Machine Learning for Business Analytics: A Deep Dive into Data with Python
Extract richer information from your data using Python and machine learning
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.
- Familiarity with the basics of Python and Jupyter notebooks, as covered in Business Data Analytics Using Python (live online training course with Walter Paczkowski, Ph.D.).
- Take Business Data Analytics Using Python (live online training course with Walter Paczkowski, Ph.D.) or have equivalent experience.
- If possible, preview Chapters 1-3 in Introduction to Machine Learning with Python (book).
- Read Introduction to Machine Learning with Python (book)
- Take Hands-on Machine Learning with Python: Classification and Regression (live online training course with Matt Harrison)
- Read Thoughtful Machine Learning with Python (book)
- Read Machine Learning with Python Cookbook (book)
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)
- Presentation: Machine learning: uses and importance
- Presentation: Business problem applications
Data Preprocessing (30 minutes)
- Presentation: Standardization
- Presentation: Transformations
- Presentation: Data encoding
- Presentation: Dimension reduction
- Exercises: Solidify understanding of data preprocessing by applying techniques to a transactions data set: transform, one-hot encode, and standardize data
Supervised Learning Methods (60 minutes)
- Supervised vs Unsupervised Learning
- Presentation: Types and Characteristics of Supervised Learning
- Regression Modelling
- Presentation: Generalized Linear Models
- Presentation: Logistic Regression
- Break (5 minutes)
- Presentation: Naïve Bayes
- Presentation: Support Vector Machines (SVM)
- Presentation: Decision trees
- Exercises: Solidify understanding of supervised learning by applying techniques to a transactions data set: estimate a linear model, grow a decision tree, and classify data
Unsupervised Learning Methods (40 minutes)
- Presentation: Types and Characteristics of Unsupervised Learning
- Presentation: Hierarchical clustering: Agglomerative vs Divisive
- Presentation: K-Means clustering
- Break (5 minutes)
- Presentation: Gaussian mixture models
- Exercises: Solidify understanding of unsupervised learning by applying techniques to a transactions data set: cluster data using hierarchical, K-Means clustering, and mixture model.
Model Evaluation (30 minutes)
- Presentation: Cross-validation
- Exercises: Solidify understanding of model evaluation by applying techniques to a transactions data set: evaluate a linear model and interpret results.
Summary and wrap-up (10 minutes)