Hear an introduction to numeric datatypes and variables, how to do math operations, and datatype conversions. Learn to store numeric values and perform math operations. Working with Dates and Times Get the details on date variable storage and issues, along with date functions and formatting, so you can store and manipulate date values. Making Decisions with Code Hear an introduction to basic if/else statements and Boolean variables, so you can write code that reacts differently to different user inputs. Complex Decisions with Code Explore and/or statements, nested if statements, and elif, so you can write code that reacts differently to more complex user inputs. Repeating Events Take a look at for loops and nested for loops, so you can write programming in Python that repeats a fixed number of times. Repeating Events Until Done Play with while loops, and learn when to use for versus while loops, so you can write code that repeats as often as needed. Remembering Lists Get the details on arrays and lists, so you can store multiple values. How to Save Information in Files Hear about functions for creating and writing to files, so you can write code that saves information in a file and remember it later. Reading from Files Explore functions for reading from files, so you can read information that was saved in a file. Functions Learn about the syntax for declaring functions and how to call functions from your code, so you can use functions to avoid retyping the same code over and over. Handling Errors Get the details on syntax for error handling, so you can write code that can handle common error situations without crashing. Learn Video source via: MVA ---------------------------------------------------- Website: https://goo.gl/XnM72d Website: https://goo.gl/AWpXfC Playlist: https://goo.gl/hnwbLS Fanpage: https://goo.gl/o6pVzp Twitter: https://goo.gl/UrBoeq Wordpress: https://goo.gl/qAJxMe Pinterest: https://goo.gl/GrRx7B Tumblr: https://goo.gl/6fTauh
Views: 16663 coderschool
Machine Learning Tutorial 2 - Intro to Predictive Data Analytics
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Intro to Predictive Analytics is the second video in this machine learning course. This video explains how machine learning algorithms are used in the field of data analytics to create models of reality. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 10580 Caleb Curry
Course Preview: Introduction to Data Visualization with Python
 
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View full course: https://www.pluralsight.com/courses/data-visualization-with-python-introduction Join Pluralsight author [NAME] as he/she walks you through a preview of his/her "COURSE TITLE" course found only on Pluralsight.com. Become smarter than yesterday with [AUTHORS]’s help by... Visit Pluralsight.com to start your free trial today to view this course in its entirety. Visit us at: Facebook: https://www.facebook.com/pluralsight Twitter: https://twitter.com/pluralsight Google+: https://plus.google.com/+pluralsight LinkedIn: https://www.linkedin.com/company/pluralsight Instagram: http://instagram.com/pluralsight Blog: https://www.pluralsight.com/blog
Views: 217 Pluralsight
3D graphs with NetworkX, VTK, and ParaView - Alex Razoumov - May 24, 2016
 
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This webinar was hosted by Alex Razoumov, WestGrid's Visualization Coordinator. Description: Options for 3D graph visualization and analysis are very limited, confined primarily to short-lived research projects or legacy tools that can still be downloaded but are no longer maintained and updated. The reason for this is the popularity of 2D tools such as Gephi and Cytoscape and the expectation that in 3D complex networks will look messy, with some structures occluding others. On the other hand, in 3D layouts we can encode three independent attributes and visualize some unique connection topologies that will be lost in 2D. To view the slides and other session details, visit: https://www.westgrid.ca/events/westgrid_online_workshop_3d_graphs_networkx_vtk_and_paraview For more information on other WestGrid training sessions, click here: https://www.westgrid.ca/events/westgrid-training-events
Views: 2345 WestGrid
17 - Exploratory Data Analysis | How to Win a Data Science Competition: Learn from Top Kagglers
 
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Lecture video from the course How to Win a Data Science Competition: Learn From Top Kagglers in the Advanced Machine Learning Specialization from the National Research University Higher School of Economics Download all the lecture notes of this course here: https://github.com/MrNewHorizons/StudyMaterials/tree/master/HowToWinDataScienceCompetition You can enroll in the course for a certificate here: https://www.coursera.org/learn/competitive-data-science
Views: 749 Hasan Shaukat
Ion Reporter Software and Server – Simplify your bioinformatics path to research results
 
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From human variant detection to microbial diversity, Ion Reporter™ Software provides an optimized suite of simple data analysis tools that streamline Ion PGM™ and Ion Proton™ System data analysis, so you can focus on finding the biological meaning of your data. Learn more at: http://www.lifetechnologies.com/us/en/home/life-science/sequencing/next-generation-sequencing/ion-torrent-next-generation-sequencing-workflow/ion-torrent-next-generation-sequencing-data-analysis-workflow/ion-reporter-software.html?icid=COAS?ICID=ta-lm-ion%20reporter%20software-Ion%20Reporter%20Software
Use Python to Load & Prepare Data Analytics
 
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Python Software Foundation Distinguished Service Award recipient Raymond Hettinger demonstrates how to use Python tools for loading and preparing data analytics. This lesson is an excerpt from his video course "Modern Python Programming: Big Ideas and Little Code in Python", which is designed to provide developers with an approach to programming in Python that expresses big ideas succinctly, with the minimum of code, allowing the business logic to shine through. Learn more and purchase course at informit.com/modernpython Save 50% with discount code YOUTUBE Also available in Safari subscription service.
Views: 4895 LiveLessons
Visualizing twitter discussions with networkx
 
