What you'll learn

  • Programming in R
  • Data wrangling
  • Statistics & Probability
  • Modelling in R
  • Mining Algorithm using R
  • Time Series Forecasting in R and Deployment
  • Data Wrangling using sql and excel
  • Data Analysis and visualization using tableau
  • Case study on R, Excel, Sql and tableau
  • Capstone Project

Why Learn Data Analytics Masters At iNeuron?

Learn data analytics at iNeuron with industry professionals to gain advanced knowledge and develop analytical skills via rigorous curriculum training, in-house live projects and internship opportunities which will enable the candidate to solve any complex business problems with high efficiency and on point accuracy.

  • Students:1400+
  • Duration:9 months
  • Major Projects:20
  • Minor Projects:10
  • Mode:Online
  • Job Assistence:100%
  • Certificate:Yes
  • Mock Interviews:Yes

Course Curriculum

    Programming is a key skill for any professional working with big data. start by learning programming with R.

  • Basics of R
  • Conditional and loops
  • R packages/libraries
  • Data mining gui in R
  • Data structures in R
  • Exceptions/debugging in R

  • A challenge of working with is wrangling it. learn how to read, clean and sort data after learning programming.
  • Reading csv, json, xml, .xlsx and html files using R
  • Etl operations in R
  • Sorting / merging data in R
  • Cleaning data
  • Data management using dplyr in R

  • Data analysis would not be possible without statistics. learn the fundamentals of statistics and probability that are useful to draw inferences from data.
  • Descriptive statistics, random variables, and probability distribution functions
  • Data distributions like uniform, binomial, exponential, poisson, etc
  • Probability concepts, set theory and hypothesis testing
  • Central limit theorem, t-test, chi-square, z-test
  • Central limit theorem
  • Anova

    Machine’s have increased our ability to learn from data radically.learn how to effectively model programs in r for fruitful data analysis.
  • Linear regression model in R
  • Multiple linear regressions model
  • Representation of regression results
  • Non-linear regression models
  • Tree-based regression models
  • Decision tree-based models
  • Rule-based systems

    Machine learning requires knowledge of algorithms. learn the most popular and important algorithms that can help recognize features of data in R.
  • Association analysis
  • Market-based analysis / rules
  • Apriori algorithm
  • Ensemble models – random forest model, boosting model
  • Segmentation analysis- types of segmentation, k-means clustering, bayesian clustering.
  • Feature selection/ dimension reduction- multidimensional scaling, dimension reduction, factor or component analysis.
  • Axes
  • Covariance

    It is one thing to create a model and quite another to deploy it effectively. learn to make timely predictions using models in R with this module.
  • Basics of time series
  • Components of time series
  • Time series forecasting
  • Deploying predictive models
  • Using sql server
  • Using external tools
  • Using big data tools
  • Integrating R with hadoop/spark

    Data wrangling, as has been mentioned, is an important technique in the data analyst’s arsenal. learn additional tools like sql and excel and add to your data wrangling repertoire.
  • Sql queries
  • Integrating with R
  • Deployment and execution
  • Data modelling and formatting using excel
  • Excel formulas to perform analytics
  • Macros for job automation

    The most crucial aspects of working with data are analysis and visualization. learn how to interpret data and communicate insights effectively with tableau.
  • Introduction to tableau and its layout
  • Connecting tableau to files and databases
  • Data filters in tableau
  • Calculation and parameters
  • Tableau graphs and maps
  • Creating tableau dashboard
  • Data blending
  • Creating superimposed graphs
  • Integrating tableau with R

    Finally, round up your experience with critical work experience by engaging in 4 projects and a case study.
  • projects and a case study

  • The course culminates in an enterprise project for a fictitious client that will expose you to different aspects of data analytics. the project is an opportunity for you to test your skills and demonstrate your ability to invent solutions for real world problems.


  • E-commerce case study.
  • Healthcare Analytics.
  • Telemetrics NPS Analytics.
  • Startup growth Rate Analytics.
  • Vehicle Traffic Report Analytics
  • Inventory Management Analytics

Batch Timings & Fee

Fee Structure



Start Date : 28th Mar 2020

Sat & Sun: 10.00 AM - 12.00 PM

Thu(Doubt Clearing): 10.00 PM - 12.00 AM

Data Analytics Masters from iNeuron

Complete your Data Analytics program at iNeuron along with your internship and get your certificate.

  • 6 Months live session training
  • Placement Assistance
  • 3 Months in-house internship

Our Features

6 Months Classroom Training

Once on-board our candidates will go through an intense classroom training session of 3 months conducted by our best team of experienced senior data scientists, who will provide all conceptual knowledge in an innovative as well as an interactive manner.

Career Counselling

After successful completion of our course, we train our candidates via mock interviews, personal interviews, and group discussions as well as provide them with professional mentoring and help them build an attractive resume which will enable them to fetch a lucrative job.

3 Months Of In-House Projects

What makes us different from other training institutes is that we are also into product development and that enables us to provide hands-on experience to our candidates to contribute in live projects, which will result in a deeper understanding of the course with industry-level knowledge.

Admission Process

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