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Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 266018 CS Dojo
Python for Data Analysis | Python for Data Visualisation | Python Tutorial | Learn Python
 
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#Python | Learn Data Visualisation and Data Analytics techniques using Python in a hands-on example. Know the basics of Python and how it can be used in Data analytics. Access 100s of hours of similar high-quality FREE learning content at http://greatlearningforlife.com Learn More: https://goo.gl/ufKJsH Know about our analytics programs: PGP-Business Analytics: https://goo.gl/UpQETw PGP-Big Data Analytics: https://goo.gl/9tv7Ay Business Analytics Certificate Program: https://goo.gl/9b9poE #DataVisualisation #DataAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence.
Views: 395200 Great Learning
Getting Started with Python | Data Analysis and Visualization
 
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Uses yhat rodeo that has IDE similar to RStudio and matlab. Data file link: https://drive.google.com/open?id=1tHAdr3V1N8BzZShg0A5ZU6eAUN_9kZZf
Views: 1173 Bharatendra Rai
Mastering Data Analysis With Python Pandas & Matplotlib 2018
 
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Welcome! “Mastering Data Analysis With Python Pandas & Matplotlib 2018” is an excellent choice for both beginners and experts looking to expand their knowledge in Machine Learning field.Data Analysis is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.Mastering Data Analysis With Python Pandas & Matplotlib 2018 offers in-depth video tutorials in which we’ll dive into tons of different datasets, short and long, broken and pristine. I’ll take you step-by-step through Data Analysis process using the most powerful python libraries (Numpy, Pandas and Matplotlib), from installation to visualization! . tutorials include: Installing. Creating. Accessing. Applying arithmetic operations. Reindexing. Slicing. Tidying up. Handling missing data. Handling duplicated data. Concatenating. Grouping. Aggregating. deleting. visualizing.
Views: 12007 Harshad Rupawate
Data Analysis of Uber trip data using Python, Pandas, and Jupyter Notebook
 
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https://github.com/mnd-af/src/blob/master/2017/06/04/Uber%20Data%20Analysis.ipynb
Views: 30481 MandarinaCS
Exploratory Data Analysis In Python,  Interactive Data Visualization [Course] With Python and Pandas
 
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In this Statistics Using Python Tutorial, Learn Exploratory Data Analysis In python Using data set from gapminder.org . We will code interactive graphs in Python using matplotlib and pandas within Jupyterlab. 🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷 Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ 🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
Views: 2525 TheEngineeringWorld
Graphing/visualization - Data Analysis with Python and Pandas p.2
 
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Doing some basic visualizations with our Pandas dataframe in Python with Matplotlib. Text-based tutorial: https://pythonprogramming.net/graph-visualization-python3-pandas-data-analysis/ Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex G+: https://plus.google.com/+sentdex
Views: 17457 sentdex
Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Training | Edureka
 
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( Python Training : https://www.edureka.co/python ) This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014. This Python Pandas tutorial video helps you to learn following topics: 1. What is Data Analysis? 2. What is Pandas? 3. Pandas Operations 4. Use-case Check out our Python Training Playlist: https://goo.gl/Na1p9G Subscribe to our channel to get video updates. Hit the subscribe button above. #Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonPandas How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. 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: 173059 edureka!
data visualization in python using matplotlib, pandas and numpy
 
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In this video we will learn about matplotlib, little bit of pandas and numpy. 1) matplot lib is graph plotting library of python. using matplotlib we can plot dirrerent scatter plots, line graphs, bar graphs, pie chart and histograms . Using these plots we can visualize our data 2) pandas is a library for data analysis. Using pandas, we can analysis big datasets 3) Numpy is use for numerical and scientific computation. Numpy is way faster and optimized than inbuilt python list and other functions. You can create your own build system in sublime text to run python script directly from sublime text.Just create a new build system and add these lines : - { "cmd": ["gnome-terminal -e 'bash -c \"/usr/bin/python3 -u $file;echo;echo Press ENTER to exit; read line\"'"], "shell": true } add proper path of python in the above. I installed python3 in /usr/bin/ .So I added this path. You can get proper path of your python by typing "which python" or "which python3" in the linux of mac terminal.
Views: 22876 Arpan Pathak
Data Visualization and Exploration with Python || Stephen Elston
 