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Visualizing twitter discussions with networkx Every day, millions of people use social media like Twitter or Facebook, to catch up on news and engage in discussions with other users. Our research group at Aalto University analyzes such discussions and studies how people interact on social media. Towards this end, visualization can be a very useful tool. In this talk, we use networkx, a python library, to process and visualize user interactions on Twitter. We introduce networkx with simple examples and continue with the visualization of twitter data. During the talk, we’ll see that discussions about polarized topics (e.g., elections) look quite different than non-polarised ones." About the authors: Kiran Garimella is a PhD student at the Department of Computer Science, Aalto University. For his PhD, Kiran studies controversy and polarization on social media. Earlier in his career, he worked as Research Engineer at Yahoo! Research and the Qatar Computing Research Institute, and Machine Learning / Data Science intern at LinkedIn and Amazon. Michael Mathioudakis is a postdoctoral researcher at the Department of Computer Science, Aalto University. His research focuses on social media, social networks, and urban computing. Earlier, Michael did a PhD at the University of Toronto, and worked as Research Intern at Microsoft and Yahoo! Research. He is also currently working as a Data Scientist at Sometrik.
Views: 2224 PyCon Finland
Data analysis on PM Modi activities Kerala floods relief - TV9
 
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Watch #VinayaVidheyaRama LIVE : https://www.youtube.com/watch?v=D7Nyfrl9GCk For More from #RamCharan's #VVR : https://goo.gl/U4FmD8 Watch #NTRKathanayakudu Audio LIVE: https://www.youtube.com/watch?v=XXx3TLBF-6I Watch #TelanganaElectionCounting LIVE: https://www.youtube.com/watch?v=v-bZONX2rp4 #TelanganaElectionResults2018 LIVE updates: https://goo.gl/rAxF6G #TelanganaElectionResultsOnTV9 Data analysis on PM Modi activities Kerala floods relief - TV9 ► Download Tv9 Android App: http://goo.gl/T1ZHNJ ► For More: https://goo.gl/UC3Yjq ► Circle us on G+: https://plus.google.com/+tv9 ► Like us on Facebook: https://www.facebook.com/tv9telugu ► Follow us on Twitter: https://twitter.com/Tv9Telugu ► Pin us on Pinterest: https://www.pinterest.com/Tv9telugu
Views: 956 TV9 Trending
The Complete MATLAB Course: Beginner to Advanced!
 
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Get The Complete MATLAB Course Bundle for 1 on 1 help! https://josephdelgadillo.com/product/matlab-course-bundle/ Enroll in the FREE Teachable course! https://uthena.com/courses/matlab?ref=744aff Time Stamps 00:51 What is Matlab, how to download Matlab, and where to find help 07:52 Introduction to the Matlab basic syntax, command window, and working directory 18:35 Basic matrix arithmetic in Matlab including an overview of different operators 27:30 Learn the built in functions and constants and how to write your own functions 42:20 Solving linear equations using Matlab 53:33 For loops, while loops, and if statements 1:09:15 Exploring different types of data 1:20:27 Plotting data using the Fibonacci Sequence 1:30:45 Plots useful for data analysis 1:38:49 How to load and save data 1:46:46 Subplots, 3D plots, and labeling plots 1:55:35 Sound is a wave of air particles 2:05:33 Reversing a signal 2:12:57 The Fourier transform lets you view the frequency components of a signal 2:27:25 Fourier transform of a sine wave 2:35:14 Applying a low-pass filter to an audio stream 2:43:50 To store images in a computer you must sample the resolution 2:50:13 Basic image manipulation including how to flip images 2:57:29 Convolution allows you to blur an image 3:02:51 A Gaussian filter allows you reduce image noise and detail 3:08:55 Blur and edge detection using the Gaussian filter 3:16:39 Introduction to Matlab & probability 3:19:47 Measuring probability 3:26:53 Generating random values 3:35:40 Birthday paradox 3:43:25 Continuous variables 3:48:00 Mean and variance 3:55:24 Gaussian (normal) distribution 4:03:21 Test for normality 4:10:32 2 sample tests 4:16:28 Multivariate Gaussian
Views: 1060697 Joseph Delgadillo
The Four Pillars of OOP in Python 3 for Beginners
 
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Social Network for Developers ☞ https://morioh.com Aiodex’s Referral Program  will give you 20% -80% commission from their transaction fee for 7 years. The value will be calculated starting from the date the member you invite sign up ☞ https://aiodex.com/p/referral-program Next Generation Shorten Platform: Optimal choice to make a profit and analyze traffic sources on the network. Shorten URLs and Earn Big Money ☞ https://viralroll.com Get Free 15 Geek ☞ https://geekcash.org Developers Chat Channel ☞ https://discord.gg/KAe3AnN Playlists Video Tutorial ☞ http://dev.edupioneer.net/f086e182ab Complete Python Bootcamp: Go from zero to hero in Python 3 ☞ http://deal.codetrick.net/p/S15_M7e-l Complete Python Masterclass ☞ http://deal.codetrick.net/p/SkCfL7xbe The Python Bible™ | Everything You Need to Program in Python ☞ http://deal.codetrick.net/p/Skc18mgbl Python and Django Full Stack Web Developer Bootcamp ☞ http://deal.codetrick.net/p/r1-quFMgce Learning Python for Data Analysis and Visualization ☞ http://deal.codetrick.net/p/HywrG7e-l Python for Financial Analysis and Algorithmic Trading ☞ http://deal.codetrick.net/p/BkBWKHZtb Python OOP Simplified: Learn Object Oriented Programming using Python in a way that you really understand Learn to structure your Python code by making use of Classes and Objects. In this course you will learn how to achieve object oriented programming in Python by learning how to bundle attributes and methods within a class and instantiating them through an object. You will learn about the four pillars that hold together the object oriented programming, which are: Abstraction Encapsulation Polymorphism Inheritance At the end of this course, you will be able to write your own object oriented programs in Python! Who is the target audience? Students who would like to enhance their Python skills by learning the basics of object oriented programming Video source viva: Udemy ---------------------------------------------------- Website: http://bit.ly/2pN2aXx Playlist: http://bit.ly/2Eyn3dI Website: http://bit.ly/2Hay229 Fanpage: http://bit.ly/2qi5j1A Twitter: http://bit.ly/2GOyTlA Pinterest: http://bit.ly/2qihWtz Tumblr: http://bit.ly/2qjBcGo
Views: 2148 Learn4Startup
Big Data tutorial by Marko Grobelnik
 
