#PhD Research topic in web mining | Explore Tumblr posts and blogs

eminentstechnologies-blog · 7 years ago

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Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web Server is designed to serve HTTP Content.

#web mining phd program #web mining phd topics #PHD Projects in web mining #PhD Research topic in web mining #PhD in web mining

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derrickcodes · 7 years ago

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Hi, can you help me, if you have some time? I’m in college and I’m supposed to choose my specialty in like a month, but I still don’t know what do I want to do. I feel like there’s so much to learn and I don’t want to miss out on anything. Can you tell me what should I expect from working with different languages? (I’ve tried only like two so far) or do you have any tips which would help me figure it out? Please, you’d literally help save my future (dramatic, I know, sorry xD).

Sure! I understand your sentiment completely. Computer Science is such a vast field, it can feel overwhelming with how much there is to learn. I was in that same boat for the first three years of my comp sci degree and I still don’t fully know what I want to do.

The great thing about computer science is that while it is a relatively new field, it has spread its wings and has branched out in so many ways and has even affected other areas of study. Here are 10 common specializations, what they do, and what some code might look like (when possible):

Software development is what people tend to think of when studying computer science. This typically involves wanting to work in the industry as someone who develops code based on what a client or company wants. You will take courses about the software development process, such as software testing and agile development. There aren’t really any languages I would recommend, since this is such a broad field, but good places to start are C++, Java, C#, and Python. If anything, I would suggest reading further, since software development can be broken down into the other categories. An example of Java code can be seen below (and C++ and C# basically look like this as well).

Game development is another topic people think of with computer science. A lot of our generation grew up playing video games and somewhere along the line thought that they would want to develop games as well. Game developers need to have a good understanding of computer graphics (such as using OpenGL), physics, and computer programming in C++ and C#. A great place to start is looking into Unity. It’s free, it’s easy to use, and it’s what a lot of industry people use today.

Web development has been, currently is, and will always be in high demand. Most interactions people have with computers are through websites, so of course there’s a lot of companies whose development revolves around websites. The standard languages to learn are HTML, CSS, and JavaScript, although if you want an edge up, look into JavaScript libraries and frameworks, like Angular and Node.js. Also, W3Schools will be your best friend. It’s hard to show examples of this that aren’t hundreds of lines long, so here’s a little example showing HTML, CSS, and JavaScript similar to a W3Schools example.

Data science is exploding right now. The world has so much data and we’re just now beginning to analyze all of it. Say you have the history of every user that has ever been shown your ad and who clicked on it and when. Could you use that to determine anything about the effectiveness of the ad, time of day, where it’s displayed, and if they’ll click again? That’s data science. Typical courses include Statistical Computing, Data Mining, and Machine Learning. Typical languages for data science include R and Python. One subtopic that’s really big is machine learning. Can you take the data that you have and have a program “learn” off that data and make predictions about the future? Take a look at this Python code that analyzes a data set and is able to predict whether or not breast cancer is present based on a few attributes:

Information systems is the foundation of both web development and data science, as it involves how and where we store our information and data. You’ll study database management and possibly some cloud storage, since this is usually where we store things. You will want a strong understanding of data structures if you really want to learn the best ways to store things (I’ll give you a hint, databases usually use a variation of Binary Search Trees). You’ll also learn how to retrieve and manipulate the data that is stored. The languages you’ll want is SQL (rather MySQL or NoSQL) and PHP. Some MySQL code for creating a schema with tables will look like this.

Computer engineering is a close friend of computer science, but is mostly focused on the hardware side of things. Computer engineering is all about how you build the computer system. You will spend a lot of time learning the physics that goes into computer design, namely electricity and magnetism. Some classes would include Circuit Analysis, Signals, and Digital Systems, but a lot of it is up to you.

Systems & Architecture is similar to computer engineering, as you’re still focused on being close to the hardware, but you’re more interested in the software side. This was my favorite section of computer science, because you get to learn about computers from a brand new perspective and see how they work down to the electricity flowing through it. Typical courses include Computer Architecture, Operating Systems, Parallel Systems, and the like. You will learn languages like C and Assembly so you can get a good grasp of how fast and powerful a computer can be since you’re almost talking directly to it. For example, this C code is typical practice for interacting with dynamic libraries.

