Название: Business Analytics: Data Science for Business Problems
Автор: Walter R. Paczkowski
Издательство: Springer
Год: 2022
Страниц: 416
Язык: английский
Формат: pdf (true)
Размер: 14.0 MB

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of:

1. A theoretical understanding of statistical, econometric, and (in the current era) machine learning methods
2. Data handling capabilities encompassing data organizing, preprocessing, and wrangling
3. Programming knowledge in at least one software language.

These three components form a synergistic whole, a unifying approach if you wish, for doing business data analytics, and, in fact, any type of data analysis. This synergy implies that one part does not dominate any of the other two. They work together, feeding each other with the goal of solving only one overarching problem: how to provide decision makers with rich information extracted from data. Recognizing this problem was the most valuable lesson of all. All the analytical tools and know how must have a purpose and solving this problem is that purpose - there is no other.

Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, Data Science, and market research.

In Machine Learning, the independent variables are called features. In regression analysis, an example of supervised learning, the goal is to estimate the unknown parameters based on the features to give the best estimate of the target. Since unsupervised learning has neither a target nor a set of parameters, the goal is to find clusters or groups of features using algorithms. Unsupervised learning methods are clustering, pattern recognition, and classification identifiers unlike supervised learning methods which are parameter-identifying methods like OLS.

I divided this book into three parts. In Part I, I cover the basics of business data analytics including data handling, preprocessing, and visualization. In some instances, the basic analytic toolset is all you need to address problems raised by business executives. Part II is devoted to a richer set of analytic tools you should know at a minimum. These include regression modeling, time series analysis, and statistical table analysis. Part III extends the tools from Part II with more advanced methods: advanced regression modeling, classification methods, and grouping methods (a.k.a., clustering). The three parts lead naturally from basic principles and methods to complex methods.

Contents:
Part I. Beginning Analytics
1. Introduction to Business Data Analytics: Setting the Stage
2. Data Sources, Organization, and Structures
3. Basic Data Handling
4. Data Visualization: The Basics
5. Advanced Data Handling: Preprocessing Methods
Part II. Intermediate Analytics
6. OLS Regression: The Basics
7. Time Series Analysis
8. Statistical Tables
Part III. Advanced Analytics
9. Advanced Data Handling for Business Data Analytics
10. Advanced OLS for Business Data Analytics
11. Classification with Supervised Learning Methods
12. Grouping with Unsupervised Learning Methods