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Time-Series Analysis with R | Classification
 
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Provides steps for carrying out time-series analysis with R and covers classification stage. Previous video - time-series clustering: https://goo.gl/UwsTxQ R code file: https://goo.gl/orX2YM Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 1076 Bharatendra Rai
Time-Series Analysis with R | Clustering
 
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Provides steps for carrying out time-series analysis with R and covers clustering stage. Previous video - time-series forecasting: https://goo.gl/wmQG36 Next video - time-series classification: https://goo.gl/w3b55p Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 1414 Bharatendra Rai
Time Series In R | Time Series Forecasting | Time Series Analysis | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future! Below are the topics we will cover in this live session: 1. Why Time Series Analysis? 2. What is Time Series Analysis? 3. When Not to use Time Series Analysis? 4. Components of Time Series Algorithm 5. Demo on Time Series 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: 88044 edureka!
Classifying and Clustering Data with R : Time Series Decomposition with R  | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2xQrLB8]. This video shows how to do time series decomposition in R. • Discuss an example of time series data • Show how to do log transformation of data • Show how to do decomposition of additive time series For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 5415 Packt Video
Forecasting Time Series Data in R | Facebook's Prophet Package 2017 & Tom Brady's Wikipedia data
 
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An example of using Facebook's recently released open source package prophet including, - data scraped from Tom Brady's Wikipedia page - getting Wikipedia trend data - time series plot - handling missing data and log transform - forecasting with Facebook's prophet - prediction - plot of actual versus forecast data - breaking and plotting forecast into trend, weekly seasonality & yearly seasonality components prophet procedure is an additive regression model with following components: - a piecewise linear or logistic growth curve trend - a yearly seasonal component modeled using Fourier series - a weekly seasonal component forecasting is an important tool related to analyzing big data or working in data science field. 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: 23299 Bharatendra Rai
Time-Series Analysis with R | Decomposition
 
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Provides steps for carrying out time-series analysis with R and covers decomposition stage. Next video - Time-Series Forecasting: https://goo.gl/o6uh67 Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 2247 Bharatendra Rai
Time-Series Analysis with R | Forecasting
 
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Provides steps for carrying out time-series analysis with R and covers forecasting stage. Previous video - time-series decomposition: https://goo.gl/hRJmU1 Next video - time-series clustering: https://goo.gl/5gMryj Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 1507 Bharatendra Rai
Time Series Classification Using Wavelet Scattering Transform
 
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This is a ~3-minute video highlight produced by undergraduate students Charlie Tian and Christina Coley regarding their research topic during the 2017 AMALTHEA REU Program at Florida Institute of Technology in Melbourne, FL. They were mentored by doctoral student Kaylen Bryan and professor Dr. Adrian Peter (Engineering Systems Department). More details about their project can be found at http://www.amalthea-reu.org.
Time Series in R Session 1.6 (Stationary and Non-Stationary Models)
 
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Time Series in R, Session 1, part 6 (Ryan Womack, Rutgers University) http://libguides.rutgers.edu/data twitter: @ryandata
Views: 33656 librarianwomack
Time Series Prediction
 
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Time series is the fastest growing category of data out there! It's a series of data points indexed in time order. Often, a time series is a sequence taken at successive equally spaced points in time. In this video, I'll cover 8 different time series techniques that will help us predict the price of gold over a period of 3 years. We'll compare the results of each technique, and even consider using a learning technique. From Holts Winter Method to Vector Auto Regression to Reinforcement Learning, we've got a lot to cover here. Enjoy! Code for this video: https://github.com/llSourcell/Time_Series_Prediction Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f https://towardsdatascience.com/bitcoin-price-prediction-using-time-series-forecasting-9f468f7174d3 https://www.datascience.com/blog/time-series-forecasting-machine-learning-differences https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/ https://www.youtube.com/watch?v=hhJIztWR_vo Join us at School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/ Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 63718 Siraj Raval
Time Series Data Mining Forecasting with Weka
 
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I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 25658 Web Educator
R tutorial: Importing, exporting and converting time series
 
