Data Science in R Programming Training in 30 days

  • Become a Data Scientist by getting project experience
  • Stay updated in your career with lifetime access to live classes
  • Get hands-on experience with access to remote Data Science labs
  • Connect with recruiters through video project portfolios

Upcoming Live Data Science in R Programming


26
Aug
Sat and Sun(5 weeks)
7:00 AM - 10:00 AM PST
$399

Want to work 1 on 1 with a mentor. Choose the project track

About Data Science in R Programming Course

Project Portfolio

Build an online project portfolio with your project code and video explaining your project. This is shared with recruiters.

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40 hrs live hands-on sessions with industry expert

The live interactive sessions will be delivered through online webinars. All sessions are recorded. All instructors are full-time industry Architects with 14+ years of experience.

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Remote Lab and Projects

You will be working on real case studies and solving real world problems. You will get access to a remote lab for this purpose. Assignments will be given to get you familiarized with numerous libraries in R which are used by Data Scientists for data analysis.

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Lifetime Access & 24x7 Support

Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.

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Weekly 1-on-1 meetings

You will get 6 one-on-one meetings with an experienced Data Scientist architect who will act as your mentor.

Benefits of Data Science in R Programming

How will this help me get jobs?

  • Display Project Experience in your interviews

    The most important interview question you will get asked is "What experience do you have?". Through the DeZyre live classes, you will build projects, that have been carefully designed in partnership with companies.

  • Connect with recruiters

    The same companies that contribute projects to DeZyre also recruit from us. You will build an online project portfolio, containing your code and video explaining your project. Our corporate partners will connect with you if your project and background suit them.

  • Stay updated in your Career

    Every few weeks there is a new technology release in Big Data. We organise weekly hackathons through which you can learn these new technologies by building projects. These projects get added to your portfolio and make you more desirable to companies.

How will I benefit from the Mentorship Track with Industry Expert?

  • Learn by working on an end to end Data Science in R project approved by Industry Expert.
  • Meet every week, 1-on-1, with an experienced Data Scientist who will act as your mentor.
  • Highlight this globally recognized certificate in your resume and LinkedIn profile.
  • To take advantage of this opportunity, please check "Mentorship Track with Industry Expert" when you enroll.

How will this Data Science in R training benefit me?

  • Learn to use different functions of R to extract, manipulate and clean data from different sources.
  • Learn statistical methods/packages in R to real life industry problems.
  • Learn to implement machine learning in R.
  • Learn to use R tools for Data Visualization.
  • Learn to use ggplot2, plyr, car, lmtest, tseries, DBI, randomForest, XLconnect and all major packages.

What if I have any doubts?

For any doubt clearance, you can use:

  • Discussion Forum - Assistant faculty will respond within 24 hours
  • Phone call - Schedule a 30 minute phone call to clear your doubts
  • Skype - Schedule a face to face skype session to go over your doubts

Do you provide placements?

In the last module, DeZyre faculty will assist you with:

  • Resume writing tip to showcase skills you have learnt in the course.
  • Mock interview practice and frequently asked interview questions.
  • Career guidance regarding hiring companies and open positions.

Data Science in R Programming Course Curriculum

Module 1

Introduction to Data Science Methologies

  • Data Types
  • Introduction to Data Science Tools
  • Statistics
  • Approach to Business Problems
  • Numerical Categorical
  • R, Python, WEKA, RapidMiner
  • Hypothesis testing: Z, T, F test Anova, ChiSq
Module 2

Correlation / AssociationRegressionCategorical variables

  • Introduction to Correlation Spearman Rank Correlation
  • OLS Regression - Simple and Multiple Dummy variables
  • Multiple regression
  • Assumptions violation - MLE estimates
  • Using UCI ML repository dataset or Built in R dataset
Module 3

Data Preparation

  • Data preparation & Variable identification
  • Advanced regression
  • Parameter Estimation / Interpretation
  • Robust Regression
  • Accuracy in Parameter Estimation
  • Using UCI ML repository dataset or Built in R dataset
Module 4

Logistic Regression

  • Introduction to Logistic Regression
  • Logit Function
  • Training-Validation approach
  • Lift charts
  • Decile Analysis
  • Using UCI ML repository dataset or or Built in R dataset
Module 5

