APPLIED COURSE IN DATA ANALYTICS USING R

25,000.00

 

Next course starts: 11/08/2018 Intended audience: Professionals with a few years of experience who expect to transition to data analytics projects.

Objectives: To introduce

• R as a programming language
• mathematical and statistical foundations required for data science
• first level data science algorithms
• a data analytics problem solving framework
• a practical capstone case study

Learning Outcomes:

• Describe a flow process for data science problems (Remembering)
• Classify data science problems into standard typology (Comprehension)
• Develop R codes for data science solutions (Application)
• Correlate results to the solution approach followed (Analysis)
• Assess the solution approach (Evaluation)
• Construct use cases to validate approach and identify modifications required (Creating)

 





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Description

 

+ Additional Information: Significant pre-course material on R will be provided. Will require about 10 hours of pre-course work by the participants on R. R is open source software and GDPL will specify the version that the participants should download and use.

 

  Module description Duration   Module description Duration
First session on R training 3H Univariate and multivariate linear regression 1H
Linear algebra for data science (algebraic view – vectors, matrices, product of matrix & vector, rank, null space, solution of over-determined set of equations and pseudo-inverse) 2H Model assessment (including cross validation) 1H
R practicals – Data imputation case study 1H Verifying assumptions used in linear regression 1H
Linear algebra for data science (geometric view – vectors, distance, projections, eigenvalue decomposition) 2H Assessing importance of different variables, subset selection 1H
R practicals – Data imputation (noisy case), Process monitoring and visualization 2H Data science examples of regression in R 4H
Statistics (descriptive statistics, notion of probability, distributions, mean, variance, covariance, covariance matrix) 2H Introduction to classification and classification using logistics regression 2H
R practicals – Use of descriptive statistics in data science 1H Classification using logistic regression in R 1H
Statistics (Understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidence interval for estimates) 2H Classification using various clustering techniques 2H
R practicals – Hypothesis testing examples from data science, understanding calculations related to distributions in R 2H Classification using clustering techniques in R 2H
Typology of data Science problems and a solution framework 1H Introduction to advanced topics 2H
Use of framework for development of solution strategy for four case studies 2H Capstone case study 3H

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