Welcome to this second data analytics book! The focus of this book is business applications and problems that are to be solved using SQL and Python. Many books focus on theory or syntax but miss the point of problem solving. Many of my former students have been great coders but struggled solving problems. This book shows many contemporary business problems and solutions that we have built for them.
Another shortcoming with many books is the context of problems that they solve. If a data analytics book focuses on baseball statistics or space travels, it misses the point of solving business problems. By repeatedly being faced with business situations and solving those, students are much more valuable after they leave their training.
Some of the datasets we use in this book include daily stock prices, accounting financial statement data sourced from the Securities and Exchange Commission (SEC), and commodities data sourced from Yahoo Finance. These rich types of data are excellent for business students to gain skills in SQL and Python.
We proceed quickly past calculating averages and top performers to advanced SQL query techniques that require multiple common table expressions and measures of variance among other things. In Python, we proceed past scraping data and reading it into a data frame to correlation and regression analysis. We also show predictive analytics with the focus being on improved decision making.
David Olsen has been an educator since 1993. He received his Ph.D. in Management Information Systems from The University of Arizona in 1993 and taught at The University of Akron accounting department in accounting information systems for five years.
His research interests include database concurrency control and modeling, accounting information systems, and the integration of SQL with other useful tools like XML, SSIS, SSRS, PYTHON, EXCEL, and TABLEAU. David has been published in journals such as Communications of the ACM, Issues in Accounting Education, and the Journal of Database Management; he has published research with many different graduate students over the years. Professionally, Dr. Olsen’s favorite accomplishments are the teaching awards he has received. His favorite conversation is with student that tells him, “Dr. Olsen, I got a job.”
Chapter 1 // Financial Statements Database
Chapter 2 // Introduction to Python Programming
Chapter 3 // SEC Financial Statements – Python, Excel, SQL
Chapter 4 // Entity Relationship (ER) Diagramming _
Chapter 5 // Fortune 100: Company Information and Stock Data
Chapter 6 // Data Cleansing Using SQL _
Chapter 7 // Normalization _
Chapter 8 // Monte Carlo Simulation, Ford Motor Company Stock Prices – Python
Chapter 9 // Commodities Historical Data Sets and Analysis Python and SQL
Correlation Coefficients and Regression _
Building a List of Numbers _
Cast and Convert _
Indexes _
SQL Server Integration Services (SSIS) _
BenFord’s Law _
Retail Gasoline _
XML and JSON _