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 skills

Explore programming to access, prepare, analyze, and present data.

Explore the power and convenience of Python notebooks

Traditional 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 analogies

As appropriate, analogies are presented relating Python concepts to spreadsheets to leverage students' prior knowledge. .

Animate learning

Key programs are animated and narrated using "CodeInMotion" videos narrated by the author that reveal the "living and breathing" dynamics of code.

Cement understanding

In-class exercises assure that students can apply each successive concept.

Prepare students for business

Highly-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.