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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: 941020 David Langer
My 5 favourite Coursera Courses for Python, Data Science and Machine Learning
 
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These are my 5 favourite Coursera courses for learning python, data science and Machine Learning Python for Everybody - https://www.coursera.org/specializations/python Applied Data Science with Python - https://www.coursera.org/specializations/data-science-python Deep Learning - https://www.coursera.org/specializations/deep-learning Mathematics for Machine Learning - https://www.coursera.org/specializations/mathematics-machine-learning Machine Learning - https://www.coursera.org/learn/machine-learning If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer Bonus choice - Machine Learning - https://www.coursera.org/specializations/machine-learning
Views: 19757 Python Programmer
Coursera - Getting and Cleaning Data - Idaho Housing
 
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www.bit.ly/R-videos | Coursera Data Science Specialization
Views: 3794 Dragonfly Statistics
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial: 1) Introduction to Classification 2) Why Random Forest? 3) What is Random Forest? 4) Random Forest Use Cases 5) How Random Forest Works? 6) Demo in R: Diabetes Prevention Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #RandomForest #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies 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 Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 54600 edureka!
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 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 Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 444294 edureka!
R Programming - Overview and History of R by Johns Hopkins University
 
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This video is part of an online course, R Programming created by Johns Hopkins University. Enroll today at https://www.coursera.org/learn/r-programming?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=jhu to get access to the full course. About this course: In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples. Visit https://www.coursera.org/learn/r-programming?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=jhu to learn more! Specialization: https://www.coursera.org/specializations/jhu-data-science?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=jhu Keep in touch with Coursera! Twitter: https://twitter.com/coursera Facebook: https://www.facebook.com/Coursera/
Views: 344 Coursera
Analyzing Text Data with R on Windows
 
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Provides introduction to text mining with r on a Windows computer. Text analytics related topics include: - reading txt or csv file - cleaning of text data - creating term document matrix - making wordcloud and barplots. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 9578 Bharatendra Rai
Lecture 43 — Collaborative Filtering | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Introduction to Data Analytics with R, Tableau & Excel | Data Analytics Career in 2019 & Beyond
 
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Introduction to Data Analytics with R, Tableau & Excel | Data Analytics Career in 2019 & Beyond https://acadgild.com/big-data/data-analytics-training-certification?aff_id=6003&source=youtube&account=UgnojgSKQLk&campaign=youtube_channel&utm_source=youtube&utm_medium=intro-DA-R-tableau-excel&utm_campaign=youtube_channel Did you know? by 2020, every human being will create over 1.5 megabytes of data per second on average. In 2025, the sum of digital data will add up to 180 zettabytes, which is over 1600 trillion gigabytes. Considering these numbers, it is an understatement to say that the data is only BIG. So, what is Big Data and how is it related to Data Analytics? Big data is a large volume of data that consists of both structured and unstructured data forms. helps organizations to draw meaningful insights from their data to learn and grow. Thus, it’s the data that matters and not it’s volume. Structured data is organized information that can be accessed with the help of simple search algorithms. While Unstructured data as the name suggests is less uniform and thus difficult to work with. The lack of structure makes compiling data at a time and energy-consuming task. The Relation Between Big Data and Analytics: The process of uncovering hidden patterns, unknown correlations, market trends, customer preferences and other useful information from both structured and unstructured data is called Data analytics. The Benefits of Using Data Analytics. • Analytics help organizations make informed decisions and choices. • It boosts the overall performance of the organization by refining the financial processes, increasing visibility, providing insights and granting control over managerial processes. • It detects fraud and flaws by keeping a close vigil. • It further Improves the IT economy by increasing agility and flexibility of systems. The above mentioned are just a few advantages, however, the list goes on. Despite the growing interest in data analytics, there is an acute shortage of professionals with good data analytical skills. Thus, only 0.5% of the data we produce is analysed. There is a serious shortage of skilled professionals. Thus, the ones who are called proficient data analysts must have certain skills. They must possess a varied skill-set like computer science, data mining and business management to provide from the data they are working on. Their computer science skills should include both programming skills and technical skills • Programming Skills: Python, R, and Java • Technical Skills: Knowledge of platforms like Hadoop, Hive, Spark, etc., Their data skills should include Warehousing Skills, Quantitative & Statistical Skills & Analytical & Interpretation Skills • Warehousing Skills: Data scientist must possess good analytical skills • Quantitative & Statistical Skills: As technology is a key aspect of big data analysis, quantitative and statistical skills are essential • Analytical & Interpretation Skills: knacks to analyses and interpret data The business skills are important to use the data effectively and to improve various aspects such as operations, finance, productivity, etc., These are the skills that make the data analytics professional an invaluable asset to the organization. The lack of skilled data professionals is an opportunity in turn for upcoming data scientists to make their mark in the field of data analytics. As the significance of data grows in the business world, the value of professionals working in analytics also increases. This is creating a variety of job roles amongst organizations and they are. Data Analyst, Analytics Consultant, Business Analyst, Analytics Manager, Data Architect, Metrics and Analytics Specialist, Analytics Associate these are only some of the job titles that data analytics professionals can acquire in business organizations. The list is presumably greater. The Chief Software Platforms are R, Tableau & Excel R is one of the robust statistical computing solutions. Tableau is the foremost business intelligence platform that offers eminent data visualization and exploration capabilities. Coming to Excel, it is used for managing, manipulating and presenting data. When combined, Tableau, R and Excel offer the most powerful and complete data analytics solutions. So, the demand for data analytics and its professionals is augmenting at a great pace. Organizations are interested in analysts to maximize their data potential, while professionals are interested in capitalizing on the analytical crunch in many parts of the world. #DataAnalytics, #Tableau, #R, #Excel, #career Please like share and subscribe the channel for more such video. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 2270 ACADGILD
R Spatial Data 2: KNN from Longitude and Latitude
 