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VideoLectures.Net View the talk in context: http://videolectures.net/eswc2012_grobelnik_big_data/ View the complete ESWC summer school: http://videolectures.net/eswc2012_summer_school/ View blog: http://blog.videolectures.net/deconstructing-big-data/. Speaker: Marko Grobelnik Artificial Intelligence Laboratory, Jožef Stefan Institute License: Creative Commons CC BY-NC-ND 3.0 More information at http://videolectures.net/site/about/ More talks at http://videolectures.net/ Big data applies to information that can't be processed or analyzed using traditional processes or tools. IBM claims in its 2012 report that every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. 0:00 Big - Data Tutorial 0:35 Outline 1:18 Big data - a growing torrent 3:00 Big data - capturing its value 3:38 What is Big - Data? 4:41 Characterization of Big - Data 5:39 Big - Data popularity on the Web 7:47 Big - Data in Gartner Hype - Cycle 2011 9:05 Why Big - Data? 9:34 Enabler: Data storage 10:35 Enabler: Computation capacity 12:04 Enabler: Data availability 13:27 Type of available data 14:40 Data available from social networks and mobile devices 16:09 Data available from ''Internet of Things'' 17:43 Big - Data value chain 18:52 Gains from Big - Data per sector 21:54 Predicted lack of talent for Big - Data related technologies 23:16 Big - Data value chain 24:40 Tools 24:43 Types of tools typically used in Big - Data scenarios 27:02 Distributed infrastructure 28:31 Distributed processing 30:52 MapReduce 32:30 High - performance schema - free databases 35:31 Techniques 35:33 When Big - Data is really a hard problem? 38:39 What matters when dealing with data? 42:39 Meaningfulness of Analytic Answers (1/2) 43:58 Meaningfulness of Analytic Answers (2/2) 48:09 What are ''atypical'' operators on Big - Data 51:12 Analytical operators on Big - Data 51:28 What are ''atypical'' operators on Big - Data 53:06 Analytical operators on Big - Data 53:51 ...guide to Big - Data algorithmics 54:34 Applications 54:46 Application: Recommendation 55:47 The context of each click on the web site used for recommendation 56:33 Application: Online Advertising for NYTimes 56:59 Scale of one day NYTimes data 57:21 Application: Telecommunication Network Monitoring 59:07 Application: Monitoring global main stream news 59:12 http://newsfeed.ijs.si/ 59:51 Semantic text enrichment (DBpedia, OpenCyc, ...) with Enrycher 60:10 Application: Text visualization 60:15 Application: Analysis of MSN - Messenger Social - network 61:00 Data Statistic: Total activity 61:50 Who talks to whom: Number of conversations 62:33 Who talks to whom: Conversation duration 63:14 Geography and communication 63:48 How is Europe talking 64:10 Network: Small - word 66:28 Literature on Big - Data 67:42 ...to conclude
Views: 7972 VideoLecturesChannel
4) Next Generation Sequencing (NGS) - Data Analysis
 
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For more information on Next Generation Sequencing analyses and for a list of the sources used, please visit: ➜ Knowledge Base: https://goo.gl/Ce0M4O What is covered in this video: ➜ Previous videos in our Next Generation Sequencing (NGS) series describe the theory and technology of NGS platforms (https://youtu.be/jFCD8Q6qSTM), and the steps of library preparation for sequencing on the Illumina platform (https://youtu.be/-kTcFZxP6kM). In this installment we describe some of the common formats of NGS raw data and software that can be used for downstream analysis. Watch the other videos in this series on NGS: ➜ Introduction: https://youtu.be/jFCD8Q6qSTM ➜ Sample Preparation: https://youtu.be/-kTcFZxP6kM ➜ Coverage & Sample Quality Control: https://youtu.be/PGAfwSRYv1g ➜ NGS Playlist: https://youtu.be/jFCD8Q6qSTM?list=PLTt9kKfqE_0Gem8hIcJEn7YcesuuKdt_n Connect with us on our social media pages to stay up to date with the latest scientific discoveries: ➜ Facebook: https://goo.gl/hc9KrG ➜ Twitter: https://goo.gl/gGGtT9 ➜ LinkedIn: https://goo.gl/kSmbht ➜ Google+: https://goo.gl/5bRNwC
Как сделать первое приложение в Microsoft Power BI - что такое курсы Power BI, анализ продаж
 
