Building R Packages

Building R Packages is offered on Coursera by Johns Hopkins University,Baltimore,USA. This 4 week course contains almost 30 video lectures.




Business Analytics Using Forecasting

Business Analytics Using Forecasting is offered on futurelearn.com by Prof. Galit Shmueli,National Tsing Hua University.The program has a 6 week period of 3 hours a week




Capstone: Create Value from Open Data

Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free.

Capstone: Create Value from Open Data is offered on Coursera by ESSEC Business School, Cergy, France. The Capstone project is an individual assignment. Participants decide the theme they want to explore and define the issue they want to solve. Their “playing field” should provide data from various sectors (such as farming and nutrition, culture, economy and employment, Education & Research, International & Europe, Housing, Sustainable, Development & Energies, Health & Social, Society, Territories & Transport). Participants are encouraged to mix the different fields and leverage the existing information with other (properly sourced) open data sets.




Case studies in business analytics with ACCENTURE

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Case studies in business analytics with ACCENTURE is offered on Coursera by ESSEC Business School, Cergy, France. This course is RESTRICTED TO LEARNERS ENROLLED IN Strategic Business Analytics SPECIALIZATION as a preparation to the capstone project. During the first two MOOCs, we focused on specific techniques for specific applications. Instead, with this third MOOC, we provide you with different examples to open your mind to different applications from different industries and sectors. The objective is to give you an helicopter overview on what’s happening in this field. You will see how the tools presented in the two previous courses of the Specialization are used in real life projects. We want to ignite your reflection process. Hence, you will best make use of the Accenture cases by watching first the MOOC and then investigate by yourself on the different concepts, industries, or challenges that are introduced during the videos.




Case Studies in Data Mining with R

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Course Description

Case Studies in Data Mining was originally taught as three separate online data mining courses. We examine three case studies which together present a broad-based tour of the basic and extended tasks of data mining in three different domains: (1) predicting algae blooms; (2) detecting fraudulent sales transactions; and (3) predicting stock market returns. The cumulative “hands-on” 3-course fifteen sessions showcase the use of Luis Torgo’s amazingly useful “Data Mining with R” (DMwR) package and R software. Everything that you see on-screen is included with the course: all of the R scripts; all of the data files and R objects used and/or referenced; as well as all of the R packages’ documentation. You can be new to R software and/or to data mining and be successful in completing the course.

The first case study, Predicting Algae Blooms, provides instruction regarding the many useful, unique data mining functions contained in the R software ‘DMwR’ package. For the algae blooms prediction case, we specifically look at the tasks of data pre-processing, exploratory data analysis, and predictive model construction. For individuals completely new to R, the first two sessions of the algae blooms case (almost 4 hours of video and materials) provide an accelerated introduction to the use of R and RStudio and to basic techniques for inputting and outputting data and text.

Detecting Fraudulent Transactions is the second extended data mining case study that showcases the DMwR (Data Mining with R) package. The case is specific but may be generalized to a common business problem: How does one sift through mountains of data (401,124 records, in this case) and identify suspicious data entries, or “outliers”? The case problem is very unstructured, and walks through a wide variety of approaches and techniques in the attempt to discriminate the “normal”, or “ok” transactions, from the abnormal, suspicious, or “fraudulent” transactions. This case presents a large number of alternative modeling approaches, some of which are appropriate for supervised, some for unsupervised, and some for semi-supervised data scenarios.

The third extended case, Predicting Stock Market Returns is a data mining case study addressing the domain of automatic stock trading systems. These four sessions address the tasks of building an automated stock trading system based on prediction models that utilize daily stock quote data. The goal is to predict future returns for the S&P 500 market index. The resulting predictions are used together with a trading strategy to make decisions about generating market buy and sell orders. The case examines prediction problems that stem from the time ordering among data observations, that is, from the use of time series data. It also exemplifies the difficulties involved in translating model predictions into decisions and actions in the context of ‘real-world’ business applications.

What are the requirements?

  • Students will need to install no-cost R software and the no-cost RStudio IDE (instructions are provided).

What am I going to get from this course?

  • Understand how to implement and evaluate a variety of predictive data mining models in three different domains, each described as extended case studies: (1) harmful plant growth; (2) fraudulent transaction detection; and (3) stock market index changes.
  • Perform sophisticated data mining analyses using the “Data Mining with R” (DMwR) package and R software.
  • Have a greatly expanded understanding of the use of R software as a comprehensive data mining tool and platform.
  • Understand how to implement and evaluate supervised, semi-supervised, and unsupervised learning algorithms.

Who is the target audience?

  • The course is appropriate for anyone seeking to expand their knowledge and analytical skills related to conducting predictive data mining analyses.
  • The course is appropriate for undergraduate students seeking to acquire additional in-demand job skill sets for business analytics.
  • The course is appropriate for graduate students seeking to acquire additional data analysis skills.
  • Knowledge of R software is not required to successfully complete this course.
  • The course is appropriate for practicing business analytics professionals seeking to acquire additional job skill sets



Case Studies in Functional Genomics

Case Studies in Functional Genomics is offered on EDx by University of HarvardX,Cambridge, Massachusetts.The time commitment is 2-4 hours for 4 weeks of study




Cleaning Data in R

Cleaning Data in R is offered on Datacamp by Nick Carchedi, Johns Hopkins University data science specialisation developer. This course has 59 exercises and 16 videos.




Clustering & Classification With Machine Learning in R

CLUSTERING & CLASSIFICATION WITH MACHINE LEARNING IN R         

With so many R based Data Science & Machine Learning courses around, why this course?

As the title name suggests- this course your complete guide to both supervised & unsupervised learning using R. This means, this course covers MAIN ASPECTS  of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science.  In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge –and boost your career to the next level.

 BOOST YOUR CAREER TO THE NEXT LEVEL !!

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

But first things first. My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University . 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 . This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.

Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in  Data Science! You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R.

Inside this course, you’ll discover 8 complete sections addressing every aspect of R Machine Learning:

• A full introduction to the R Framework for data science • Data Structures and Reading in R, including CSV, Excel and HTML data • How to Pre-Process and “Clean” data by removing NAs/No data,visualization • Machine Learning, Supervised Learning, Unsupervised Learning in R • Model building and selection !

With this course, you’ll have the keys to the entire R Machine Learning Kingdom!

You DO NOT need any prior R or Statistics/Machine Learning Knowledge to get Started

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 data science packages like caret to work with real data in R. You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. We will work with real data and you will have access to all the code and data used in the course.

Most importantly, you will learn to implement these techniques practically using R.Remember, I am always around to support my students!!.

Who is the target audience?
  • It will be beneficial to have prior exposure to R programming (not essential)
  • Students interested in getting started with data science applications in the R and RStudio environment
  • People wanting to master the R and RStudio environment for data science
  • Students wishing to learn the implementation of unsupervised learning on real data
  • Students wishing to learn the implementation of supervised learning (classification) on real data using R



Clustering and Finding Patterns

Clustering and Finding Patterns is offered on Datasociety by Savanna Flakes. This course is designed for students with a basic familiarity with R and some experience with data analysis and data manipulation. In just 100 minutes of instructional time, students learn how to think about finding patterns, perform different types of clustering analyses, and evaluate the quality of the results.




Code Clinic: R

Code Clinic: R is offered on Lynda by Mark Niemann-Ross. This is an intermediate level course.