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Business data analytics using Python

Getting the most from your business data

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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 has a Ph.D. in Economics from Texas A&M University (1977). With 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 at AT&T's Computer Systems division, and founder of Data Analytics Corp., he brings a wealth of knowledge to share about data analysis. His work as a market research consultant is focused on helping companies in a wide range of industries, such as telecommunications, pharmaceuticals, jewelry, food & beverages, and automotive to mention a few, to turn their market data into actionable market information. Walter is also currently on the faculty of the Department of Economics, Rutgers University (Adjunct) and was formerly with the Department of Mathematics & Statistics, The College of New Jersey (Adjunct). Walter is also 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, 2019). You can learn more about Walter and his consulting company, Data Analytics Corp., at www.dataanalyticscorp.com.


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

Introduction (10 minutes)

  • Lecture: The role and importance of information; business intelligence versus business data analytics; business data analytics overview; types of business problems
  • Group discussion
  • Q&A

Tools for business analytics (10 minutes)

  • Lecture: The benefits of using Python versus Excel; prerequisites and background overview
  • Group discussion
  • Q&A

Simple analytics: Understanding and preparing your data (30 minutes)

  • Lecture and hands-on exercises: Introduction to Python, Jupyter, and pandas; importing packages into Python; importing your data into pandas; checking your data; cleaning and wrangling your data; summary statistics in pandas
  • Group discussion
  • Q&A
  • Break (5 minutes)

Data visualization for insight (45 minutes)

  • Lecture and hands-on exercises: Distributions—histograms, box plots, and kernel density plots; relationships—scatter plots, scatterplot matrices, and line plots; advanced graphs
  • Group discussion
  • Q&A
  • Break (5 minutes)

Predictive modeling: Introduction to machine learning (60 minutes)

  • Lecture and hands-on exercises: Creating training and test datasets; types of models—OLS and logistic; model instantiation; model fit; fit summary; introduction to decision trees; building decision trees; interpreting decision trees
  • Group discussion
  • Q&A

Exporting to HTML and PDFs (10 minutes)

  • Lecture and hands-on exercises: Why export?; ways to export notebooks; slide shows
  • Q&A

Wrap-up and Q&A (5 minutes)