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Как работать с Microsoft Power BI? Как сделать первый dashboard в Power BI - информация, курсы, скачать Power BI можно на https://www.biconsult.ru Как сделать модель данных? Разработка отчетов и хранилища на MS Power BI. Уроки, материалы, учебные курсы по Microsoft Power BI power bi, power bi desktop, microsoft power bi, power bi скачать, power bi отчеты, power bi reports, power bi server, power bi report server, power bi excel, power bi обучение, ms power bi, dax power bi, power bi примеры, power bi pro, power bi desktop скачать, курс power bi, визуализации power bi, power bi на русском, аналитик power bi, power bi аналитика, power bi power query, power bi мера, power bi купить, r power bi, power bi analytics, power bi карта, power bi sql, интеграция power bi, power bi шлюз, power bi настройка, функции power bi, power bi бесплатно, power bi диаграммы, power bi формулы, power bi обновление, power bi sharepoint, power bi service, power bi table, power bi цена, дашборд power bi, power bi filter, power bi график, power bi сделать, программа power bi, power bi 1c, power bi download, power bi api, power bi для интернет маркетинга, power bi торрент, power bi google analytics, power bi метрика, отзывы power bi, power bi аналоги, power bi gateway, power bi на русском скачать, power bi директ, power bi возможности, power bi как работать, power bi стоимость, microsoft power bi desktop, power bi visuals, power bi map, power bi яндекс директ, power bi сравнение, power bi embedded, использование power bi, power bi презентация, power bi внедрение, power bi примеры отчетов, power bi установка, power bi dashboard, power bi to pdf, power bi premium, видео power bi, power bi вход, power bi и 1с, power bi torrent, power bi источники данных, power bi web, power bi уроки, power bi azure, power bi яндекс метрика, power bi уваров, power bi лицензии, power bi книга, power bi лицензирование, app power bi, power bi desktop на русском, power bi инструкция, microsoft power bi скачать, power bi учебник, power bi закладки, power bi скачать бесплатно, дата в power bi, power bi desktop на русском скачать, power bi sql server, power bi сквозная аналитика, power bi регистрация, power bi facebook, power bi облако, power bi update, power bi обновление данных, power bi учетная запись, power bi поиск, power bi создать таблицу, power bi reporting services, power bi форум, power bi blog, power bi параметры, profile powered by discuz bi basic, power bi локальный шлюз, power bi olap, power bi визуальные элементы, power bi com, power bi amocrm, bi quiet power, bi power shot, care glamour bi power, bi power keratin treatment, power bi шаблоны, майкрософт power bi, power bi python, power bi условное форматирование, power bi calculate, power bi курс скачать, power bi mobile, связи в power bi, power bi примеры дашбордов, панель мониторинга power bi, power bi публикация отчетов, power bi examples, power bi gallery, care glamour bi power keratin treatment, power bi measure, power bi office 365, power bi специалист, power bi report server лицензирование, как установить power bi, power bi svg, power bi для анализа продаж, power bi преимущества, power bi для интернет маркетинга скачать, сервер отчетов power bi, power bi r script, power bi online, power bi контекстная реклама, визуализации power bi скачать, power bi группировка, power bi on premise, руководство power bi, power bi уваров скачать, кнопки в power bi, kpi power bi, power bi сервис, power bi excel 2016, dax формулы power bi, power bi описание, обновить power bi, power bi visual gallery, power bi битрикс 24, fish power bi, power bi mysql, power bi postgresql, microsoft power bi pro, power bi desktop учебник, битрикс24 коннектор power bi, power bi visualization, power bi картинки, power bi карта россии, power bi desktop download, служба power bi, вакансии power bi, power bi википедия, работа в power bi, power bi mac os, comagic power bi, power bi для интернет маркетинга торрент, power bi svg map, power bi календарь, power bi vk, power bi обзор, курсы по power bi в москве, компании power bi, power bi concatenate, power bi настройка шлюза, power bi publisher, power bi видео уроки, power bi youtube, adwords power bi, power bi русский язык, сравнение периодов power bi, power bi report server скачать, безопасность в power bi, power bi онлайн, power bi детализация, интеграция битрикс24 с power bi, power bi для seo, power bi custom visuals, power bi обучение на русском, mcsa сертификат power bi
Data Science Tutorial for Beginners - 1 | What is Data Science? | Data Analytics Tools | Edureka
 
02:32:56
( Data Science Training - https://www.edureka.co/data-science ) Data Science Blog Series: https://goo.gl/1CKTyN http://www.edureka.co/data-science Please write back to us at [email protected] or call us at +91-8880862004 for more information. Data Science is all about extracting knowledge from data. Data Science is the integration of methods from mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modelling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. This interdisciplinary and cross-functional field leads to decisions that move an organization forward in terms of proposed investment, decisions regarding a product or business strategy. Data Science is a buzzword, often used interchangeably with analytics or big data. At times, Analytics is synonymous with Data Science, but at times it represents something else. A Data Scientist using raw data to build a predictive behaviour model, falls in to the category of analytics. About the Data Science Course at edureka! - This Data Science course is designed to provide knowledge and skills to become a successful Data Scientist. The course covers a range of Hadoop, R and Machine Learning Techniques encompassing the complete Data Science study. Course Objectives After the completion of the Data science Course at Edureka, you should be able to: Gain an insight into the 'Roles' played by a Data Scientist. Analyse Big Data using Hadoop and R. Understand the Data Analysis Life Cycle. Use tools such as 'Sqoop' and 'Flume' for acquiring data in Hadoop Cluster. Acquire data with different file formats like JSON, XML, CSV and Binary. Learn tools and techniques for sampling and filtering data, and data transformation. Understand techniques of Natural Language Processing and Text Analysis. Statistically analyse and explore data using R. Create predictive using Hadoop Mappers and Reducers. Understand various Machine Learning Techniques and their implementation these using Apache Mahout. Gain insight into the visualisation and optimisation of data. Who should go for this course? This course is designed for all those who want to learn machine learning techniques and wish to apply these techniques on Big Data. The course is amalgamation of two powerful open source tools: 'R' language and Hadoop software framework. You will learn how to explore data quantitatively using tools like Sqoop and Flume, write Hadoop MapReduce Jobs, perform Text Analysis and implement Language Processing, learn Machine Learning techniques using Mahout, and optimize and visualize the results using programming language 'R' and Apache Mahout. This course is for you if you are: A SAS, SPSS Analytics Professional. A Hadoop Professional working on Database management and streaming of Big Data. An 'R' professional who wants to apply Statistical techniques on Big Data. A Statistician who wants to understand Data Science methodologies to implement the statistics methods and techniques on Big data. Any Business Analyst who is working on creating reports and dashboards. Pre-requisites Some of the prerequisites for learning Data Science are familiarity with Hadoop, Machine Learning and knowledge of R (recommended not mandatory as these concepts will also be covered during the course). Also, having a statistical background will be an added advantage. Why Learn Data Science? 'Data Science' is a term which came into popularity in past decade. Data Science is the process of extracting valuable insights from "data". It is the right time to learn Data science because: We are living in the Big Data Era, Data Science is becoming a very promising field to harness and process huge volumes of data generated from various sources. A data scientist has a dual role -- that of an "Analyst" as well as that of an "Artist"! Data scientists are very curious, who love large amount of data, and more than that, they love to play with such huge data to reach important inferences and spot trends. You could be one of them! As 'Data Science' is an emerging field, there is a plethora of opportunities available world across. Just browse through any of the job portals; you will be taken aback by the number of job openings available for Data scientists in different industries, whether it is IT or healthcare, Retail or Government offices or Academics, Life Sciences, Oceanography, etc. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 212971 edureka!
Power BI Tutorial for Beginners - Getting Started
 
