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Business data analytics using Python

Getting the most from your business data

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Topic: Data
Walter Paczkowski, Ph.D.

Many business analysts believe that the only way to analyze data is by creating simple charts and estimating simple linear models. However, to truly extract the key information buried inside your business data—information that is important for making sound and reasonable business decisions—you need to perform sophisticated, high-powered analyses.

In this three-hour hands-on course, expert Walter Paczkowski walks you through data visualization and statistical methods implemented in Python for analyzing business data, whether sales, personnel, logistics, marketing, or financial. You'll explore the nature of business data, the application and interpretation of statistical and machine learning methods for gaining insight into your business, and how to present conclusions in tabular and graphical formats. By the end of the course, you'll be able to use Python to interactively visualize data, estimate predictive models, and distribute reports from Jupyter notebooks.

What you'll learn-and how you can apply it

By the end of this live, hands-on, online course, you’ll understand:

  • How to use Jupyter notebooks to manage an analytical assignment
  • How to use several Python packages for business analysis, including pandas for data manipulation; StatsModels, SciPy, and scikit-learn for modeling; and Seaborn for visualization
  • How to import different data formats (CSV, Excel, etc.) into pandas
  • How to divide data into training and test datasets for validation
  • How to visualize business data
  • How to estimate and interpret statistical models, such as OLS and logistic regression
  • How to cross-validate model estimations
  • How to export Jupyter notebooks to the HTML and PDF formats for sharing

And you’ll be able to:

  • Take a new business dataset and analyze it for key insights using the Python packages
  • Visualize business data for key insights, such as relationships, trends, patterns, and anomalies

This training course is for you because...

  • You're a business analyst responsible for conducting, analyzing, and interpreting data for key business decisions, and you want to learn how to use Python and its main packages.
  • You want to expand your knowledge of and experience with toolsets for analytical methods, such as machine learning, and software so you can provide the best insights to your clients and advance your career.


  • A basic understanding of statistics and regression analysis
  • The ability to interpret basic data visualization tools such as box plots, histograms, and scatter plots
  • Experience working with business datasets
  • Familiarity with business problems and functional areas such as marketing, sales, and finance

Recommended preparation:

About your instructor

  • Walter R. Paczkowski is a market research consultant at Data Analytics Corp., helping companies in a wide range of industries, such as telecommunications, pharmaceuticals, jewelry, food and beverages, and automotive, to mention a few, turn their market data into actionable information. Walter is also an adjunct faculty member of the Department of Economics at Rutgers University. He brings a wealth of knowledge to share about data analysis, drawing on his over 40 years of extensive quantitative experience as an analyst in AT&T's Analytical Support Center, a member of the technical staff at AT&T Bell Labs, head of pricing research in AT&T's Computer Systems Division, and founder of Data Analytics Corp. He was also an adjunct faculty member of the Department of Mathematics and Statistics at the College of New Jersey. Walter is the author of two analytical books—Market Data Analysis Using JMP (SAS Press, 2016) and Pricing Analytics (Routledge, 2018)—with a third forthcoming on quantitative methods for new product development (Routledge, 2020). He holds a PhD in economics from Texas A&M University.


The timeframes are only estimates and may vary according to how the class is progressing

Introduction (25 minutes)

  • Lecture: the importance of information for business decision making; Business Intelligence vs Business Analytics; business problems addressed by Business Analytics.
  • Group Discussion
  • Q&A

Tools for Business Analytics (30 Minutes)

  • Lecture: tools for Business Analytics; spreadsheets issues; the benefits of using Python; the role and power of Jupyter notebooks; a Business Analytics Roadmap.
  • Group Discussion
  • Q&A

Break (5 minutes)

Simple Analytics: Understanding and Preparing Your Data (45 minutes)

  • Lecture, demonstrations, and exercises: role and structure of a Data Dictionary; loading Python packages; importing data into Pandas; checking data for errors and missing values; cleaning/wrangling of data; calculating summary statistics in Pandas.
  • Group Discussion
  • Q&A

Break (5 minutes)

Data Visualization for Insight (45 minutes)

  • Lecture, demonstrations, and exercises: role of scientific data visualization; using Seaborn; what to look for in graphs; dealing with Large-N data sets.
  • Group Discussion
  • Q&A

Break (5 minutes)

Predictive Modeling: Introduction to Machine Learning (75 minutes)

  • Lecture, demonstrations, and exercises: prediction vs forecasting; steps for predictive modeling; creating training and testing data sets; predictive modeling using OLS, logit modeling, and decision trees.
  • Group Discussion
  • Q&A

Wrap-up (5 minutes)