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Here I read in some longitude and latitudes, and create a K nearest neighbor weights file. Then we visualize with a plot, and export the weights matrix as a CSV file. Link to R Commands: http://spatial.burkeyacademy.com/home/files/knn%20in%20R.txt Link to Spatial Econometrics Cheat Sheet: http://spatial.burkeyacademy.com/home/files/BurkeyAcademy%20Spatial%20Regression%20CheatSheet%200.6.pdf Link to Census Site: https://www.census.gov/geo/reference/centersofpop.html Great Circle Distances: https://youtu.be/qi9KIKDpHKY My Website: spatial.burkeyacademy.com or www.burkeyacademy.com Support me on Patreon! https://www.patreon.com/burkeyacademy Talk to me on my SubReddit: https://www.reddit.com/r/BurkeyAcademy/
Views: 1764 BurkeyAcademy
Data Munging - Data Analysis with R
 
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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 2522 Udacity
DATA MINING   1 Data Visualization   3 2 2  Multidimensional Scaling
 
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https://www.coursera.org/learn/datavisualization
Views: 9925 Ryo Eng
R Programming - Introduction to R by Johns Hopkins University
 
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This video is part of an online course, R Programming created by Johns Hopkins University. Enroll today at https://www.coursera.org/learn/r-programming?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=jhu to get access to the full course. About this course: In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples. Visit https://www.coursera.org/learn/r-programming?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=jhu to learn more! Specialization: https://www.coursera.org/specializations/jhu-data-science?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=jhu Keep in touch with Coursera! Twitter: https://twitter.com/coursera Facebook: https://www.facebook.com/Coursera/
Views: 545 Coursera
R Tutorial - from coursera
 
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The structure of a data analysis (steps in the process, knowing when to quit, etc.) Types of data (census, designed studies, randomized trials) Types of data analysis questions (exploratory, inferential, predictive, etc.) How to write up a data analysis (compositional style, reproducibility, etc.) Obtaining data from the web (through downloads mostly) Loading data into R from different file types Plotting data for exploratory purposes (boxplots, scatterplots, etc.) Exploratory statistical models (clustering) Statistical models for inference (linear models, basic confidence intervals/hypothesis testing) Basic model checking (primarily visually) The prediction process Study design for prediction Cross-validation A couple of simple prediction models Basics of simulation for evaluating models Ways you can fool yourself and how to avoid them (confounding, multiple testing, etc.)
Views: 1189 Anand Maurya
12. Clustering
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 80359 MIT OpenCourseWare
Data objects and classes in R
 
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DragonflyStats.github.io | Rstats
Views: 7090 Dragonfly Statistics
Getting and Cleaning Data - dplyr Basic Functions
 
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This video is under a Creative Commons Attribution - Noncommercial - Share Alike license (CC-BY-NC-SA)
Views: 454 Open Education Lab
R - Association Rules - Market Basket Analysis (part 1)
 
