Learning Objectives :Introduce R as a programming languageIntroduce the mathematical foundations required for data scienceIntroduce the first level data science algorithmsIntroduce a data analytics problem solving frameworkIntroduce a practical capstone case studyLearning 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)INTENDED AUDIENCE: Any interested learnerPREREQUISITES: 10 hrs of pre-course material will be provided, learners need to practise this to be ready to take the course.INDUSTRY SUPPORT: HONEYWELL, ABB, FORD, GYAN DATA PVT. LTD.
Week 1: Course philosophy and introduction to RWeek 2: 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)
Week 3:Statistics (descriptive statistics, notion of probability, distributions, mean, variance, covariance, covariance matrix, understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidence interval for estimates)Week 4: OptimizationWeek 5: 1. Optimization 2. Typology of data science problems and a solution frameworkWeek 6: 1. Simple linear regression and verifying assumptions used in linear regression 2. Multivariate linear regression, model assessment, assessing importance of different variables, subset selectionWeek 7: Classification using logistic regressionWeek 8: Classification using kNN and k-means clustering