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Learn more about importing, exporting and converting time series data: https://www.datacamp.com/courses/manipulating-time-series-data-in-r-with-xts-zoo In the last video we looked at creating xts objects from scratch. These were rather contrived, simply to illustrate the mechanics of xts construction and what xts objects looked like internally. In most real life cases, you'll be working with data that already exists - usually from some other process. Maybe it is a time series from a colleague in a different data class. In other cases you may find yourself importing data from an external source which may meet all the criteria you need for xts but it is coming from a file instead of another R object. In this chapter, well look at converting types using xts, reading data into R as an xts object, as well as exporting xts objects from R for other uses. First let's take a look at the most useful and simple way to convert most objects you'll encounter in R into xts. This will work in 90% of cases, as xts was designed from the beginning to make working with R's myriad time series and time classes as easy and flexible as possible. To illustrate how easy this is, we'll use the famous sunspots dataset that ships with R. sunspots is a ts object, which is fairly challenging to work with as it is regular - i.e. fixed intervals, but is less intuitive in its structure. To convert, we only need to use as.xts and you see the series now is well structured and looks like you might expect a time series to look. To import data from outside, we can follow a similar pattern. Here, we can read data into R using built in functions such as read.table, and coerce into xts at that point. As we mentioned earlier, since xts is a proper subclass of zoo, we can also leverage the powerful tools zoo provides to make life even easier. read.zoo is a great tool to read in data as a time series, and once again as.xts will dutifully convert it to its final xts class if so desired. Finally, you might be finished with your data manipulation or transformation in xts, and you may need to send it along to a process outside of R. Once again zoo provides a great function called 'write.zoo' which will do a lot of the heavy lifting of extracting and formating your times for you. If you are merely saving data for use in R later, I recommend you save it using the base 'saveRDS' function. This is optimized for objects like xts and makes it fast and efficient to read and write data to disk. Now that you've seen a bit on moving data into and out of xts, lets give it a try!
Views: 18260 DataCamp
Introduction To Time Series In R: Measuring Predictive Model Quality
 
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When developing predictive models and algorithms, whether linear regression or ARIMA models it is important to quantify how well the model fits to the future observations. One of the simplest methods of calculating how correct a model is uses the error between the predicted value and the actual value. From there, there are several methodologies that take this difference and further exploit meaning from it. Quantifying the accuracy of an algorithm is an important step to justifying the usage of the algorithm in product. If your team needs help developing data science systems, or algorithms please fee free to reach out to us! We are here to help guide you on your data science journey. http://www.acheronanalytics.com/contact.html Also feel free to watch some of our previous videos to this lecture: Basic Forecasting Models: https://youtu.be/8cKeAH2aGVI What Is A Time Series: https://youtu.be/uW3PQmzvUcw
Views: 929 Ben R
Chapter 16: Time Series Analysis (1/4)
 
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Time Series Analysis: Introduction to the model; Seasonal Adjustment Method Part 1 of 4
Views: 187042 Simcha Pollack
Two Effective Algorithms for Time Series Forecasting
 
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In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting. Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 48364 InfoQ
Time Series Forecasting Theory | AR, MA, ARMA, ARIMA | Data Science
 
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In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models. For training, consulting or help Contact : [email protected] For Study Packs : http://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: 406431 Analytics University
TensorFlow Tutorial #23 Time-Series Prediction
 
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How to predict time-series data using a Recurrent Neural Network (GRU / LSTM) in TensorFlow and Keras. Demonstrated on weather-data. https://github.com/Hvass-Labs/TensorFlow-Tutorials
Views: 67954 Hvass Laboratories
Comparing Time Series
 