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Visualization is an essential method in any data scientist’s toolbox and is a key data exploration method and is a powerful tool for presentation of results and understanding problems with analytics. Attendees are introduced to Python visualization packages, Matplotlib, Pandas, and Seaborn. Jupyter notebook: https://github.com/StephenElston/ExploringDataWithPython EVENT: PyData Seattle 2017 SPEAKER: Stephen Elston PERMISSIONS: PyData provided Coding Tech with the permission to publish this video. CREDITS: Original video source: https://www.youtube.com/watch?v=KvZ2KSxlWBY
Views: 7695 Coding Tech
Christopher Roach | Visualizing Geographic Data With Python
 
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PyData SF 2016 The statistician George Box once wrote that “all models are wrong, but some are useful”; the same could be said for maps. In this talk, we’ll discuss the problems that arise when creating 2-dimensional representations of our world. We'll then see how to create data-rich maps using Python, matplotlib, and the basemap toolkit. we'll also see how to create maps for the web using the Folium library. Maps have been such a mainstay of our lives for so long now that it's hard to imagine just how complex it is to create one. Keep in mind though, the earth is a 3-dimensional spherical object, so we're stuck with the problem of "projecting" the world onto a 2-dimensional surface. What this means is that every map you've ever looked at was wrong in some way. In this talk, we’ll discuss what a map projection is, and why the Mercator projection, the map you use everyday, is both incorrect and unfair, but useful nonetheless. We’ll also see some ways that we can create maps using Python. We’ll first see how to create data-rich maps using the matplotlib library with the basemap toolkit. We’ll then see how to create maps for the web using libraries like Folium, the python interface to Leaflet.js. By the end of this talk, you should have a general understanding of the problems surrounding the creation of effective maps. You should also feel comfortable picking out a proper map projection and plotting data on it using a multitude of techniques and the Python language.
Views: 32992 PyData
Stephen Elston - Data Visualization and Exploration with Python
 
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Description Visualization is an essential method in any data scientist’s toolbox and is a key data exploration method and is a powerful tool for presentation of results and understanding problems with analytics. Attendees are introduced to Python visualization packages, Matplotlib, Pandas, and Seaborn. The Jupyter notebook can be downloaded at https://github.com/StephenElston/ExploringDataWithPython Abstract Visualization of complex real-world datasets presents a number of challenges to data scientists. By developing skills in data visualization, data scientists can confidently explore and understand the relationships in complex data sets. Using the Python matplotlib, pandas plotting and seaborn packages attendees will learn to: • Explore complex data sets with visualization, to develop understanding of the inherent relationships. • Create multiple views of data to highlight different aspects of the inherent relationships, with different graph types. • Use plot aesthetics to project multiple dimensions. • Apply conditioning or faceting methods to project multiple dimensions 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: 16590 PyData
Reproducible Data Analysis in Jupyter, Part 1/10: Loading and Visualizing Data
 
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Jupyter notebooks provide a useful environment for interactive exploration of data. A common question I get, though, is how you can progress from this nonlinear, interactive, trial-and-error style of exploration to a more linear and reproducible analysis based on organized, packaged, and tested code. This series of videos presents a case study in how I personally approach reproducible data analysis within the Jupyter notebook. For more information and resources relating to these videos, see http://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/ In this first video, we download the Fremont Bridge bicycle data and produce some basic visualizations in the Jupyter notebook.
Views: 20804 Jake Vanderplas
Data Visualization In Python, [ Plots Of Two Variables ] Statistics & Data Analysis With Python 🐍
 
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In this statistics With Python Series Tutorial, we learn data visualization In python Using Jupyter lab. we learn scatter plots by applying different statistical methods using matplotlib, pandas and NumPy scipy.stats. 🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷 Python Graph Visualization, Statistics For Data Analytics [ Python Bar Graph Example Tutorial ] https://youtu.be/3KofFIhtjNE Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI Python Describe Statistics, Exploratory Data Analysis Using Pandas & NumPy [Descriptive Statistics] https://youtu.be/6SeJH0p7n44 🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
Views: 531 TheEngineeringWorld
Tweet Visualization and Sentiment Analysis in Python - Full Tutorial
 
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In this Python tutorial, the Tweepy module is used to stream live tweets directly from Twitter in real-time. The tweets are visualized and then the TextBlob module is used to do sentiment analysis on the tweets. 💻Code: https://github.com/vprusso/youtube_tutorials/tree/master/twitter_python ⭐️ Contents ⭐️ ⌨️ (00:06) Streaming live tweets ⌨️ (23:17) Cursor and pagination ⌨️ (43:28) Analyzing tweet data ⌨️ (1:03:16) Visualizing tweet data ⌨️ (1:20:18) Sentiment analysis 🔗Tweepy Website:http://www.tweepy.org/ 🔗Cursor Docs: http://docs.tweepy.org/en/v3.5.0/cursor_tutorial.html 🔗API Reference: http://docs.tweepy.org/en/v3.5.0/api.html Tutorial from Vincent Russo of Lucid Programming. Check out his YouTube channel: http://bit.ly/lucidcode 🐦Vincent on Twitter: @captainhamptons -- 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: 18136 freeCodeCamp.org
The beauty of data visualization - David McCandless
 
