Practical Business Analytics Using R and Python: Solve Business Problems Using

Section 1: Introduction to Analytics In this section, we discuss the necessary foundations required to perform data analytics. We discuss different analytics terms, basics statistics and probability theory, descriptive statistics including various plots, and various measures for evaluating your predictive models. Chapter 1: Business Analytics Revolution Chapter 2: Foundations of Business Analytics Chapter 3: Structured Query Language (SQL) Analytics Chapter 4: Business Analytics Process Chapter 5: Exploratory Data Analysis (EDA) Chapter 6: Evaluating Analytics Model Performance Section II: Supervised Learning and Predictive AnalyticsIn this section, we introduce statistical learning models and machine learning models. We present various regression analysis and classification analysis. We also discuss logistic regression and end our discussion by introducing Neural Network and gradient descent algorithms. Chapter 7: Simple Linear Regressions Chapter 8: Multiple Linear Regressions Chapter 9: Classification Chapter 10: Neural Networks Chapter 11: Logistic Regression Section III: Time series modelsIn this section, we introduce optimization models and Time series analysis. In time series, we discuss different forecasting models, and in optimization models, we introduce both linear and non-linear optimization models.Chapter 12: Time Series - Forecasting Section IV: Unsupervised model and Text MiningIn this section, we discuss two popular unsupervised models - cluster analysis and relationship data mining techniques. Finally, we end this section by introducing text mining and NLP and briefly introducing big data. Chapter 13: Cluster Analysis Chapter 14: Relationship Data Mining Chapter 15: Mining Text and Text Analytics Chapter 16: Big Data and Big Data Analytics Section V: Business Analytics ToolsThis is the last part. In this section we This section summarizes what we have learned in the earlier section by working on some case studies. We work on practical cases using public datasets using both 'R' and 'Python'.Chapter 17: R programming for Analytics Chapter 18: Python Programming for Analytics