Cluster AnalysisClassification Models

  • Introduction to Cluster Techniques
  • Distance Methodologies
  • Hierarchical and Non-Hierarchical Procedure
  • K-Means clustering
  • Introduction to decision trees / segmentation with Case Study
  • Using UCI ML repository dataset or or Built in R dataset
Module 6

Introduction and to Forecasting Techniques

  • Introduction to Time Series
  • Data and Analysis
  • Decomposition of Time Series
  • Trend and Seasonality detection and forecasting
  • Exponential Smoothing
  • Builting R Dataset
  • Sales forecasting Case Study
Module 7

Advance Time Series Modeling

  • Box - Jenkins Methodology
  • Introduction to Auto Regression and Moving Averages, ACF, PACF
  • Detecting order of ARIMA processes
  • Seasonal ARIMA Models (P,D,Q)(p,d,q)
  • Introduction to Multivariate Time series Analysis
  • Using built in R datasets
Module 8

Stock market prediction

  • Live example/ live project
  • Using client given stock prices / taking stock price data
Module 9

Pharmaceuticals

  • Case Study with the Data
  • Based on open set data
Module 10

Market Research

  • Case Study with the Data
  • Based on open set data
Module 11

Machine Learning

  • Supervised Learning Techniques
  • Conceptual Overview
Module 12

Machine Learning

  • Unsupervised Learning Techniques
  • Association Rule Mining Segmentation
  • Conceptual Overview
Module 13

Fraud Analytics

  • Fraud Identification Process in Parts procuring
  • Sample data from online
Module 14

Text Analytics

  • Text Analytics
  • Sample text from online
Module 15

Social Media Analytics

  • Social Media Analytics
  • Sample text from online

Upcoming Classes for Data Science in R Programming

 

August 26th

  • Duration: 5 weeks
  • Days: Sat and Sun
  • Time: 7:00 AM - 10:00 AM PST
  • 6 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll
 

Data Science in R Programming Course Reviews

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FAQs for Data Science in R Programming Online Course

  • Why should I learn R programming for a Data Science career?

    R is one of the most prominent and powerful tools - that is used to extract, clean and build models on a huge amount of data and it has been used in all major companies by leading data scientists. It is one of the easiest tools to learn and implement, for data analysis and it is required that one should know R programming in order to get a job in the field of data analysis. 

  • What are the pre-requisites to learn Data Science in R?

    The pre-requisites to learn Data Science in R is pretty straightforward. You need to have a strong aptitude for numbers, basic programming exposure and college level mathematics mastery.

  • Who will be my faculty?

    All the faculty are leading Data Scientists in multi national analytics firms. They have all been approved to teach Data Science at DeZyre, after going through a series of stringent tests. So you can be assured that whatever you are learning is cutting edge and industry relevant.

  • What will I learn in this course?

    We will begin the course by covering basic syntax in R programming like - small programs to handle data, basic statistical concepts and then move on to different statistical methods to drive or summarize the data to get conclusions. The next level will be to implement all the statistical concepts in R - to solve data analysis problems. The last level will be implementing machine learning techniques to solve real industry problems.

Data Science in R Programming short tutorials

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  • What is Back-propagation learning for Neural Networks?

    In simple terms, back-propagation learning for Neural networks is gradient descent method. In this method, random weights are initialized to the nodes of the neural network.
    Forward propagation through the layers is done to save the output of each layer. Then an error variable is calculated by computing the difference between desired output and actual output. Then the model is back propagated to find the error at each layer, weights adn bias of each layers are updated to minimize the error at each layer. The same process is repeated till the error variable reaches the threshold.

  • What are the advantages of Neural Network over Support Vector Machines?

    Multi-layer feed forward networks of Artificial Neural Networks are comparable to Support Vector Machines. The clear benefit for these models over SVM is the fact that these are parametric models with fixed node size, while SVM's are non-parametric. Any artificial Neural Network is made up of multiple hidden layers with variable number of nodes and bias parameters depending upon number of features. On the other hand, an SVM is consisted of a set of support vectors with assigned weights calculated from training set.
    One of the key advantages of Neural Networks is that they have multiple outputs, whereas any SVM will only produce one output. Therefore to create an n-ary classifier with SVM, we need to create n SVMs and train them separately; while n-ary classifier using a Neural Network can be trained in a single instance.

  • What does .SD stand for in data.table in R?