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View full lesson: http://ed.ted.com/lessons/david-mccandless-the-beauty-of-data-visualization David McCandless turns complex data sets, like worldwide military spending, media buzz, and Facebook status updates, into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world. Talk by David McCandless.
Views: 603360 TED-Ed
Data Analysis with Python for Excel Users
 
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A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis
Views: 179735 APMonitor.com
Python Data Visualization [ Graphing Categorical Data ] Pandas Data Analysis & Statistics Tutorial
 
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Learn about chart in Python in this python data visualization tutorial. explore graphing with python by describing categorical data inside Jupyterlab. This is a part of statistics with Python Tutorial series. 🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷 Data Visualization In Python, [ Plots Of Two Variables ] Statistics & Data Analysis With Python 🐍 https://youtu.be/uufMAMUEAaQ Python Graph Visualization, Exploratory Data Analysis With Pandas & Matplotlib [ Python Statistic ] https://youtu.be/Eb9eD4aNS7o Python Data Visualization [ Graphing Categorical Data ] Pandas Data Analysis & Statistics Tutorial https://youtu.be/M1h0pPFVy0E Exploratory Data Analysis In Python, Email Analytics With Pandas [ Predictive Analytics Python ] 🔴 https://youtu.be/03OJrdbhor0 Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics] https://youtu.be/sNg8VnMOAfw Python Graph Visualization, Statistics For Data Analytics [ Python Bar Graph Example Tutorial ] https://youtu.be/3KofFIhtjNE Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI Python Describe Statistics, Exploratory Data Analysis Using Pandas & NumPy [Descriptive Statistics] https://youtu.be/6SeJH0p7n44 🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g 📌📌📌📌📌📌📌📌📌📌
Views: 1616 TheEngineeringWorld
Python Data Visualization With Bokeh
 
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In this video we will get started with data visualization in Python by creating a top horsepower chart using the Bokeh library Code: https://github.com/bradtraversy Bokeh Docs: https://bokeh.pydata.org/en/latest/ Sponsor: DevMountain Bootcamp https://goo.gl/6q0dEa 💖 Become a Patron: Show support & get perks! http://www.patreon.com/traversymedia Website & Udemy Courses http://www.traversymedia.com Follow Traversy Media: https://www.facebook.com/traversymedia https://www.twitter.com/traversymedia https://www.instagram.com/traversymedia
Views: 19297 Traversy Media
Skills Needed For Data Scientist and Data Analyst
 
