Data Science with R.

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.
  •   Certificate : by TechSim+


Why Should I Learn Data Science with R from Techsim+?
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
According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
Wired.com points to a report by Glassdoor that the average salary of a data scientist is ₹ 77,16,085
Randstad reports that pay hikes in the analytics industry are 50% higher than IT



What are the course objectives?
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.
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.
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.



What skills will you learn?
This data science training course will enable you to:

Gain a foundational understanding of business analytics.
Install R, R-studio, and workspace setup, and learn about the various R packages
Master R programming and understand how various statements are executed in R
Gain an in-depth understanding of data structure used in R and learn to import/export data in R
Define, understand and use the various apply functions and DPLYP functions
Understand and use the various graphics in R for data visualization
Gain a basic understanding of various statistical concepts
Understand and use hypothesis testing method to drive business decisions
Understand and use linear, non-linear regression models, and classification techniques for data analysis
Learn and use the various association rules and Apriori algorithm



Who should take this course?
There is an increasing demand for skilled data scientists across all industries, making this data science course well-suited for participants at all levels of experience. We recommend this Data Science training particularly for the following professionals:

IT professionals looking for a career switch into data science and analytics
Software developers looking for a career switch into data science and analytics
Professionals working in data and business analytics
Graduates looking to build a career in analytics and data science
Anyone with a genuine interest in the data science field
Experienced professionals who would like to harness data science in their fields


Key Features

Master the Concept of your Training Module

20+ Real-Time Industry-Based Projects

Instructor-led training

Hands on Practical Classes

One Year Training Menbership with TechSim+

What You Will Learn
In this Journey

  • Stage 1

    1. Introducing R Programming

    Using intraction with R

    Functions

    Data Pipelines

    Exercies

  • Stage 2

    2. Reproduciable Analalysis

    The Markdown Language

    Running R code in Markdown Document

    Exercises

    Create an R Markdown Document

  • Stage 3

    3. Data Manipultion

    Example of Reading And Formatting Datasets

    Manipulating Data with dplyr

    Some Useful dplyr Functions

  • Stage 4

    4. Visualizing Data

    Basic Graphics

    The Grammar of Graphics and the ggplot2 package

  • Stage 5

    5. Work With Large Database

  • Stage 6

    6. Supervised Learning

    Machine Learning

    Supervised Learning

    Specifying Models

    Validating Models

    Evaluating Classification Models

    Examples of Supervised learning Package

  • Stage 7

    7. Unsupervised Learning

    Dimensionality Reduction

    Clustering

    Dealing with Missing Data in the HouseVotes84 Data

    Exploring the data

  • Stage 8

    8. More R Programming

    Expressions

    Basic Data types

    Data Structures

    Control Structures

    Functions

  • Stage 9

    9. Advance R Programming

    Working with Vectors and Vectorizing Functions

    The apply Family

    Advanced Functions

    Functional Programming

  • Stage 10

    10. Object Oriented Programming

    Immutable Objects and Polymorphic Functions

    Data structures

    Polymorphic Functions

    Class Hierachies

  • Stage 11

    11. Buliding an R Progamming

    Creating an R Package

    Description

    Roxygen

  • Stage 12

    12. Testing and Package Checking

    Unit testing

    Automating Testing

    Using Random Numbers in Test

  • Stage 13

    13. Version

    Version Control and Repositories

    Using git RStudio

  • Stage 14

    14. Profilling and Optimizing

    Profiling

    A Graph-Flowing Algorithm

    Bayesian linear Regression

    Sample from a Multivariate Normal Distribution

    Formulas and Their Model Matrix

    Interface to a blm class

    Model Method

    Building an R Package for blm

    Adding READEME and NEWS files to your package