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You want to analyze data, create your individual datasets and create beautiful and easy-to-make visualizations? Then the Power BI tools are the tools to choose for you! Have a look at the First Hour of my 10 Hour "Microsoft Power BI - A Complete Introduction" Course. ---------- Join the entire 10 Hour course on Udemy for only $14: https://www.udemy.com/powerbi-complete-introduction/?couponCode=YOUTUBE_BI ---------- • You can follow Max on Twitter (@maxedapps). • You can also find us on Facebook.(https://www.facebook.com/academindchannel/) • Or visit our Website (https://www.academind.com) and subscribe to our newsletter! See you in the videos!
Views: 85009 Academind
Making great tasting dog food with Python
 
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How we are using social network analysis to understand palatability in dog food recipes Lorna Brightmore Sunday 16th, 11:30 (Room A) A talk (25 minutes) I’m a data scientist working for a tailor made dog food company, and in this session I’ll explain how we are applying ideas in social network analysis to understand and improve the overall taste and palatability of our dry dog food recipes. Understanding what makes dog food delicious is a difficult problem to solve, and as the intended consumers of our food are dogs, this presents some obvious limitations to the methods available to approach this problem. I am taking a fairly unique and data led approach to this, using ideas in social network analysis to visualise our dog food recipes and find ingredients (or combinations of ingredients) common in recipes where we know that the dog didn’t like the recipe. In this talk, I’ll tell the story of how this project evolved, and how we’ve been using the Python library NetworkX to help us create some really insightful visualisations of our data. Along the way I’ll cover some of the great things about this library, as well as some of the pitfalls. The speaker suggested this session is suitable for data scientists.
Views: 64 PyCon UK
Tutorial: Applied Data Science in Python
 
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Tennessee Leeuwenburg https://linux.conf.au/schedule/30020/view_talk Ever tried to get into data science or machine learning, but struggled with getting your tech stack working, or found the maths off-putting? Curious about the limits of what your laptop or desktop really are when it comes to Big Data and predictive analytics? Ever wondered if these tools were really accessible to a general developer? This tutorial will provide attendees with a walkthrough on getting set up for this work, and an overview of a good tech stack / software ecosystem for beginning work. We'll cover some of the standard data sets in machine learning, and how to apply interesting algorithms. Random Forests and neural networks will be included, but with a minimum of fuss and jargon. There will be a focus on the application of technology, with only the most relevant theoretical aspects included. This is about actually getting things done. This tutorial would be suitable for intermediate developers of any background, or experienced developers who would like an introduction to data science that gets to the point fast. Prerequisites: the ability to install Python modules on your laptop, the ability to set up a new virtual environment, and an interest in applying new techniques. The tutorial will include clear walkthroughs, as well as allowing adequate time for discussion and individual learning. Please contact Tennessee via email ahead of time if you would like to get a head start on setting up your environment -- this may help you get more out of the tutorial.
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 433787 sentdex
Sampling & Probability | Learning Statistics: Concepts and Applications in R | The Great Courses
 
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Study sampling and probability, which are key aspects of how statistics handles the uncertainty inherent in all data. See how sampling aims for genuine randomness in the gathering of data, and probability provides the tools for calculating the likelihood of a given event based on that data. Solve a range of problems in probability, including a case of medical diagnosis that involves the application of Bayes’ theorem. This free full-length lecture comes from the course Learning Statistics: Concepts and Applications in R. Learn more about this course and start your FREE trial of The Great Courses Plus here: https://www.thegreatcoursesplus.com/show/learning_statistics_concepts_and_applications_in_r?utm_source=US_OnlineVideo&utm_medium=SocialMediaEditorialYouTube&utm_campaign=151291 About this course: “Show me the data!” This is coin of the realm in science, medicine, business, education, journalism, and countless other fields. Of course, it’s more complicated than that, because raw data without interpretation is useless. What they mean is “Show me the statistics”—well-founded, persuasive distillations of data that support a claim under discussion. The ability of statistics to extract insights from a random collection of facts is one of the most astonishing and useful feats of applied mathematics. That power is all the more accessible today through the statistical programming language R, a free, open-source computer language with millions of users worldwide—everyone from students and nonprofessionals to managers and researchers at the forefront of their disciplines. In this era of big data, with a solid understanding of statistics and the tools for interpreting data, you don’t have to trust someone else’s analysis of medical treatments, financial returns, crop yields, voting trends, home prices, or any other interpretation of data. You can do it yourself. Designed for those who appreciate math or want an introduction to an essential toolkit for thinking about the uncertainty inherent in all sorts of information, Learning Statistics: Concepts and Applications in R teaches you elementary statistical methods and how to apply them in R, which is made even more powerful when combined with the user interface of RStudio. (Both R and RStudio are free and downloadable for multiple platforms.) In 24 challenging and in-depth half-hour lectures, award-winning Professor Talithia Williams of Harvey Mudd College walks you through major concepts of an introductory college-level statistics course, and beyond, using examples developed and presented in R. Compared with “canned” statistics packages, R brings users into a more hands-on, mind-engaging approach that is becoming the standard at top-tier statistics programs throughout the country. An Associate Professor of Mathematics and the Associate Dean for Research and Experiential Learning at Harvey Mudd, Dr. Williams is a nationally recognized innovator in statistics education, noted for her popular TED Talk, “Own Your Body’s Data,” and she is cohost of the PBS NOVA series NOVA Wonders. Learn more about this course and start your FREE trial of The Great Courses Plus here: https://www.thegreatcoursesplus.com/show/learning_statistics_concepts_and_applications_in_r?utm_source=US_OnlineVideo&utm_medium=SocialMediaEditorialYouTube&utm_campaign=151291
Copy and paste visuals in Power BI | Power Week 11.18 - Power BI Desktop Update November 2018
 