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In this Video, We will be discussing about the skills needed for data analyst and data scientist roles. The reason for making one video to discuss both data analyst and data scientist roles is because there are a lot things in common between both these two role. Data Analyst does a lot of descriptive analytics. On the other hand, Data Scientist also does descriptive analytics. But also data scientists do something called predictive analytics. So let's try to understand what Descriptive and Predictive analytics mean. Descriptive Analytics is all about analyzing the historical data to answer this particular question which is "WHAT HAS HAPPENED TILL NOW??". Predictive Analytics also involves analysis of historical data but, predictive analytics is mainly all about answering the question which is.. "WHAT WILL HAPPEN IN THE FUTURE??" Let's understand this with a simple example. I have sales data of XYZ company in a table format. As part of descriptive analytics, we can simply create a scatter chart so that we can quickly understand how the company has been performing in terms of sales in the previous years. Now let's look at predictive analytics. So now that we know how the company has been performing in the previous years, can we predict what's gonna happen to the sales in the coming years?.. Will the sales increase, or decrease or does it remain the same??.. If we are able to answer these questions, then it is called as predictive analytics. So coming back to the comparison of Data Analyst and Data Scientist roles, Now that we have some idea about the differences between the two roles, lets now look at skills needed for each of these two roles. Data Analysts should be good with Math and Statistics. They should be good with handling the data. -- This includes knowledge of ETL (or Extract Transform and Load) operations on data and experience working with popular ETL tools such as Informatica – PowerCenter,IBM – Infosphere Information Server, alteryx, Microsoft – SQL Server Integrated Services (SSIS), Talend Open Studio, SAS – Data Integration Studio ,SAP – BusinessObjects Data Integrator, QlikView Expressor or any other popular ETL tool. -- They should be comfortable in handling data from different sources and in different formats such as text, csv, tsv, excel, json, rdbms and others popular formats. -- They should have excellent knowledge of SQL (or Structured Query Language). Its a Bonus to have -- The knowledge of Big data tools and technologies to handle large data sets. -- NoSQL databases such as HBase, Cassandra and MongoDB. They should be expert in Analysing and Visualizing the data. -- They Should have experience working with popular data analysis and visualization packages in python and R such as numpy, scipy, pandas, matplotlib, ggplot and others. -- Experience with popular data analysis and visualization BI tools such as Tableau, Microsoft Power BI, SAP BI, SAS BI, Oracle BI, QlikView or any other popular BI tool They should have good communication and storytelling skills. Lets now look at the skills needed for data scientist role. Data scientist also does descriptive analytics just like data analysts. Apart from that, they also do predictive analytics. So as part of Descriptive analytics: Data Scientists should be excellent with Math and Statistics. Data scientists should be good with handling data -- So yes, they should have experience working with popular ETL frameworks. -- They should have excellent knowledge of SQL. -- Many companies expect data scientists to have mandatory knowledge of big data tools and technologies to work with large datasets and also to work with structured, semi-structured and unstructured data. -- Its good to have the knowledge of NoSQL databases such as HBase, Cassandra and MongoDB. They should be expert in Analysing and Visualizing the data. -- Experience working with popular data analysis and visualization packages in python and R. -- Experience with popular data analysis and visualization BI tools such as Tableau, Microsoft Power BI, SAP BI, SAS BI, Oracle BI, QlikView or any other popular BI tool. They should also have excellent communication and storytelling skills. And as part of predictive analytics, They should be good in using the techniques in artificial intelligence, data mining, machine learning, and statistical modeling to make future predictions using the historical data. Exposure to popular predictive analytics tools such as SAP Predictive analytics, Minitab, SAS Predictive Analytics, Alteryx Analytics, IBM predictive analytics or any other popular predictive analytics tool. They should have very good exposure to popular machine learning and deep learning packages available for Python and R such as scikit learn, tensorflow, theano,rpart, caret, randomForest, nnet, and other popular libraries.
Views: 33653 Art of Engineer
Python: Intro to Visualization with Matplotlib
 
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Intro to how to visualize data in a variety of plots and charts using Python Matplotlib for plotting. RELATED VIDEOS ► Numpy Intro: https://youtu.be/8Mpc9ukltVA ► Numpy Intro Jupyter nb: https://youtu.be/AAS8yoKuK7M ► Pandas Intro: https://youtu.be/e60ItwlZTKM ► Pandas and MPL for Data Analysis: https://youtu.be/ALX88JzeQnk ► Matplotlib Intro: https://youtu.be/MbKrSmoMads
Views: 4004 Joe James
Data Analytics Overview | Data Science With Python Tutorial
 
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The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants. Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization. Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS. As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis. Python for Data Science Certification Training: http://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Introduction-Python-Data-Science-ZH13ZXh1_-w&utm_medium=SC&utm_source=youtube Who should take this course? There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. Analytics professionals who want to work with Python 2. Software professionals looking for a career switch in 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 6. Anyone with a genuine interest in the field of Data Science For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 25201 Simplilearn
Visualizing Correlation Table - Data Analysis with Python and Pandas p.4
 
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Visualizing the correlation table with matshow in Matplotlib, among other things! Text-based tutorial: https://pythonprogramming.net/correlation-table-python3-pandas-data-analysis/ Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex G+: https://plus.google.com/+sentdex
Views: 11083 sentdex
Learn Data Science for Data Analysis and Visualization | Python |
 
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Learn python for data analysis and data visualization
Views: 55 dataSreka
The Python ecosystem for Data Science: A guided tour - Christian Staudt
 
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Description Pythonistas have access to an extensive collection of tools for data analysis. The space of tools is best understood as an ecosystem: Libraries build upon each other, and a good library fills an ecological niche by doing certain jobs well. This is a guided tour of the Python data science ecosystem, aiming to help us select the right stack for our next data-driven project. Abstract Python is on its way to becoming the lingua franca of data science, and Pythonistas have access to an impressive and extensive collection of tools for data analysis. Here, a data scientist needs to see the forest for the trees: The space of tools is best understood as an ecosystem, where libraries build upon each other, and where a good library fills an ecological niche by doing certain jobs well. This talk is a guided tour of the Python data science ecosystem. More than a list of libraries, it aims to provide some structure, classing tools by type of data, size of data, and type of analysis. In our tour, we visit a number of areas, including working with tabular data (numpy, pandas, dask, ...) and graph data (e.g. networkx), statistics (e.g. statsmodels), machine learning (scikit-learn, ...), data visualization (matplotlib, seaborn, bokeh, ...). Aspiring data scientists, and everyone else working with data, should find this useful for selecting the right tools for their next data-driven project. 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: 22473 PyData
Intro - Data Visualization GUIs with Dash and Python p.1
 
