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Learning Objectives :
1. Introduce R as a programming language
2. Introduce the mathematical foundations required for data science
3. Introduce the first level data science algorithms
4. Introduce a data analytics problem solving framework
5. Introduce a practical capstone case study
Learning Outcomes:
1. Describe a flow process for data science problems (Remembering)
2. Classify data science problems into standard typology (Comprehension)
3. Develop R codes for data science solutions (Application)
4. Correlate results to the solution approach followed (Analysis)
5. Assess the solution approach (Evaluation)
6. Construct use cases to validate approach and identify modifications required (Creating)

Week

Topics

1.

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)

2.

† Linear algebra for data science (geometric view - vectors, distance, projections, eigenvalue decomposition)

3.

Statistics (descriptive statistics, notion of probability, distributions, mean, variance, covariance, covariance matrix)

4.

Statistics (Understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidence interval for estimates)

5.

Typology of data Science problems and a solution framework

6.

Univariate and multivariate linear regression Model assessment (including cross validation)

7.

Verifying assumptions used in linear regression , Assessing importance of different variables, subset selection

8.

Introduction to classification and classification using logistics regression ,Classification using various clustering techniques.

10 HRS OF PRE-COURSE MATERIAL ON R WILL BE PROVIDED. PARTICIPANTS NEED TO PRACTICE THIS.


  • ¬†INTRODUCTION TO LINEAR ALGEBRA - BY GILBERT STRANG
  • APPLIED STATISTICS AND PROBABILITY FOR ENGINEERS ‚Äď BY DOUGLAS MONTGOMERY
 
 

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