    SD is a data.table containing the subset of data for every group, excluding the columns of group. It is to be used when grouping by 'i', when keying by 'by', grouping by 'by' and ad hoc 'by'.
    .SD stands for 'Subset of Data.Table'. The full stop in the beginning of character SD is to avoid match with any user-defined column name.
    Consider a data.table:

    DT = data.table(x=rep(c("a","b","c"),each=2), y=c(1,3), v=1:6)
    setkey(DT, y)
    DT
    #      y x v
    # [1,] 1 a 1
    # [2,] 1 b 3
    # [3,] 1 c 5
    # [4,] 3 a 2
    # [5,] 3 b 4
    # [6,] 3 c 6

    Instead of this, you can use .SD :

    DT[, .SD[,paste(x,v, sep="", collapse="_")], by=y]
    #      y       V1
    # [1,] 1 a1_b3_c5
    # [2,] 3 a2_b4_c6

     

  • How to count consecutive patterns in string using R?

    Consider a random string input with recurring characters. The objective of this task is to count the consecutive patterns and print them along with the character of the repetitive strings. This can be done in R using the dplyr library. The code for which is mentioned below:

    library(dplyr)
    library(stringi)
    library(tidyr)
    
    //strings input = "Z,Z,Z,Y,X,X,W,W"
    data_frame(test_string = input) %>%
      group_by(test_string) %>%
      do(.$test_stringstring %>%
           first %>%
           stri_split_fixed(",") %>%
           first %>%
           rle %>%
           unclass %>%
           as.data.frame) %>%
      summarize(output_string = paste(lengths, values, collapse = " , "))

     

  • How to convert lists of different length vectors to data.frame in R?

    Consider a list which contains different length vectors, which needs to be converted to Data.Frame. For example, here is input for a list with two columns with unequal length vectors:

    SampleList <- list(A=c(1,2,3),B=c(1,2,3,4,5,6))

    There are quite a few ways to accomplish this conversion, some of the notable ones are mentioned below:

    data.frame(lapply(SampleList, "length<-", max(lengths(SampleList))))

    or

    ListToDataFrame <- function(SampleList){
      sapply(SampleList, "length<-", max(lenghts(SampleList)))
    }

    Then the following command can be executed for any list by simply calling the function.

    ListToDataFrame(SampleList)
  • How to calculate mean and variance of a data set in R?

    Suppose you have the following data type:

      Name Value
    1 A 10
    2 B 12
    3 C 11
    4 D 13

    and so on. The objective is to calculate mean and variance of the data set in R. There are various commands which can used to do the job:

    with (df, tapply(Value, Name, function(x) c(mean(x), var(x))))

    or

    aggregate(Value ~ Name, df, function (x) c(mean(x), var(x)))

    or

    do.call(rbind, by(df, df$Name, function(x) c(mean(x$Value), var(x$Value))))

    or

    library(data.table)
    setDT(df)[, list(Var=var(value), Mean = mean(value), by = Name)]
  • How to use Linear Regression and Group by function in R?

    A linear regression in R can be performed using either lme4 package or the plyr package or the nlme approach. For a data set which has multiple vectors, a mixed linear model will be a better approach.

    **

    library(nlme)
    lme(response ~ vector1, random = ~vector1|state1, correlation = corAR1(~vector1))

    **

    require(base) 
    library(base) 
    attach(data) # data = your data base
            #state is your label for the states column
    modell<-by(data, data$state, function(data) lm(y~I(1/var1)+I(1/var2)))
    summary(modell)
    
    **
  • What is the Neural Network Activation function in R?

    An activation function converts the weighted inputs of nodes in a Neural Network to its output activation. There are various activation functions used with Neural Networks, below mentioned is a list of few:

    1. Step Function
    2. Linear Combination
    3. Continuous Log-Sigmoid Functions
    4. Continuous Tan-Sigmoid Functions
    5. Softmax Functions
  • What is the difference between Data Science, Big Data and Business Analytics?

    Big Data is the term used to refer to high volume of data, that can be generated from various sources and in different formats. Big Data are often complex and large enough to be processed by traditional database management techniques. Data Science is term which refers to the discipline of analyzing the data. A data scientist creates knowledge out of the data using traditional and non-traditional tools and techniques.

    Business analytics is usually followed by Data Science applications. A Business analyst gathers insight from the previous business performance and results obtained by data analytics.