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(Index: https://www.stat.auckland.ac.nz/~wild/wildaboutstatistics/ ) It is often interesting and useful to compare several series in terms of trend and seasonal patterns. How do the trends compare? How big are the seasonal effects for one series compared to another? Do they all behave in the same way at the same times? What oddities stand out in the plots? After you’ve watched this video, you should be able to answer these questions •When we are plotting several related series so that we can compare the patterns in them, what are the strengths and the weaknesses of a plot that puts all of the series on the same graph? •When we are plotting several related series so that we can compare the patterns in them, what are the strengths and the weaknesses of a plot that puts all of the series on their own separate graphs? •What types of feature of each series can we compare using the iNZight graphs for comparing series?
Views: 6825 Wild About Statistics
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. 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: 83895 edureka!
Forecasting with the Microsoft Time Series Data Mining Algorithm
 
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Imagine taking historical stock market data and using data science to more accurately predict future stock values. This is precisely the aim of the Microsoft Time Series data mining algorithm.. MSBI - SSAS - Data Mining - Time Series. In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA vesves ARIMA modelling and how to use these models to do forecast.. I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 864 Fidela Aretha
Time Series ARIMA model Using R | Stationarity | Non Stationarity
 
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Time series modelling is a popular way for forecasting data. In this video you will learn how to build a ARIMA model using R. ARIMA is known as Auto Regressive Integrated Moving Average which consists of AR, MA components. You will learn for both stationary and non-stationary series. We have taken time series data of stock price and return to demonstrate 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: 2377 Big Edu
Time Series Analysis - 2 | Time Series in R | ARIMA Model Forecasting | Data Science | Simplilearn
 
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This Time Series Analysis (Part-2) in R tutorial will help you understand what is ARIMA model, what is correlation & auto-correlation and you will alose see a use case implementation in which we forecast sales of air-tickets using ARIMA and at the end, we will also how to validate a model using Ljung-Box text. Link to Time Series Analysis Part-1: https://www.youtube.com/watch?v=gj4L2isnOf8 You can also go through the slides here: https://goo.gl/9GGwHG A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R. Below topics are explained in this " Time Series in R Tutorial " - 1. Introduction to ARIMA model 2. Auto-correlation & partial auto-correlation 3. Use case - Forecast the sales of air-tickets using ARIMA 4. Model validating using Ljung-Box test To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 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 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics 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 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Time-Series-Analysis-Y5T3ZEMZZKs&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: 21917 Simplilearn
Time series in hindi and simple language
 
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Thank you friends to support me Plz share subscribe and comment on my channel and Connect me through Instagram:- Chanchalb1996 Gmail:- [email protected] Facebook page :- https://m.facebook.com/Only-for-commerce-student-366734273750227/ Unaccademy download link :- https://unacademy.app.link/bfElTw3WcS Unaccademy profile link :- https://unacademy.com/user/chanchalb1996 Telegram link :- https://t.me/joinchat/AAAAAEu9rP9ahCScbT_mMA
Views: 26352 study with chanchal
Performance 1: Data partitioning for time series
 
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Data partitioning is a fundamental step in predictive modeling. For time series, partitioning is done differently from cross-sectional data. This video supports the textbook Practical Time Series Forecasting. http://www.forecastingbook.com http://www.galitshmueli.com
Views: 4780 Galit Shmueli
Time Series Forecasting Example in RStudio
 
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Demonstrates the forecasting process with a business example - the monthly dollar value of retail sales in the US from 1992-2017. Link to Hyndman and Athanasopoulos: https://otexts.org/fpp2/
Views: 4656 Adam Check
Machine Learning for Time Series Data in Python | SciPy 2016 | Brett Naul
 
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The analysis of time series data is a fundamental part of many scientific disciplines, but there are few resources meant to help domain scientists to easily explore time course datasets: traditional statistical models of time series are often too rigid to explain complex time domain behavior, while popular machine learning packages deal almost exclusively with 'fixed-width' datasets containing a uniform number of features. Cesium is a time series analysis framework, consisting of a Python library as well as a web front-end interface, that allows researchers to apply modern machine learning techniques to time series data in a way that is simple, easily reproducible, and extensible.
Views: 43562 Enthought
Time Series Plots in R
 
