Python for Data & Analytics: A Business-Oriented Approach, Edition 2.0
Groner, Daniel H.
Synopsis "Python for Data & Analytics: A Business-Oriented Approach, Edition 2.0"
*** An eTextbook version is available at VitalSource.com for $63.85USD. ISBN: 978-1-958303-31-3. ***Python for Data & Analytics introduces essential programming concepts using Python, pandas, and other packages for the purpose of accessing data and performing analyses, and developing applications. The presentation presumes no prior programming experience. It is designed for introductory and more advanced programming courses in a data analytics, information systems, or business program. To this end, business examples are emphasized throughout. Various topics often covered in a computer-science-oriented course are purposefully not included in this textbook including topics like recursion and sorting algorithms.Edition 2.0 has been updated to provide coverage of more key topics (e.g., web APIs, simulation, NumPy, Matplotlib ), as well as "In Brief" appendixes allowing accelerated coverage of certain topics (lists, dictionaries, reading files).FEATURES: Impart data analytics skillsExplore programming to access, prepare, analyze, and present data.Explore the power and convenience of Python notebooksTraditional introductions teach computer programming primarily as a sequence of statements called programs. Today, Python is still used in programs but it is also used for interactive data analysis and in a hybrid technique called a notebook. This text introduces all three approaches.Jumpstart learning with spreadsheet analogiesAs appropriate, analogies are presented relating Python concepts to spreadsheets to leverage students' prior knowledge. .Animate learningKey programs are animated and narrated using "CodeInMotion" videos narrated by the author that reveal the "living and breathing" dynamics of code.Cement understandingIn-class exercises assure that students can apply each successive concept.Prepare students for businessHighly-relevant examples are drawn from finance, logistics, marketing, accounting, health, and other functional areas. They are real (or realistic), not too complex, and explained in detail.
