Data Science for GTU 18 Course (V - IT - 3151608)

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1. Introduction to Business Analytics : Why Analytics, Business Analytics : The Science of Data-Driven Decision Making, Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, Descriptive, Predictive and Prescriptive Analytics Techniques, Big Data Analytics, Web and Social Media Analytics, Machine Learning Algorithms, Framework for Data-Driven Decision Making, Analytics Capability Building, Roadmap for Analytics Capability Building, Challenges in Data-Driven Decision Making and Future (Chapter - 1) 2. Descriptive Analytics : Introduction to Descriptive Analytics, Data Types and Scales, Types of Data Measurement Scales, Population and Sample, Percentile, Decile and Quartile, Measures of Variation, Measures of Shape - Skewness and Kurtosis (Chapter - 2) 3. Introduction to Probability : Introduction to Probability Theory, Probability Theory - Terminology, Fundamental Concepts in Probability - Axioms of Probability, Application of Simple Probability Rules - Association Rule Learning, Bayes’ Theorem, Random Variables, Probability Density Function (PDF) and Cumulative Distribution Function (CDF) of a Continuous Random Variable, Binomial Distribution, Poisson Distribution, Geometric Distribution, Parameters of Continuous Distributions, Uniform Distribution, Exponential Distribution, Chi-Square Distribution, Student’s t-Distribution, F-Distribution. (Chapter - 3) 4. Sampling and Estimation : Introduction to Sampling, Population Parameters and Sample Statistic Sampling, Probabilistic Sampling, Non-Probability Sampling, Sampling Distribution, Central Limit Theorem (CLT), Sample Size Estimation for Mean of the Population, Estimation of Population Parameters, Method of Moments, Estimation of Parameters Using Method of Moments, Estimation of Parameters Using Maximum Likelihood Estimation. (Chapter - 4) 5. Simple Linear Regression : Introduction to Simple Linear Regression, History of Regression-Francis Galton’s Regression Model, Simple Linear Regression Model Building, Estimation of Parameters Using Ordinary Least Squares, Interpretation of Simple Linear Regression Coefficients, Validation of the Simple Linear Regression Model, Outlier Analysis, Confidence Interval for Regression Coefficients and , Confidence Interval for the Expected Value of Y for a Given X, Prediction Interval for the Value of Y for a Given X. (Chapter - 5) Logistic Regression : Introduction - Classification Problems, Introduction to Binary Logistic Regression, Estimation of Parameters in Logistic Regression, Interpretation of Logistic Regression Parameters, Logistic Regression Model Diagnostics, Classification Table, Sensitivity and Specificity, Optimal Cut-Off Probability, Variable Selection in Logistic Regression, Application of Logistic Regression in Credit Rating, Gain Chart and Lift Chart. (Chapter - 6) Decision Trees : Decision Trees : Introduction, Chi-Square Automatic Interaction Detection (CHAID), Classification and Regression Tree, Cost-Based Splitting Criteria, Ensemble Method, Random Forest (Chapter - 7)

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Pages: 160 Edition: 2023 Vendors: Technical Publications