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This course provides an in-depth knowledge of R and practical exposure as well as run various Analytics techniques on it. In this course we cover basics of Analytics also so that you become aware of theoretical aspects too.

 About the Course 

R for data Science Course is designed for the aspirants who want to become Data Science Professional can join this course. This training covers all the major topics related to analytics used in data science using R, Like Descriptive Statistics, Probability, Regression, cluster analysis, decision Tree, Random forest and XG-Boost. The R data Science course is designed and delivered by Industry professionals.

Learning Outcomes

After completing this course, students will be able to tackle a data science problem from scratch and build predictive models on the data.

Predictive Analytics vs. Data Science

Predictive Analytics Professionals

  • Analyze data to glean insights
  • and prescribe action
  • Quantitative skills
  • Structured data

Data Scientists

  • Analyze data to glean insights
  • and prescribe action
  • Quantitative skills
  • Unstructured, streaming data
  • Computer science/coding
  • Work on every stage of data life-cycle

Course Curriculum

Introduction- Data Science with R
Why Data Science & Why Now ? 00:00:00
Intuition and Used Cases 00:00:00
Types of Variables 00:00:00
Measures of Central Tendency
Mean , Mode , Median & Standard Deviation 00:00:00
R- Programming Basics 00:00:00
Basics of Probability
Probability Distributions-Binomial, Poisson, Normal 00:00:00
Practice of all topics covered on-R Software 00:00:00
Hypothesis Testing
T-tests- One Sample, Two Sample 00:00:00
Paired t-test 00:00:00
ANOVA
Practice of all topics covered on R
Univariate Regression
OLS Regression 00:00:00
Assumptions of linear regression 00:00:00
Analyzing output of the regression with R 00:00:00
Residual analysis 00:00:00
Hands on practice on R- Software 00:00:00
Multiple Linear Regression
Intuition 00:00:00
Dealing with Multicollinearity 00:00:00
Hands on practice on R-Software 00:00:00
Logistic Regression
Why logistic regression 00:00:00
Intuition 00:00:00
Logit 00:00:00
Log of odds 00:00:00
Interpreting the output 00:00:00
Hands on practice on R-Software 00:00:00
Regularization
PCA, Ridge and Lasso 00:00:00
Hands on practice on R 00:00:00
Cluster Analysis
K-Means clustering 00:00:00
Heirarchical clustering 00:00:00
Hands on practice on R-Software 00:00:00
Decision Trees
Intuition, How decision trees work 00:00:00
Interpreting the output 00:00:00
Hands on practice on R 00:00:00
Bagging, Boosting, Ensembles
Intuition, Bagging 00:00:00
Boosting, Boosting 00:00:00
Hands on practice on R- Software 00:00:00
Random Forests
Intuition 00:00:00
How random forests work 00:00:00
Interpreting the output 00:00:00
Hands on practice on R 00:00:00
XG-Boost
Intuition 00:00:00
How it works 00:00:00
Why it works well most of the time 00:00:00
Interpreting the output 00:00:00
Hands on practice on R-Software 00:00:00

FAQs

  • Any graduate with interest in data science
  • Working professionals looking for a transition to data science career
  • Working professional or managers who want to use data science in their work for decision making
  • Corporate training – Corporates who want to train their employees on data science
  • Students who want to start their career in Data Science
  • College faculties who want to either teach or get a feel of practical data science.

R Programming is one of the most popular language in data science and statistics, It was created by the University of Auckland in New Zealand, professor, Ross Ihaka and Robert in the 1990s as a statistical platform for their students, open-source R has been extended over the decades by thousands of user-created libraries.   R programming is used in the industries like banking, finance, Social Netwroking, Analytics etc. Across the globe. Here are few companies : Bank Of America, Facebook , New York Times, Twitter, Amazon, flipcart, Geneact.

Over the past four years, we’ve seen the preference for open source tools steadily climbing, with 66% of respondents choosing R or Python this year. Python climbed from 20% in 2016 to 26% year2017. (Source: burtchworks)

Data scientists are a type of predictive analytics professional, who applies sophisticated quantitative and computer science skills to both structures and analyze massive stores or continuous streams of unstructured data, with the intent to derive insights and prescribe action. The depth and breadth of data scientists’ coding skills distinguish them from other predictive analytics professionals and allows them to exploit data regardless of its source, size, or format. Through the use of one or more general-purpose coding languages and data infrastructures, data scientists can tackle problems that are made very difficult by the size and disorganization of the data.


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