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How to create browser-based interactive data visualization interfaces with Python and Dash Text tutorials and sample code: https://pythonprogramming.net/data-visualization-application-dash-python-tutorial-introduction/ Discord: https://discordapp.com/invite/3jCqXJj https://pythonprogramming.net/support-donate/ https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://www.twitch.tv/sentdex https://plus.google.com/+sentdex
Views: 85310 sentdex
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 211066 APMonitor.com
Plotting real-time data using Python
 
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Learn how to plot real time data using Python. Here, we plot the live CPU usage percentage of PC using matplotlib. Code here: https://gist.github.com/nikhilkumarsingh/1dcec96a1eb0aeb8975fc13ec5825d43 Explore my tutorials: https://www.indianpythonista.tech/tutorials/ More awesome topics covered here: WhatsApp Bot using Twilio and Python: http://bit.ly/2JmZaNG Discovering Hidden APIs: http://bit.ly/2umeMHb RegEx in Python: http://bit.ly/2Hhtd6L Introduction to Numpy: http://bit.ly/2RZMxvO Introduction to Matplotlib: http://bit.ly/2UzwfqH Introduction to Pandas: http://bit.ly/2GkDvma Intermediate Python: http://bit.ly/2sdlEFs Functional Programming in Python: http://bit.ly/2FaEFB7 Python Package Publishing: http://bit.ly/2SCLkaj Multithreading in Python: http://bit.ly/2RzB1GD Multiprocessing in Python: http://bit.ly/2Fc9Xrp Parallel Programming in Python: http://bit.ly/2C4U81k Concurrent Programming in Python: http://bit.ly/2BYiREw Dataclasses in Python: http://bit.ly/2SDYQub Exploring YouTube Data API: http://bit.ly/2AvToSW Jupyter Notebook (Tips, Tricks and Hacks): http://bit.ly/2At7x3h Decorators in Python: http://bit.ly/2sdloX0 Inside Python: http://bit.ly/2Qr9gLG Exploring datetime: http://bit.ly/2VyGZGN Computer Vision for noobs: http://bit.ly/2RadooB Python for web: http://bit.ly/2SEZFmo Awesome Linux Terminal: http://bit.ly/2VwdTYH Tips, tricks, hacks and APIs: http://bit.ly/2Rajllx Optical Character Recognition: http://bit.ly/2LZ8IfL Facebook Messenger Bot Tutorial: http://bit.ly/2BYjON6 Facebook: https://www.facebook.com/IndianPythonista/ Github: https://www.github.com/nikhilkumarsingh/ Twitter: https://twitter.com/nikhilksingh97 #python #matplotlib #real-time
Views: 9182 Indian Pythonista
Python for Data Analysis and Visualization | Webinar by Vinod Venkatraman | Hackerearth
 
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About the webinar: Data analytics using Python's numpy, scikit, pandas modules Data Visualisation using Python's matplotlib module Big Data Analytics using PySpark with spark-core and mllib About the Speaker: The Speaker is Vinod Venkatraman. A passionate technology man of multiple talents, Vinod is spearheading the core technology initiatives at Great Learning. Be it a seamless user experience, the collection of thousands of critical user action data points daily or rolling out a great new feature, Vinod obsesses about it as fervently as he does create a new tune on his guitar. Vinod holds a B.Tech from IIT Bombay in Computer Science. He spent 7 years at Stratify Inc, a Silicon Valley-based product firm, to start his career, followed by 4 years at Flipkart, where he rose to be Software Architect. He now looks forward to leading the effort to build his own unicorn. Subscribe Our Channel For More Updates : https://goo.gl/suzeTB For More Updates, Please follow us on : Facebook : https://goo.gl/40iEqB Twitter : https://goo.gl/LcTAsM LinkedIn : https://goo.gl/iQCgJh Blog : https://goo.gl/9yOzvG
Views: 3120 HackerEarth
PLOTCON 2016: Irene Ros, Text is data! Analysis and visualization methods
 