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With the Power BI Desktop Update November 2018 release, you can now copy a visual either through the visual’s context menu or through the standard Ctrl+C keyboard shortcut and paste it into another report through Ctrl+V. This is very useful for anyone who builds and updates multiple reports frequently. When copying between files, formatting that has been explicitly set in the formatting pane will carry forward, and anything that is relying on a theme or the default settings will update to match the theme of the destination report. If the fields in your model are different, you’ll see an error on the visual and a warning on the fields that don’t exist, similar to the experience you see if you delete a field in the model a visual is using. All you’ll need to do is replace the broken fields with the ones you want to use from the new model. If you are using a custom visual, you’ll also need to import it to the destination file as well. Here you can download all the pbix files: https://curbal.com/donwload-center SUBSCRIBE to learn more about Power and Excel BI! https://www.youtube.com/channel/UCJ7UhloHSA4wAqPzyi6TOkw?sub_confirmation=1 Our PLAYLISTS: - Join our DAX Fridays! Series: https://goo.gl/FtUWUX - Power BI dashboards for beginners: https://goo.gl/9YzyDP - Power BI Tips & Tricks: https://goo.gl/H6kUbP - Power Bi and Google Analytics: https://goo.gl/ZNsY8l ☼☼☼☼☼☼☼☼☼☼ POWER BI COURSES: Want to learn Power BI? How about you take one of our courses? Here you can find the available courses: https://curbal.com/courses-overview ☼☼☼☼☼☼☼☼☼☼ ABOUT CURBAL: Website: http://www.curbal.com Contact us: http://www.curbal.com/contact ▼▼▼▼▼▼▼▼▼▼ If you feel that any of the videos, downloads, blog posts that I have created have been useful to you and you want to help me keep on going, here you can do a small donation to support my work and keep the channel running: https://curbal.com/product/sponsor-me Many thanks in advance! ▲▲▲▲▲▲▲▲▲▲ QUESTIONS? COMMENTS? SUGGESTIONS? You’ll find me here: Linkedin ► https://goo.gl/3VW6Ky Twitter ► @curbalen, @ruthpozuelo Facebook ► https://goo.gl/bME2sB
Views: 900 Curbal
Webinar: Using Python and LabVIEW to Rapidly Solve Engineering Problems | Enthought
 
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Engineers and scientists all over the world are using Python and LabVIEW to solve hard problems in manufacturing and test automation, by taking advantage of the vast ecosystem of Python software. But going from an engineer’s proof-of-concept to a stable, production-ready version of Python, smoothly integrated with LabVIEW, has long been elusive. View the live webinar and demo, as we take a LabVIEW data acquisition app and extend it with Python’s machine learning capabilities, to automatically detect and classify equipment vibration. Using a modern Python platform and the Python Integration Toolkit for LabVIEW (https://www.enthought.com/python-for-labview), we’ll show how easy and fast it is to install heavy-hitting Python analysis libraries, take advantage of them from live LabVIEW code, and finally deploy the entire solution, Python included, using LabVIEW Application Builder. In this webinar we'll demonstrate: *How Python’s machine learning libraries can simplify a hard engineering problem *How to extend an existing LabVIEW VI using Python analysis libraries *How to quickly bundle Python and LabVIEW code into an installable app
Views: 9404 Enthought
Chip seq (chromatin immuno-precipitation followed by sequencing)
 
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This video describes the basic concepts and use of chromatin immunoprecipitation in a concise manner and also describes how this technique is used to detect DNA protein interaction in Vivo.
Views: 4909 Arpan Parichha
RNA-Seq Analysis with Partek Flow Software
 
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This seven-minute video demonstrates how to analyze an RNA-Seq data set using simple point-and-click actions. Steps include alignment, quantification, quality control, statistical visualization, and gene ontology.
Views: 1215 PartekIncorporated
QIIME Workshop & نظرية التطور : PROMO 2
 
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CONTACTS: Email: [email protected] EVENT Facebook Page: https://www.facebook.com/events/1088714877822325/ Attendance Form: http://goo.gl/forms/86m4E2d9kl Facebook Page: https://www.facebook.com/Advancedscienec?fref=ts Instructor Facebook: https://www.facebook.com/abdelrahman.mahmoud.5 THE WORKSHOP PLACE: Faculty of Postgraduate Studies Of Advanced Sciences Beni Suef University, Egypt. BRIEF INFO ABOUT OUR WORKSHOP : We will present an exciting workshop on Egypt which you will learn "How to analyse your 16S rRNA sequencing data by QIIME Software?". QIIME is one of the most advanced softwares on the metagenomics research field. Also, we will cover a variety of topics such as Evolution theory, Metagenomics, Human Microbiome Project, Bioinformatics basics, Next Generation Sequencing technologies, Linux operating system and we will perform a practical tutorial by QIIME SOFTWARE using a real next generation sequencing data. References: The Origin Of Species: http://www.amazon.com/The-Origin-Of-Species-Anniversary/dp/0451529065 Discovery of DNA Structure and Function: Watson and Crick: http://www.nature.com/scitable/topicpage/discovery-of-dna-structure-and-function-watson-397 Bioinformatics: http://bioinfo.mbb.yale.edu/mbb452a/intro/ Metagenomics: http://www.nature.com/nrmicro/posters/metagenomics/index.html Keyboard-microbiome: http://www.wired.com/2010/03/keyboard-microbiome/ Community genomics in microbial ecology and evolution: http://www.nature.com/nrmicro/journal/v3/n6/full/nrmicro1157.html Metagenomics - a guide from sampling to data analysis: http://www.microbialinformaticsj.com/content/2/1/3 A framework for human microbiome research: http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html Human Microbiome Project (HMP): http://hmpdacc.org/ QIIME SOFTWARE: http://qiime.org/ QIIME allows analysis of high-throughput community sequencing data: http://www.nature.com/nmeth/journal/v7/n5/full/nmeth.f.303.html
Views: 5573 Abdelrahman Mahmoud
Torrents and peers - SocialLinks Maltego Transforms
 