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Association Rules for Market Basket Analysis using arules package in R. The data set can be load from within R once you have installed and loaded the arules package. Association Rules are an Unsupervised Learning technique used to discover interesting patterns in big data that is usually unstructured as well.
Views: 53201 Jalayer Academy
Introduction to R Data Analysis: Data Cleaning
 
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Data Cleaning and Dates using lubridate, dplyr, and plyr
Views: 44464 John Muschelli
Coursera - Analysis of Variance - Data Analysis
 
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www.Stats-Lab.com | Coursera Data Science Specialization
Data Mining Capstone Project Overview
 
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An overview of my Capstone Project for the Coursera Data Mining Specialization offered by the University of Illinois at Urbana-Champaign, by Phil Ferriere (2016) Contact: https://www.linkedin.com/in/philferriere For a longer, detailed review [42 slides, 25mn], please check out https://youtu.be/Fx30sj78Ef4
Views: 646 Phil Ferriere
Coursera Offers MOOC-Based Master's in Data Science
 
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See: http://www.i-programmer.info/news/150-training-a-education/9588-coursera-offers-mooc-based-masters-in-data-science.html
Views: 888 IProgrammerTV
DATA MINING   1 Data Visualization   2 1 3  Charts
 
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https://www.coursera.org/learn/datavisualization
Views: 78 Ryo Eng
Data Science With R | Introduction to Data Science with R | Data Science For Beginners | Simplilearn
 
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This Data Science with R tutorial will help you understand what is R, why R, what is comprehensive R archive network, how to install R, what is linear regression, what is correlation analysis in R and at the end you will also see a use case implementation using R where we predict the class of a flower. Today, it is imperative for every modern business to understand the huge amounts of data it maintains on its customers and itself. R programming language makes it easy for a business to go through the business’s entire data. Now, lets deep dive into this video to understand Data Science using R programming. Below topics are explained in this Data Science with R tutorial: 1. Introduction to R ( 00:38 ) - Why R? - Comprehensive R archive network - Installing R 2. Simple linear regression using R ( 12:20 ) - The line of best fit - Correlation analysis in R 3. Classification using R ( 38:24 ) - Use case: Predict the class of a flower 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/WGtBKQ Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analysis techniques that can be performed using R. The data science course is packed with real-life projects and case studies and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Science-With-R-0vCK17cQt14&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - 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: 5076 Simplilearn
R tutorial: Introduction to cleaning data with R
 
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Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data. You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere. Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned. Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data. With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer. In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw. Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help. In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis. Let's jump right in!
Views: 31229 DataCamp
DATA MINING   1 Data Visualization   2 2 2  Parallel Coordinates
 
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https://www.coursera.org/learn/datavisualization
Views: 1082 Ryo Eng
Cross Validation
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-312357973/m-438108645 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 88820 Udacity
Summary: what is special about mining spatial data (2014 Coursera)
 
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Summary: what is special about mining spatial data
Views: 504 Spatial Computing
4.3 Introduction to data.table (Exploratory Data Analysis with data.table)
 
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See here for the course website, including a transcript of the code and an interactive quiz for this segment: http://dgrtwo.github.io/RData/lessons/lesson4/segment3/
How DTW (Dynamic Time Warping) algorithm works
 
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In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them. Source code of graphs available at https://github.com/tkorting/youtube/blob/master/how-dtw-works.m The presentation was created using as references the following scientific papers: 1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49. 2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009. 3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf
Views: 33632 Thales Sehn Körting
Goodness of Fit and Test of Independence with R - Examples Using Chi-Square Test
 
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Includes, - test of goodness of fit - test of independence - Yate's correction - monte carlo simulation when expected frequencies are less than 5 R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 4265 Bharatendra Rai
DATA MINING   1 Data Visualization   3 1 3  Graph Visualization
 
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https://www.coursera.org/learn/datavisualization
Views: 169 Ryo Eng
Bagging & Boosting Algorithms | Decision Tree | Data Science
 
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In this video you will learn about theory behind bootstrap method of building decision tree and combining them for better prediction.. This type of algorithms are known as Bagging & boosting or in general known as Ensemble learning . Apart from these two random forest is also a popular ensemble training algorithms ANalytics Study Pack : https://analyticuniversity.com Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 9004 Big Edu
Business Data Analysis with Excel
 