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Text is one of the most interesting and varied data sources on the web and beyond, but it is one of the most difficult to deal with because it is fundamentally a messy, fragmented, and unnormalized format. If you have ever wanted to analyze and visualize text, but don’t know where to get started, this talk is for you. Irene will go through examples of text visualization approaches and the analysis methods required to create them. Irene is an information visualization researcher and developer creating engaging, informative and interactive data-driven interfaces and visualizations. Irene's career in data and its visual forms started at The Visual Communication Lab @ IBM Research and now continues at Bocoup where she is the Director of the Data Visualization team. Irene is also the organizer and program co-chair of Bocoup's OpenVis Conf, a two day conference about the practice of data visualization on the open web.
Views: 3661 Plotly
Pandas with Python 2.7 Part 6 - Data visualization with Matplotlib
 
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One of the most powerful aspects of Pandas is it's easy inclusion into the Matplotlib module. Matplotlib is a popular and robust Python module that allows programmers to create graphs and charts from their data. Pandas makes loading your data into Matplotlib slightly easier, as well as handles almost all of the processing necessary to get it ready for Matplotlib. Pandas is basically created to do this in the most efficient way possible. Pandas is also quite remarkably good at working with data with dates and Matplotlib. Traditionally, working with data that is indexed by date is somewhat challenging with Matplotlib, but not when using Pandas! Sample code for the series: http://pythonprogramming.net/python-2-7-pandas-data-analysis/ Pandas tutorial series: https://www.youtube.com/playlist?list=PLQVvvaa0QuDfHt4XU7vTm22xDegR0v0fQ http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 35247 sentdex
Time Series Data Visualization Using Matplotlib and Seaborn in Python - Tutorial 10
 
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In this Python for Data Science Tutorial you will learn about Time series Visualization in python using matplotlib and seaborn in jupyter notebook (Anaconda). This is the 10th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets. Download Link for Cars Data Set: https://www.4shared.com/s/fWRwKoPDaei Download Link for Enrollment Forecast: https://www.4shared.com/s/fz7QqHUivca Download Link for Iris Data Set: https://www.4shared.com/s/f2LIihSMUei https://www.4shared.com/s/fpnGCDSl0ei Download Link for Snow Inventory: https://www.4shared.com/s/fjUlUogqqei Download Link for Super Store Sales: https://www.4shared.com/s/f58VakVuFca Download Link for States: https://www.4shared.com/s/fvepo3gOAei Download Link for Spam-base Data Base: https://www.4shared.com/s/fq6ImfShUca Download Link for Parsed Data: https://www.4shared.com/s/fFVxFjzm_ca Download Link for HTML File: https://www.4shared.com/s/ftPVgKp2Lca
Views: 5151 TheEngineeringWorld
Groupby - Data Analysis with Python and Pandas p.3
 
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Hello and welcome to another data analysis with Python and Pandas tutorial. In this tutorial, we're going to change up the dataset and play with minimum wage data now. Text-based tutorial: https://pythonprogramming.net/groupby-python3-pandas-data-analysis/ Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex G+: https://plus.google.com/+sentdex
Views: 11462 sentdex
Analysis and Visualization of 3D Data with yt | SciPy 2016 | Matthew Turk
 
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yt is a Python package designed for domain-specific inquiry of volumetric data, licensed under the BSD license and available at yt-project.org. Utilizing numerous components of the scientific Python ecosystem, it is able to ingest data from numerous different sources from domains such as astrophysics, nuclear engineering, weather and climate, oceanography, and seismology. Building on top of a parallelized framework for data selection, analysis, processing and visualization, inquiry can be driven based on relevant, physical quantities rather than those specific to data formats. I will describe recent advances in the yt 3.0 series, including support for particle, octree, patch and unstructured mesh datasets; interactive and batch volume rendering using both software and OpenGL backends; semantically-rich ontologies of fields, derived quantities and affiliated units (powered by sympy); user-defined kernel estimates for density; support for visualization in non-Cartesian domains; and a flexible chunking system for data IO. I will describe some of the non-astrophysics domains that yt has been applied to, and the infrastructure implemented to support that. Finally, I will describe the community-driven approach taken to designing, developing and implementing new features, and describe some of the challenges this has presented in the context of scientific software developers.
Views: 6179 Enthought
Data Carpentry - Data Analysis and Visualization with Python - Part 1
 
02:13:43
An introduction to programming in Python with pandas, best practices for data in spreadsheets, and what a typical data analysis workflow looks like. Lesson material: https://nbviewer.jupyter.org/github/UofTCoders/2018-07-12-utoronto/blob/gh-pages/code/1-programmatic-data-analyses.ipynb http://www.datacarpentry.org/spreadsheet-ecology-lesson/
Views: 65 UofT Coders
Vehicle sensor data App Example - Data Visualization GUIs with Dash and Python p.5
 