Theoretical computer science is a very intriguing study. Instead of learning about how all these different languages can be applied, you look into what computers are actually capable of. The main courses in any theoretical computer science section are Programming Language Theory, looking into how can you design and classify a programming language, Algorithm Analysis and Design, the different paradigms used to solve different problems, and Theory of Computation, studying what problems can be solved by computers and how quickly can they be solved. Studying this is a good way to get a job in the government, as organizations like the NSA are always looking for people to work on cryptography, which has a strong background in theory.

Scientific computing is the mix of computer science and applied mathematics. You take your understanding of programming and mathematical theory to create computer algorithms to solve problems as fast as they can (and maybe faster than ever before)! You’ll want to have a very strong understanding of linear algebra (the study of matrices), since a lot of computational tasks can be done effectively and efficiently using matrices. Typical courses include Numerical Linear Algebra, Numerical Analysis, and Partial Differential Equations. For this, languages like MATLAB (or its free counterparts Octave or Scilab), Mathematica, and even Fortran are your best bets. A typical career can involve becoming a researcher, or working for a company that relies on the most optimized mathematical code, such as a government agency or somewhere in the finance world. Here’s an example of some code written in Octave to analyze a waveform and reproduce it as a series of numbers (hey, I did a post about this earlier!)

Bioinformatics is the love child of computer science and biology. In this study, you will use what you know about computer science and programming to better understand biological data. You can use this to study the human body, such as the human genome, so we as humans can have a better understanding of what makes us human, or you can apply it and develop medical software. One of my friends got a PhD in bioinformatics and she now writes the software for heart monitors. Since this is somewhat similar to data science, you’ll want to learn Python and R.

There are more specializations, like computer security or networking, but these are the 10 I’m most familiar with. I hope this helped and feel free to reach out to me if you have any more questions!

#computer science #coding #programming #advice #Ask Derrick

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transitionalwordsinessay631 · 5 years ago

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srvivingammunityessay705 · 5 years ago

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fixyourwritinghabits · 8 years ago

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19 Academic Search Engines

I had the chance to stumble upon a Facebook post by UNAM con Conciencia called 19 Buscadores Académicos que todo Tesista debe Conocer. Here’s the list with a brief description. Note: All credit goes to UNAM con Conciencia. The translation of the description is mine.

Dialnet focused on magazines, theses, scientific conferences, and such. Has links to different authors, gathers their work even some quotes. Very helpful in journalistic research, as a documentary source.

Scielo created with the purpose of giving visibility to scientific literature, mostly from the Caribbean and Latin America. Is currently supported by several foundations and associations from all over the world.

ISeek centers on University resources, ONGs, non-profit organizations, and official websites.

Eric virtual library specialized on academic issues. Is a database the American government created on 1964 which contains a variery of references: papers, magazines, and published work. Has an advanced search engine.

Academia.edu users and researchers have the chance to publish their research and essays, and to follow other members with common interests. Is a search engine similar to a social network since it allows interaction between users, such as profile activity, number of visits, followers, comments, etc.

Biology Browser focused on researchers on the field of biology and its current branches. Is made by Reuters and has a news section.

RefSeek its results contain verified web pages resources, books, encyclopedias, newspaper, magazines, research, and published work.

Science Research public and free. Uses different specialized search engines and is capable of avoiding duplicity, selecting useful information, verify it, and more. Has an auto-tour to learn how to use it.

Jurn has over 3000 specialized on arts an humanities magazines. Is a search engine which indexs academic titles and articles, and doctoral theses on several fields as art, ecology, economy, biomedic science, linguistics, and general humanities.

Teseo perfect for students who are on PhD classes and have to write their thesis, as Teseo tells which are the over investigated topics. Is a PhD theses search engine developed by Ministerio de Educación, Cultura y Deportes of Spain.

Redalyc is a scientific hemerotec everyone can acces to. Recetly added a section where you can create a profile and identify certain work.

Chemedia the best about Chemedia is that its resources (documents, articles, papers, and others) can be downloaded on PDF format. Now is ADreamUp, and it seems to be a consulting agency on technology, so cross this one out of the list, unless, of course, you’re curious and want to know about ADreamUp.

PDF SB website where you can read and download e-books for free on PDF. Has specific content on several field investigations and in different languages. Its database has over 71.600 books.

CERN Document Server digital file which features articles, reports and other multimedia content.

World Wide Science holds content from the world and shows the results in a selective way, this is, importance order.

Highbeam Research has an specialize database for professionals and students of different fields. Is a system that includes articles, book quotes, published researchs, and specialized and academic magazines.

Science search engine which has 60 databases from 200 millions of specialized websites on scientific information.