Articles on Data Science in R Programming

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Recap of Data Science News for June 2017


Data Science News - June 2017 ...

Top Machine Learning Interview Questions and Answers for 2017


According to a list released by the popular job portal Indeed.com on 30 fastest growing jobs in technology- Data science and machine learning jobs dominated the list of top tech jobs. Data scientist job postings saw an increase ...

Recap of Data Science News for May 2017


Data Science News - May 2017 ...

News on Data Science in R Programming

The Economist Intelligence Unit finds UK Companies to seriously lack Data Exploitation skills. July 27, 2016. ComputerWeekly.com


According to an Economist Intelligence Unit (EIU) conducted study, since 2011, the percentage of companies unable to use their data to business solutions has grown to 17% in the UK, while a whopping 24% of the companies worldwide, do not know how to utilize their data in 2016. (Source: http://www.computerweekly.com/news/450301067/UK-firms-miss-out-on-lucrative-data-science-exploitation )

Data Science and Democracy: A delicate balance. July 19, 2016. DemocraticAudit.com


There is a rise in political parties using data science techniques to know the moods and sensitivities of their voters. The question still arises as to whether political parties will be able to understand the needs of the people through data driven results. (Source: http://www.democraticaudit.com/?p=23474 )

Elena Grewal to head the team of Data Scientists at AirBnB. July 9, 2016. LATimes.com


Elena Grewal brings her unique blend resourcefulness to lead the data science team at AirBnB. AirBnB data science team touches every aspect of a visitor’s journey in their site and Elena aims to use that data to increase conversion. (Source: http://www.latimes.com/business/technology/la-fi-himi-grewal-snap-story.html )

The 2016 Leaderboard for Data Science Game has just been released. July 2, 2016. DZone.com


The Data Science Game is a French Association that is promoting data science learning in school to college level students. Stanford, Princeton and City University of London are some of the big names that are participating in this game. (Source: https://dzone.com/articles/data-science-game-2016-leaderboard-update )

What do recruiters look for in a Data Scientist? June 27, 2016. Dataconomy.com


Most recruiters find this question very difficult to answer. The skills and proficiency of a data scientist depends on the industry or the company he/she is being hired for. But to highlight some loose parameters, recruiters look for two basic criteria to be fulfilled – 1. The data scientist applicant has to have a PhD in either a technical or a quantitative field. 2. If hiring for a position higher up – then the person needs to have industry experience. (Source: http://dataconomy.com/recruiters-hiring-managers-looking-data-scientist/ )

Data Science in R Programming Jobs

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Senior Data Scientist

Company Name: Glassdoor
Location: Mill Valley, CA
Date Posted: 07th Oct, 2016
Description:

As a Senior Data Scientist, you’ll be part of our Jobs Data Science and Machine Learning team and build algorithms to deeply understand jobs, their requirements, as well as job seekers and their skills.

You’ll be part of a very small, fast-growing and rapidly innovating team within Glassdoor building our next generation recruiting product. You will have a lot of ownership and impact on one of the most strategic products at Glassdoor.

A typical week would comprise of prototyping models for matching job seekers with jobs, brainstorm...

Junior Data Scientist

Company Name: Axios
Location: Chantilly, VA or Dulles, VA
Date Posted: 03rd Aug, 2016
Description:
  • Able to identify and evaluate standardized methods, models and algorithms to address intelligence problems of limited scale as directed.

  • Provide informal documentation for methods and algorithms use in data science solutions.

  • Able to write well, and create draft briefings and reports.

  • Travel to other Axios Locations or Customer Sites as necessary

  • Understand and adhere to all Axios Ethical and Compliance policies

  • Proactively ensure a safe work environment and adhere to Axios EH&S policies and...

Senior Director, Data Science

Company Name: Integral Ad Science
Location: New York
Date Posted: 28th Jul, 2016
Description:
  • Work on challenging fundamental data science problems in online advertising
  • Measurably impact business KPIs by delivering high quality scalable solutions
  • Discover actionable insights from data and present them through rich visualizations
  • Establish partnerships with product and engineering teams and work closely with other teams
  • Be responsible for design, implementation, deployment and support of key components of data science driven products
  • Recruit and interview top talent, as well as motivate, inspire, mentor and scale a team
  • Know and evange...