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Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required! Dave will cover the following during the presentation: • The types of business data and why business data is a unique analytical challenge. • Requirements for robust business data analysis. • Using histograms, running records, and process behavior charts to analyze business data. • The rules of trend analysis. • How to properly compare business data across time, organizations, geographies, etc.Where you can learn more about the tools and techniques. *Excel spreadsheets can be found here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Business%20Data%20Analysis%20with%20Excel **Find out more about David here: https://www.meetup.com/data-science-dojo/events/236198327/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8xWx0 See what our past attendees are saying here: https://hubs.ly/H0f8xGd0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 47770 Data Science Dojo
DATA MINING   3 Text Mining and Analytics   1 7 Word Association Mining and Analysis
 
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https://www.coursera.org/learn/text-mining
Views: 144 Ryo Eng
Coursera - Getting and Cleaning Data - Idaho Housing ( Optional Exercise)
 
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www.Stats-Lab.com | Coursera Data Science Specialization | Getting and Cleaning Data Exercise related to the Idaho Housing Data Set. Not part of the Week 1 Quiz.
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: 155349 edureka!
R Tutorial 10: More on vectors + find and remove missing values
 
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R Tutorial 10: Vectors + Find and remove missing values How to Manipulate vectors and find missing values in R The software that is used for data mining / machine learning / data science / statistical computing and mathematical problem solving. For more detailed discussions on various topics checkout: http://rstatistics.net/ http://rstatistics.net/r-tutorial-exercise-for-beginners/ Get regular awesome tips on R programming twitter: http://twitter.com/r_programming Like our 'One R Tip A Day' facebook page and check get notifications in the 'like' button dropdown to get nice R tips on your news feed every day! http://facebook.com/rtipaday Subscribe NOW! by clicking the 'Subscribe Button' for updates on free 'R Programming for Data science' Tutorial videos on our channel. For Best Results, watch in HD. R is world's most widely used statistics programming language. It's the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in businesses worldwide. This latest R Programming Course for Data Science is most suitable for Non-Programmer statisticians and Newbies who want to become the most coveted Data science professional that most companies are looking for.
Views: 33242 LearnR
DATA MINING   3 Text Mining and Analytics   4 1 Text Clustering Motivation
 
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https://www.coursera.org/learn/text-mining
Views: 40 Ryo Eng
Sentiment Analysis in 4 Minutes
 
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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 97043 Siraj Raval
Best R Programming Tutorials
 
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What are some of the best R programming tutorials? Coursera has a free class on R programming and data analytics in general. Free is always good. You can try reading the information on John D. Cook’s blog. It isn’t a tutorial per se, but he has a lot of tips, tricks and simple advice. Simple advice is what I expect with Python and PHP, not a data analysis software and programming language. Try Inside-R.org article database for examples of how to use the language for different applications. And hopefully more than differential equations. They also have information on data analysis tools that use R, kind of like learning tools that use Mathcad as well as C++. I’d give C++ an F on usability. You could try the Revolution Analytics site; they have a good introduction to the language and the tools using it. They make tools that use R. At least they have a lot of content that is not interspersed with buy our books. It is more buy our software. The Inside-R.org site by Revolution Analytics has a calendar on R user groups, and you might find an in-person meet up or lecture from there. I’d like a more reliable resource than that. The ultimate source is the true source, the R-Project.org site. Reading a couple hundred pages on the programming language might teach me the syntax or grammar or other rules, but it does not help me utilize it. I’m sorry, there is no code combat or code kata equivalent for R. What does exist? DevCheatSheet.com has R cheat sheets for data mining, the standard commands, R with Matlab and R for regression analysis. I’d consider that pretty progressive. StatMethods.net has a number of quick lessons on statistical methods, doing time series, generating lattice graphs, finding correlations and somewhat simplified explanations of R data structures. As if anything in data analytics could be simple. StatMethods.net’s Quick-R section even has samples of code showing how to create your own value labels, something that is otherwise hard to do. What else can I do to make this less complicated? Try the RStudio.com webinars, videos and tutorials. They are one of the few sites I’ve seen with free R videos that are not half way through a solution with the final answer being hire me or buy my book. You have to admit, that type of pitch does tend to equal dollar signs.
Views: 529 Techy Help
Working with types in R
 
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DragonflyStats.github.io | Rstats
Views: 2424 Dragonfly Statistics
DATA MINING   2 Text Retrieval and Search Engines   Lesson 5 6 Link Analysis Part 1
 
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https://www.coursera.org/learn/text-retrieval
Views: 71 Ryo Eng