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Torrents to peers ip-addresses. And vice versa. For more details visit https://mtg-bi.com.
Views: 479 Social Links
COP5859 Semantic Web Programming Class Project
 
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∞ project leader and primary Python code developer ∞ created mobile application using Android Studio that would gather pedometer data ∞ adapted mobile application to send data via Bluetooth or email to Raspberry Pi to be analyzed ∞ created Python application that automatically recognizes and obtains data sent from mobile app ∞ adapted Python application to automatically connect to email server to download attachment files in case Bluetooth would not be available ∞ adapted Raspberry Pi to connect to Arduino module via use of it's 40 pin interface ∞ based on data analysis output Python application running on Raspberry Pi would control air fan ∞ stored data analysis in SQL database file and Semantic onotology file database for future reference
Views: 85 Maciej Medyk
Visualization of dpkg's git commit history - Apr 1996 to Aug 2012
 
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This 3 minutes video shows 16 years of dpkg development from version 1.1.4 to version 1.16.8. It includes over 6900 commits from 146 different contributors. More figures in this article: http://raphaelhertzog.com/2012/08/13/looking-back-at-16-years-of-dpkg-history-with-some-figures/
Views: 7687 Raphaël Hertzog
Genome Assembly Scaffold Bridging Visualization
 
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Bridging of genome assembly scaffolds with long PacBio reads. Red nodes: mitochondrial genome scaffolds; blue nodes: PacBio reads. Lines connect DNA sequences with 1200 nt overlap or longer. Visualized with PhyloGrapher http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html
Views: 452 Alexander Kozik
Python Processing Magnet Part III
 
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Recorded with http://screencast-o-matic.com
Views: 27 Christopher Roche
Bioinformatics Core
 
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Description This tool recursively align sequenced files to any number of index files in given order. When the sequenceces are aligned to an index file, the rest of the sequences are going to align to the next index file. For each alignment, the pipeline produces a stats file, an alignment file, quant file for each fasta sequence of given index file and the rest of the data. Input Dir: Full path of the input directory that includes the libraries. Input Params: Input files are given in two or three columns for the libraries. If the files are paired end enter three columns. First column is the name of the library, second column is the location of the first pair and third is the the second pair. If there is single end libraries, use two columns. The name of the library and the location of the file in the cluster. You can use 'DIR' keyword to denote 'Input Dir' to prevent repetition of entering full path for each libraries. If you give the same name for more than one library, the pipeline will merge them. Index File Full Path: This is the index file that is prepared using bowtie-build. It requires the fullpath and the name of the index file. without extensions. Name of the step: The name of the index file. This name will be used generated outputs. Ex:outdir/libname.indexname.stats Bowtie Parameters: Bowtie parameters for this individual step. Description: This is the description of the step. This naming will be used to produce summary file and calculation of the total counts to mapped to this step. Filter Out: To filter out the reads and move on to next step. This should be selected yes. If it is no, the sequences will be used without filtered out in the next step. So, if this step selected no, there can be overcount in summary tables.
Phase 1 - Recon - Part3 Appreciating Hacking Tools
 
