Data science for business
Business applications of data science is obviously a very broad topic, as data-driven approaches are becoming increasingly integrated into corporate practices. For this reason, this collection will begin as a scaffold of the topics that we are currently using or want to become familiar with in the near future. Be sure to check back regularly as we add add more content!
Getting data
Read data from Excel file:
import pandas as pd
df = pd.read_excel('https://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx')
Often, we need to extract data from print or scanned business documents, which is where these packages can come in handy:
To process OCR results into clean tabular data:
Data wrangling
Given the topic, here we will focus on replicating common Excel functionalities/tasks in Python and R:
More coming soon!
More coming soon!
More coming soon!
More coming soon!
Analysis techniques
This will be a collection of more “traditional” business analytics approaches. For machine learning methods, please see a future post devoted to the topic.
More coming soon!
More coming soon!
More coming soon!
More coming soon!
Reporting
Good results are only useful when they are effectively communicated:
More coming soon!
More coming soon!
More coming soon!