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This clip demonstrates how to use xts typed time-series data to create time-series plots in R using ggplot. The full documentation is on: http://eclr.humanities.manchester.ac.uk/index.php/R_TSplots and the data can be downloaded from: http://eclr.humanities.manchester.ac.uk/index.php/R#Intermediate_Techniques Table of Contents: 00:00 - Introduction 00:54 - Importing Data from csv 01:16 - Transforming to xts format 06:55 - Preparing data for ggplot 10:27 - A simple line graph 12:55 - Changing the plot 15:25 - Preparing for multiple grid plot 18:12 - Producing the multiple plot
Views: 36409 Ralf Becker
Time Series data Mining Using the Matrix Profile part 2
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 2 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 1231 KDD2017 video
4 2 Simulating Multivariate Time Series in R
 
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http://quantedu.com/wp-content/uploads/2014/04/Time%20Series/4_2%20Simulate_Multivariate
Views: 10833 Quant Education
Random Forest in R - Classification and Prediction Example with Definition & Steps
 
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Provides steps for applying random forest to do classification and prediction. R code file: https://goo.gl/AP3LeZ Data: https://goo.gl/C9emgB Machine Learning videos: https://goo.gl/WHHqWP Includes, - random forest model - why and when it is used - benefits & steps - number of trees, ntree - number of variables tried at each step, mtry - data partitioning - prediction and confusion matrix - accuracy and sensitivity - randomForest & caret packages - bootstrap samples and out of bag (oob) error - oob error rate - tune random forest using mtry - no. of nodes for the trees in the forest - variable importance - mean decrease accuracy & gini - variables used - partial dependence plot - extract single tree from the forest - multi-dimensional scaling plot of proximity matrix - detailed example with cardiotocographic or ctg data random forest is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi 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: 67072 Bharatendra Rai
Time Series data Mining Using the Matrix Profile part 1
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 1 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 2953 KDD2017 video
TIME SERIES ANALYSIS THE BEST EXAMPLE
 
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QUANTITATIVE METHODS TIME SERIES ANALYSIS
Views: 210549 Adhir Hurjunlal
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka
 
01:07:14
( 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: 63072 edureka!
R tutorial: xts & zoo for time series analysis
 
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Learn more about time series analysis with xts & zoo: https://www.datacamp.com/courses/manipulating-time-series-data-in-r-with-xts-zoo So, what is xts? xts stands for "eXtensible time series"; Objects that are designed to be flexible and powerful - designed to make using time series easy. At the heart of xts is a zoo object, a matrix object plus a vector of times corresponding to each row, which in turn represents an observation in time. Visually, you can think of this as data plus an array of times. To illustrate, we'll create a simple matrix called "x". Each row of our data is an observation in time. To track these observations we have dates in an object called "idx". Note that this index must be a true time object, not a string or number that looks like time. Now, xts lets you use nearly any time class - be it of class Date, POSIX times, timeDate, chron and more - but they need to be time based. Here we are using R's Date objects. At this point though we don't have a time series. We'll need to join these to create our xts object. To do this, we call the xts constructor with our data "x" and pass our dates "idx' to order.by. The constructor has a few optional arguments, the most useful being "tzone" - to set time zones and "unique", which will force all times be unique. Note that xts doesn't enforce uniqueness for your index, but you may require this in your own applications. One thing to note is that your index should be in increasing order of time. Earlier observations at the top of your object, and later more recent observations toward the bottom. If you pass in a non-sorted vector, xts will reorder your index and the corresponding rows of your data to ensure you have a properly ordered time series. Looking back to the example, you can see that we now have a matrix of values with dates on the left. They may look like rownames, but remember its really our index. So what makes xts special? As I mentioned before - xts is a matrix that has associated times for each observation. Basic operations work just like they would on a matrix, almost. One difference you'll note is that subsets will always preserve the object's 'matrix' form - choose one or more than one column will always results in another matrix object. Another difference is that attributes are generally preserved as you work with your data - so if you store something like a timestamp of when you acquired the data in an 'xts attribute' subsetting won't cause that information to be lost. Finally since xts is a subclass of zoo, you get all the power of zoo methods for free. We'll see how important this is throughout the course. One final point before we break out the exercises. Sometimes it will be necessary to reverse the steps we took to create the time series, and instead extract our raw data or raw times for use in other contexts. xts provides two functions that we'll cover here. coredata() is how you get the raw matrix back, and index() is how you extract the dates or times. Simple and effective. Now, let's get to work!
Views: 12771 DataCamp
Algorithms (Time Series Segmentation) | Medical Data Mining L01T05 | Introduction & Scientific Know.
 