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Before we start off on our journey of learning how to pentest a network, we need to gain an understanding of what it takes to create a pen test tool. We should not strive to be script kiddies, but understand and appreciate the tools that get created for us by some very clever people. The first few videos in this series will focus on creating a recon tool that scans a website and extracts email addresses. Very easy to do, but considering the small footprint we are covering it should show you just how much more is required to create something useful for the communit baring in mind that this is only one phase of pentesting that requires tools, there are many more areas where awesome tools are created. The purpose of these introductory videos will to, hopefully, help you to see just what it really takes to create great tools, make sure you atleast get the concept behind the recon tools we will use on Kali, instead of just running something and getting a result... and entice you to join the community in a more productive manner. Some of the Links I used to code the python-webcrawler.py program: http://null-byte.wonderhowto.com/inspiration/basic-website-crawler-python-12-lines-code-0132785/ http://jakeaustwick.me/python-web-scraping-resource/ http://scraping.pro/simple-email-crawler-python/ http://docs.python-guide.org/en/latest/scenarios/scrape/ https://www.crummy.com/software/BeautifulSoup/bs4/doc/ http://www.pythonforbeginners.com/beautifulsoup/scraping-websites-with-beautifulsoup http://resources.infosecinstitute.com/search-engine-hacking-manual-and-automation/ Disclaimer: You learn and use the information in these videos at your own discretion. You are responsible for anything you do with the information you learn in these videos. I am not responsible for any choices you make! Just be smart okay. own up to your own shitty mistakes that you choose to make. Code from the Video: #!/usr/bin/python from bs4 import BeautifulSoup import sys import re import requests # Lets create a class and function relevant to this application class MyWebPageCrawler(): 'Custom Webpage Crawler' # Globals DOMAINTOSEARCH = "" # Constructor # Functions # Get user input. (make sure to do the correct checks on user input !) def getDomainFromUser(self): global DOMAINTOSEARCH DOMAINTOSEARCH = raw_input("Enter the link: ") # call openUrl Function to open a URL ( we will get to this now) soup = self.openUrl(DOMAINTOSEARCH) return soup # Open the URL requested from the user def openUrl(self, urlToOpen): # Call requests to get the page page = requests.get(urlToOpen) # Convert the page into something BS4 can understand soup = BeautifulSoup("".join(page), 'html.parser') return soup # Find the links on the page def findLinks(self, soup, domainToSearch): print "[*][*] Please wait. Searching Site for links..." # find all links using the soup variable we created from our page allHref = soup.find_all("a") hrefList = [] for href in allHref: # get the link text from the actual "a" tag href = href.get('href') # add it to the list hrefList.append(href) # Helps if you print the results to screen print href return hrefList # Find all the emails on each link def findEmails(self, hrefList): print "[*][*] Please wait. Finding Emails...." for href in hrefList: page = self.openUrl(href) allEmails = set(re.findall( r"[a-z0-9\.\-+_][email protected][a-z0-9\.\-+_]+\.[a-z]+", page.text, re.I )) print "[*][*] -: Seaching Links Complete...." print "[*][*] -: Printing matched Regular Expressions" for email in set(allEmails): print email # put all your code here in main def main(self): # Get user input soup = self.getDomainFromUser() # find links from users input linksFound = self.findLinks(soup, DOMAINTOSEARCH) # find emails from links self.findEmails(linksFound) # Some basic stuff here calling main if __name__=='__main__': crawler = MyWebPageCrawler() crawler.main() sys.exit
SARscape Tools for ArcGIS - набор инструментов для работы
 
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Дата проведения: 25.10.2012 г. Ведущий: Кантемиров Юрий Игоревич
Views: 645 SOVZOND
Internet Traffic Graph: #1 Way To Promote And Boost Web Traffic
 
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Need Help promoting your website link? http://www.SpinSuccess3.com SpinSuccess is the #1 method of marketing Website links and any other url that needs traffic, sales and signups. http://www.SpinSuccess3.com Internet Traffic Graph
Tutorial - An Introduction to Data Visualization with Python
 
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Access +100 programming courses in Zenva: https://academy.zenva.com/?zva_src=youtube This course will provide you with an introduction to Data Visualization through Python. We’ll cover different techniques that will allow us to visualize data using Matplotlib. The course begins with an introduction to statistics — which we’ll need to understand some of the plots taught later. Following that, we’ll move on to learning about several types of plots that should cover most use-cases. Types of plots that will be covered in this course include Bar charts Line plots Scatter plots Advanced plots such as Quiver plots, 3D lines, and 3D surfaces Subplots Our tutorial blogs: GameDev Academy: https://gamedevacademy.org HTML5 Hive: https://html5hive.org Android Kennel: https://androidkennel.org Swift Ludus: https://swiftludus.org De Idea A App: https://deideaaapp.org Twitter: @ZenvaTweets
Views: 3295 Zenva
Creating WebSites using Python and Flask
 
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Views: 1981 coderschool
How to build Interactive Excel Dashboards
 
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Download file used in the video with step by step instructions and links to more tutorials: https://www.myonlinetraininghub.com/workbook-downloads In this video you will learn how to create an interactive dashboard from scratch using the built in Excel tools. No add-ins or VBA/Macros. Just plain Excel. Applies to Excel 2007 onward for Windows & Excel 2016 onward for Mac. Subscribe to my free newsletter and get my 100 Tips & Tricks eBook here: https://www.myonlinetraininghub.com/sign-up-for-100-excel-tips-and-tricks
Views: 1779683 MyOnlineTrainingHub
Fun and Easy Machine Learning Course in Keras and Python (Coupon Code in Description)
 
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Fun and Easy Machine Learning Course in Keras and Python Promotional Video ►FREE Video Tutorial GIFT on Yolo CNN's http://augmentedstartups.info/yolofreegiftsp https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing world of Machine Learning. Each section consists of fun and intriguing white board explanations like this one with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science. So who are we to teach you, well my Name is Ritesh Kanjee and I have a Masters Degree in Electronic engineering majoring in computer and machine vision. I have over 26000 students on Udemy teaching people from 128 countries around the world. I will be teaching you the theoretic side of Machine Learning On the Practical Side, Minerva Singh is a Bestselling Udemy Instructor & Data Scientist with a PhD from Cambridge University. Minerva is proficient in statistical analysis, machine learning and data mining. She also enjoys general programming, data visualization and web development. So you can see that you will be taught Machine learning by two qualified professionals. So We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Each theoretical lecture is uniquely designed using whiteboard animations like this which can maximise concentration and engagement in the lectures which improves knowledge retention. This ensures that you absorb as much of the content than you would traditionally would watching other videos and or reading books on this topic. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 20497 Augmented Startups
Tiny Tutorial 1: Setting Up Your Python Environment
 
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Getting started with Python? Enthought Canopy (https://www.enthought.com/products/canopy/) makes it easy! In one complete package, Canopy provides an integrated analysis environment with code editor, graphical debugger and variable browser, and Jupyter notebook support PLUS a graphical package manager with access to over 450 pre-compiled and tested scientific and analytic Python libraries in the Enthought Python Distribution. Get up and running in less than 5 minutes with Canopy (free download)! https://store.enthought.com/downloads **Learn more about Enthought's Python course offerings here: https://goo.gl/q45vt6
Views: 4141 Enthought
Java Data Science Solutions - Big Data and Visualization : The Course Overview | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2vbDmvu]. This video will give an overview of the entire course. For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 371 Packt Video