Microsoft Academic Search has over a thousand indexed publictions but is also capable of showing how certain elements are related, a useful trait when it comes to find similar material from different authors who follow similar theories, or studies about an specific topic delimited to a year an field of study. Note from the official website: The original Microsoft Academic Search has been completely decommissioned. Cross this one out too.

Google Scholar contains theses, summaries and books. At the same time allows you to find related referentes.

That’d be the list, 17 useful search engines in total. Use them wisely.

Note 2: Here’s a page where you can find the same list with more description in Spanish. An honorable mention to a comment that says Yahoo Answers should be on the list.

#resources #academic writing #I kept the 19 because reasons

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adalidda · 6 years ago

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Illustration Photo: Autonomous transport robot Omron (credits: PressReleaseFinder / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

Call for Proposals: Amazon Research Awards Program for Colleges or Universities that grant PhD degrees in fields related to Machine Learning

Amazon Research Awards (“ARAs”) are structured as a one-year unrestricted gifts to academic institutions. ARA funding amounts will be determined by Amazon in its sole discretion. ARA funding is not extendable or transferable, but you may submit new proposals for subsequent ARA Program calls.

Focus Areas

Computer vision Recognition: categorization, detection, segmentation Visual search Deep neural network compression and optimization Video understanding: actions, events Large-scale data annotation Computer vision for apparel Human body: detection, tracking, pose Motion: segmentation, tracking 3D modeling: structure-from-motion, slam, stereo and reconstruction Computational photography Computer vision for robotics Faces and gestures Image and video captioning

Fairness in artificial intelligence Transparency, explainability, and accountability in AI systems Theories of computational/algorithm fairness and factors that affect algorithmic trustworthiness Ethical decision-support and decision-making systems Detecting and ameliorating adverse biases in data and algorithms, and fairness-aware design of algorithms Metrics and methods for designing, piloting, and evaluating systems that mitigate against adverse biases and ensure fairness, including the use of human-machine collaboration and decision support Statistical methods for detecting bias in systems as they are operating

Knowledge management and data quality Data cleaning for machine learning Graph mining from knowledge graphs and user behaviors Knowledge embedding Knowledge extraction from unstructured and semi-structured data Knowledge verification Knowledge-based search Large-scale data alignment and integration Leveraging structured knowledge in deep learning and recommendation Quantitative and logical error detection

Machine learning algorithms and theory Active learning and data cleaning Data and resource efficient learning Deep learning and representation learning Fair, explicable and interpretable learning Transfer and meta-learning Online and continual learning Parallel and distributed Learning Robust and privacy preserving learning Reinforcement learning

Natural language processing Advances in neural MT for noisy and user-generated content Chatbots and dialogue systems Detection of inappropriate content Efficient training and fast inference for neural MT Context-aware MT Explainability in neural NLP methods Fact extraction, verification and trustworthiness in unstructured data Multitask and reinforcement learning for MT Named entity translation and transliteration NLP applications in search Question answering Text summarization Narrative understanding Common sense inference

Online advertising AI methods for online advertising Algorithmic marketing Large scale experimentation and testing Learning mechanisms Measurement of brand advertising Online algorithms for targeting, bidding and pricing Optimizing for long term objectives Prediction, forecasting and automated decision making in ad systems Structure of advertising marketplaces

Operations research and optimization Assortment management Management of warehouse operations Marketplace design: incentives/policies for increasing efficiency and growth in a multi-agent marketplace Strategic supply chain management: network design/topology Tactical supply chain management: vendor management (including supplier contract negotiation and procurement), inventory buying, inventory deployment, demand fulfillment Transportation: long-haul operations (including airline operations), last-mile operations Other supply chain optimization topic

Personalization Approaches to estimate quality of recommenders using abundant implicit and sparse explicit feedback Detecting and responding to spam in behavioral data to protect customers in recommendation contexts Scalable NLP approaches for search query understanding for non-English Scalable approaches to detect incorrect catalog information Approaches to identity synonyms in noisy product catalog Item-to-item collaborative filtering using deep learning

Robotics Affective and social interactions Autonomous navigation and mobility Dexterous and reactive manipulation Human machine interaction and collaboration Machine learning and learning from human preferences Motion planning Multi-robot systems and multi-agent pathfinding Object detection and pose estimation Sample-efficient reinforcement learning Semantic scene understanding for robotics Simulation and sim to real transfer SLAM and long-term autonomy Theoretical advances as well as practical applications

Search and information retrieval Multilingual language understanding Conversational search

Security, privacy and abuse prevention ML for malware analysis and detection Browser/device fingerprint and digital forensics Early detection of emerging patterns with limited labeled data (one-shot-learning) Fraud detection and prevention Graph modeling (latent representations from a graph and anomaly detection) Human-in-the-loop machine learning Online and adaptive machine learning Web behavioral modeling, online identity and password-less authentication Threat and intrusion detection for cloud security ML for obfuscation detection from text, image and online behaviors Detection and tracking of online adversarial attempts

Dateline for submission: October 4, 2019, 11:59 PT.