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Welcome to part five of the data visualization apps in Python with Dash tutorial series. In this part, we're going to cover how to make the vehicle sensor reading app that I showed in the beginning of this series. Text tutorials and sample code: https://pythonprogramming.net/vehicle-data-visualization-application-dash-python-tutorial/ Discord: https://discordapp.com/invite/3jCqXJj https://pythonprogramming.net/support-donate/ https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://www.twitch.tv/sentdex https://plus.google.com/+sentdex
Views: 20778 sentdex
Live Twitter Sentiment Graph - Data Visualization GUIs with Dash and Python p.9
 
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Welcome to part 4 of our sentiment analysis application with Dash and Python. Next, we're going to tie everything together up to this point to create a basic live-updating graph of Twitter sentiment for a term that we choose. To do this, all I am going to do is take our updates and apply them to the Live Graphs with Dash tutorial code Text tutorials and sample code: https://pythonprogramming.net/live-graph-twitter-sentiment-analysis-gui-dash-python/ Discord: https://discordapp.com/invite/3jCqXJj Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex G+: https://plus.google.com/+sentdex
Views: 14315 sentdex
Data Analysis and Visualization in python using pandas
 
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Pandas acts as a backbone of python. It is a software library written for python programming language. It offers operations that manipulates numerical tables and time series. DataFrames another concept of python that are the workhorse of pandas and are inspired by R programming language. They can be thought of as a bunch of Series objects put together to share the same index. This video explains further on why pandas is important, how it can be run and explore Dataframes using the concept.
Views: 16 Skill Analytica
Python Graph Visualization, Exploratory Data Analysis With Pandas & Matplotlib [ Python Statistic ]
 
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In this Python Pandas data Analysis Tutorial, learn Python graph visualization of More than 2 Variables. learn how to plot variables in python using Matplotlib and pandas in Jupyterlab. This is a part of Statistics With Python Tutorial Series. 🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷 Data Visualization In Python, [ Plots Of Two Variables ] Statistics & Data Analysis With Python 🐍 https://youtu.be/uufMAMUEAaQ Python Graph Visualization, Exploratory Data Analysis With Pandas & Matplotlib [ Python Statistic ] https://youtu.be/Eb9eD4aNS7o Python Data Visualization [ Graphing Categorical Data ] Pandas Data Analysis & Statistics Tutorial https://youtu.be/M1h0pPFVy0E Exploratory Data Analysis In Python, Email Analytics With Pandas [ Predictive Analytics Python ] 🔴 https://youtu.be/03OJrdbhor0 Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics] https://youtu.be/sNg8VnMOAfw Python Graph Visualization, Statistics For Data Analytics [ Python Bar Graph Example Tutorial ] https://youtu.be/3KofFIhtjNE Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI Python Describe Statistics, Exploratory Data Analysis Using Pandas & NumPy [Descriptive Statistics] https://youtu.be/6SeJH0p7n44 🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g 📌📌📌📌📌📌📌📌📌📌
Views: 934 TheEngineeringWorld
Data Visualization Tutorial For Beginners | Big Data Analytics Tutorial | Simplilearn
 
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This Data Visualization Tutorial will start by explain what Data Visualization is, Why we use Data Visualization, major considerations for Data Visualization and the basics of different types of graphs. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=BigData-Visualization-MiiANxRHSv4&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Visualization-MiiANxRHSv4&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 4322 Simplilearn
The importance of exploratory data analysis and data visualization in machine learning - PyCon 2018
 
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Speaker: Opetunde Adepoju All the data in the world is useless if you cannot understand it. EDA and data visualization are the most crucial yet overlooked stage in analytics process. This is because they give insights on the most relevant features in a particular data set required to build an accurate model. It is often said that the more the data, the better the model but sometimes, this can be counter-productive as more data can be a disadvantage. EDA helps avoid that. EDA is useful for professionals while data visualization is useful for end-users. For end-users: A good sketch is better than a long speech. The value of a machine learning model is not known unless it is used to make data driven decisions. It is therefore necessary for data scientists to master the act of telling a story for their work to stay relevant. This is where data visualization is extremely useful. We must remember that the end-users of the results are not professionals like us but people who know little or nothing about data analysis. For effective communication of our analysis, there is need for a detailed yet simple data visualization because the work of a data scientist is not done if data-driven insights and decisions are not made. For professionals: How do you ensure you are ready to use machine learning algorithms in a project? How do you choose the most suitable algorithms for your data set? How do you define the feature variables that can potentially be used for machine learning? Most data scientists ask these questions. EDA answers these questions explicitly. Also, EDA helps in understanding the data. Understanding the data brings familiarity with the data, giving insights on the best models that fit the data set, the features in the dataset that will be useful for building an accurate machine learning model, making feature engineering an easy process. In this talk, I will give a detailed explanation on what EDA and data visualization are and why they are very helpful in building accurate machine learning models for analytics as well as enhancing productivity and better understanding for clients. I will also discuss the risks of not mastering EDA and data visualization as a data scientist. Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018-slides
Views: 1775 PyCon 2018
R vs Python | Best Programming Language for Data Science and Analysis | Edureka
 