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The Online Certificate Program in Genomics and Biomedical Informatics Bar-Ilan University & Sheba Medical Center Course 803.80-675 - Medical Data Mining Spring 2018 Lecturer: Dr. Ronen Tal-Botzer [email protected] Unit L01: Introduction & Scientific Knowledge Topic T05: Algorithms (Time Series Segmentation)
R Time Series Plots
 
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Instructions for using the ggplot2 graphics package to create time series plots in R. For more on statistical analysis using R visit http://www.wekaleamstudios.co.uk and browse.
Views: 21530 ramstatvid
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: 42799 Thales Sehn Körting
Introduction to Time Series Forecasting [AAT-202]
 
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Speaker(s): Peter Myers Imagine taking historical stock market data and using data science to more accurately predict future stock values. This is precisely the aim of the Microsoft Time Series data mining algorithm. Of course, your objective doesn't need to be personal profit to attend this session! SQL Server Analysis Services includes the Microsoft Time Series algorithm to provide an approach to intuitive and accurate time series forecasting. The algorithm can be used in scenarios where you have a historic series of data and where you need to predict a future series of values based on more than just your gut instinct. This session will describe how to prepare data, create and query time series data mining models, and interpret query results. Various demonstration data mining models will be created by using Visual Studio and, in self-service scenarios, by using the data mining add-ins available in Excel.
Views: 462 PASStv
Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong
 
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Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST) in 2008, 2010, and 2014, respectively.
Views: 16479 RISE SICS
Fortune-Telling with Python: An Intro to Facebook Prophet
 
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A pythonic tour of Facebook's time series package. Intermediate level with basic statistics and time data familiarity required. Jonathan Balaban is a senior data scientist, strategy consultant, and entrepreneur with ten years of private, public, and philanthropic experience. He currently teaches business professionals and leaders the art of impact-focused, practical data science at Metis. Founded in 2003, Chicago Python User Group is one of the world's most active programming language special interest groups with over 1,000 active members and many more prestigious alumni. Our main focus is the Python Programming Language. ~~ Connect with us! ~~ chipymentor.org @ChicagoPython chipy.slack.com chipy.org
Build a basic time series model structure and create the predictions
 
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Build the data mining model structure and built the decision tree with proper decision nodes
Views: 84 Msc
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning 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: 73036 edureka!
ARIMAX Modeling in R | Time series Forecasting
 
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ARIMAX is the extended form of the ARIMA (Auto Regressive Integrated Moving Average) model (at Multivariate case ), this is the integration of Auto regressive parameters and Moving Average parameters, this is univariate short term forecasting technique, It works well when our dataset is small. In the ARIMAX model we consider all which consider in the ARIMA and add the exogenous regressive covariate. In the given problem we have to fit the time series model on the price and add the exogenous variable temp, cons, income. Model: Here 𝑦_𝑡: predicted value at time t which is regress on the 𝑦_(𝑡−1), 𝑦_(𝑡−2) and so on and Moving Average values 𝑧_(𝑡−1), 𝑧_(𝑡−2 )etc. And 𝑧_𝑡 is white noise. 𝑋_𝑡 are the another regressive variable.  is the regression coefficient.  is the AR parameter and  is the moving average parameter.
Views: 4684 Analytics University
Machine Learning Real-time - Stock Prediction Application using Shiny & R
 
08:10
Real-time Scenarios - Stock Prediction Application Data Science & Machine Learning Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Get the Code here Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes, Apriori
Views: 27468 BharatiDWConsultancy
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #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). Website: https://www.edureka.co/data-science 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. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 78480 edureka!