Check more https://adalidda.com/posts/hSXtrWahpN8qHRjQP/call-for-proposals-amazon-research-awards-program-for

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alldealcouponcode · 5 years ago

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Data Science:Data Mining & Natural Language Processing in R

MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R:

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks!

The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

(a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.

(b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.

(c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.

(e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.

More Specifically, here's what's covered in the course:

Getting started with R, R Studio and Rattle for implementing different data science techniques

Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.

How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc

Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE

Statistical analysis, statistical inference, and the relationships between variables.

Machine Learning, Supervised Learning, & Unsupervised Learning in R

Neural Networks for Classification and Regression

Web-Scraping using R

Extracting text data from Twitter and Facebook using APIs

Text mining

Common Natural Language Processing techniques such as sentiment analysis and topic modelling

We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Who this course is for:

Students wishing to learn practical data science and machine learning in R

Students wishing to learn the underlying theory and application of data mining in R

Students interested in obtaining/mining data from sources such as Twiter

Students interested in pre-processing and visualizing real life data

Students wishing to analyze and derive insights from text data

Students interested in learning basic text mining and Natural Language Processing (NLP) in R.

94% off !!! #udemy #course for #Data #Science :Data Mining & Natural Language Processing in R Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples #coupon #deal https://www.udemy.com/data-science-datamining-natural-language-processing-in-r/?couponCode=DATAMINE1

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eminentstechnologies-blog · 7 years ago

Link

Definition: Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web Server is designed to serve HTTP Content. A web server is a specialized type of…

#web mining phd program #web mining phd topics #PHD Projects in web mining #PhD Research topic in web mining #PhD in web mining

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stylemanwoman-blog · 5 years ago

Text

Data Science:Data Mining & Natural Language Processing in R

MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R:

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks!

The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

(a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.

(b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.

(c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.

(e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.

More Specifically, here's what's covered in the course:

Getting started with R, R Studio and Rattle for implementing different data science techniques

Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.

How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc

Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE

Statistical analysis, statistical inference, and the relationships between variables.

Machine Learning, Supervised Learning, & Unsupervised Learning in R

Neural Networks for Classification and Regression

Web-Scraping using R

Extracting text data from Twitter and Facebook using APIs

Text mining

Common Natural Language Processing techniques such as sentiment analysis and topic modelling

We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Who this course is for:

Students wishing to learn practical data science and machine learning in R

Students wishing to learn the underlying theory and application of data mining in R

Students interested in obtaining/mining data from sources such as Twiter

Students interested in pre-processing and visualizing real life data

Students wishing to analyze and derive insights from text data

Students interested in learning basic text mining and Natural Language Processing (NLP) in R.

94% off !!! #udemy #course for #Data #Science :Data Mining & Natural Language Processing in R Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples #coupon #deal https://www.udemy.com/data-science-datamining-natural-language-processing-in-r/?couponCode=DATAMINE1

#nlp #Data Mining #Natural Language Processing

0 notes

fashionandstyletr · 5 years ago

Text

Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples

MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R:

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks!

The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

(a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.

(b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.

(c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.

(e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.

More Specifically, here's what's covered in the course:

Getting started with R, R Studio and Rattle for implementing different data science techniques

Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.

How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc

Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE

Statistical analysis, statistical inference, and the relationships between variables.

Machine Learning, Supervised Learning, & Unsupervised Learning in R

Neural Networks for Classification and Regression

Web-Scraping using R

Extracting text data from Twitter and Facebook using APIs

Text mining

Common Natural Language Processing techniques such as sentiment analysis and topic modelling

We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Who this course is for:

Students wishing to learn practical data science and machine learning in R

Students wishing to learn the underlying theory and application of data mining in R

Students interested in obtaining/mining data from sources such as Twiter

Students interested in pre-processing and visualizing real life data

Students wishing to analyze and derive insights from text data

Students interested in learning basic text mining and Natural Language Processing (NLP) in R.

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awesomeloveandhiphopfan-blog · 5 years ago