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Machine Learning for Business Analytics: A Deep Dive into Data with Python

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Extract richer information from your data using Python and machine learning

Topic: Data
Walter Paczkowski, Ph.D.

It is almost trite to say that businesses, regardless of size, are awash in data. Whole organizations are devoted to collecting, organizing, housing, and protecting that data. Unfortunately, the very people who have the need for what is inside that data, who must make key and crucial business decisions, get, at best, only simple reports, not in-depth analyses with the rich information needed for the best decisions. Decision makers are now questioning the value of their data and demanding better, deeper, more insightful analyses to give them richer information. They expect more for their money and efforts. Business data analysts must extract more useful information from data by pushing the boundaries of their data with advanced statistical and machine learning methods. This course will show you how to go deeper into your existing data sets using advanced statistical and machine learning methods to extract more insight for business decision makers.

This course will teach you advanced statistical and machine learning methods for extracting insight from data. It will build on the techniques introduced in “Business Data Analytics Using Python: Getting the most from your business data”.

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 popular Python packages for business analytics (pandas for data manipulation and scikit-learn for modeling)
  • How to divide data into training and testing data sets for validation
  • How to preprocess data for machine learning
  • The distinction between supervised and unsupervised learning methods, and when and how to use each approach
  • How to perform cross-validation

And you’ll be able to:

  • Analyze a business dataset for key insights using Python packages and advanced analytical methods

This training course is for you because...

  • You are an advanced business analyst, either in a consultancy or internal to a business (whether large, medium, or small), responsible for conducting, analyzing, and interpreting data for key business decisions.
  • Your background is largely analytical and you want to expand your knowledge and toolset of analytical methods.
  • You have a fundamental understanding of business analytics and want to learn advanced methods.


Recommended preparation:

Recommended follow-up:

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 (20 minutes)

  • Lecture: define Machine Learning; uses of Machine Learning for business decision making; brief description of business applications.
  • Group Discussion
  • Q&A

Data Preprocessing (45 minutes)

  • Lecture, demonstrations, and exercises: importance of preprocessing data for Machine Learning; preprocessing steps; forms of preprocessing – transformation, encoding, and dimension reduction.
  • Group Discussion
  • Q&A

Break (5 minutes)

Supervised Learning Methods (75 minutes)

  • Lecture, demonstrations, and exercises: supervised vs unsupervised learning; the General Linear Model as a supervised learning method; the use and creation of training and testing data sets; supervised learning methods – linear models, logistic regression, Naïve Bayes, Support Vector Machines, and decision trees.
  • Group Discussion
  • Q&A

Break (5 minutes)

Unsupervised Learning Methods (50 minutes)

  • Lecture, demonstrations, and exercises: two methods for clustering data – hierarchical clustering and K-Means clustering; models for mixtures of distributions.
  • Group Discussion
  • Q&A

Break (5 minutes)

Model Evaluation (30 minutes)

  • Lecture, demonstrations, and exercises: ways to evaluate model predictability.
  • Group Discussion
  • Q&A

Wrap-up (5 minutes)