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***** Python Online Training: https://www.edureka.co/python ***** ***** R Online Training: https://www.edureka.co/r-for-analytics ***** This Edureka video on R vs Python provides you with a short and crisp description of the top two languages used in Data Science and Data Analytics i.e. Python and R (Blog:http://bit.ly/2ClaowR). You will also see the head to head comparison between the two on various parameters and learn why one is preferred over the other in certain aspects. Following topics are covered in the video: 1:30 Various Aspects of Comparison 1:40 Speed 1:56 Legacy 2:13 Code 2:28 Databases 2:45 Practical Agility 3:10 Trends 3:31 Salary 4:25 Syntax Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------- 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 ------------------------------------------------------------------------------------------------ #PythonVsR #Python #R #Pythononlinetraining #Javaonlinetraining ----------------------------------------------------------------- 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: 84247 edureka!
Jason Kessler - Using Scattertext and the Python NLP Ecosystem for Text Visualization
 
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Description Scattertext is a Python package that lets you compare and contrast how words are used differently in two types of documents, producing interactive, Javascript-based visualizations that can easily be embedded into Jupyter Notebooks. Using spaCy and Empath, Scattertext can also show how emotional states and words relating to a particular topic differ. Abstract Notebooks and presentation for this talk are available from https://github.com/JasonKessler/Scattertext-PyData. Motivation and introduction -What's the matter with word clouds? -How to read a plot made by Scattertext How to make your own plots -Preparing a Pandas data frame with your data set -Plotting with Scattertext, and fine tuning plots for interpretability and speed Scattertext and the Python NLP ecosystem -Visualizing emotions using Empath. -Using word vectors from spaCy and elsewhere see how topic-specific language differs. -Visualizing topic models from scikit-learn. Links -Source code for the package is hosted on Github at github.com/JasonKessler/scattertext. -For more information, please see the paper which will appear as a 2017 ACL Demo at https://arxiv.org/abs/1703.00565. 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: 4383 PyData
Tobias Stollenwerk: Data Analysis and Visualization with Python
 
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http://programm.froscon.de/2014/events/1352.html https://media.ccc.de/browse/conferences/froscon/2014/froscon2014_-_1352_-_en_-_hs5_-_201408231515_-_data_analysis_and_visualization_with_python_-_tobias_stollenwerk.html We demonstrate the usage of python's scientific tools, Numpy, Pandas and Matplotlib for data analysis and Visualization. As a use case, we present a python tool for personal bookkeeping. The talk will include: * Introduction to Numpy * Introduction to Matplotlib * Introduction to Pandas * Use case: Personal bookkeeping and analysis The presented personal bookkeeping tool reads in your bank records, automatically devides them into user defined categories and visualizes the results in a neat way.
Careers in Data Analytics and Visualization
 
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In this webinar, Andy Catlin, Director of the Katz School's Data Analytics and Visualization program discusses the many career opportunities available in the growing field of data analytics. The M.S. in Data Analytics and Visualization is a 30-credit Master's degree offered in-person or fully online. Visit yu.edu/katz, email [email protected], or call 833-241-4723 for more information.
Jeffrey Heer - Interactive Data Analysis: Visualization and Beyond
 
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Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, my talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools. 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: 7295 PyData
Python Pandas Tutorial 31 | Python Data Visualization | How to Create Scatter Matrix
 
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Hi guys...in this python data visualization video I have talked about how you can create scatter matrix in python using pandas library. Scatter matrix is very helpful to see correlation between all your numeric variable as well as their distribution by either historgram or KDE plot.
Python Data Visualization | How to create Boxplot in Matplotlib | Box plot chart with Real Data
 
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Hi guys...in this python data visualization video I have talked about how you can create boxplot in matplotlib. Box plot is very helpful in viewing the summary of dataset in an efficient way also box plot helps you in doing outlier analysis. In this video I have covered many important parameters of boxplot chart that are useful in real life data analysis.

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