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)
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.
Â INTRODUCTION TO LINEAR ALGEBRA - BY GILBERT STRANG
APPLIED STATISTICS AND PROBABILITY FOR ENGINEERS â€“ BY DOUGLAS MONTGOMERY
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