Training Session with Dr. Kirk Borne is scheduled for October 8th and October 14th at 12 PM (ET)

Training duration is 4 hours on each day

This training session will consist of: 4 modules, each module’s duration is 45-50 minutes. Each module will be followed by a 10 minutes Q&A session. The instructor will be available to answer questions throughout the training.

Regular Price: $349.80

20% discount ends soon

Price with 20% discount

Instructor

Principal Data Scientist And Executive Advisor, Booz Allen Hamilton

Dr. Kirk Borne

Dr. Kirk Borne is the Principal Data Scientist, Data Science Fellow, and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. He has worked there since 2015. He provides thought leadership, mentoring, training, and consulting activities in data science, machine learning, and AI across multiple disciplines. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years in the graduate (Computational Science and Informatics) and undergraduate (Data Science) degree programs. He was a co-creator of the world’s first undergraduate data science degree program in 2006. Prior to that, he spent nearly 20 years supporting data systems activities for NASA space science programs, including a role as NASA's Data Archive Project Scientist for the Hubble Space Telescope and as Contract Program Manager in NASA’s Space Science Data Operations Office. He is co-author of the e-book “10 Signs of Data Science Maturity”, co-author on a new book “Demystifying AI for the Enterprise”, and author of many hundreds of scientific research articles and blogs. Dr. Borne has degrees in physics (B.S., LSU) and astronomy (Ph.D., Caltech). He is an elected Fellow of the International Astrostatistics Association for his contributions to big data research in astronomy. In 2020, he was elected a Fellow of the American Astronomical Society for lifelong contributions to the field of astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including keynote presentations at dozens of data science, AI, and analytics conferences. He is an active contributor on social media, where he promotes data literacy for all and has been named consistently among the top worldwide social influencers in big data, data science, machine learning, and AI since 2013.

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What will you learn?

  • How to identify modeling opportunities and categorize them (Detect, Discover, Predict, Optimize)

  • How to select appropriate modeling components and ML algorithms for specific use cases

  • How to design your own analytics solutions

  • How to solve a broad range of problems

  • How to generate value from the data assets in your organization

  • How to communicate to your stakeholders the importance and meaning of models in data-intensive environments

  • Work on data literacy exploratory data analysis use cases that broaden and deepen one’s understanding and abilities in insights discovery and value creation from data

Course Abstract

Modeling is a fundamental aspect of any science. This fact is particularly apparent in data science. The key aspects of modeling that make it important for science are: (1) models are representations of things that cannot be fully understood or known (e.g., predictive models are essential to predict a future outcome, unless you have access to a time machine that we don’t know about); (2) models can give us new insights into those things, including their behaviors, responses, and characteristics (especially in previously unseen conditions), thereby potentially revealing causal factors for observed outcomes and informing prescriptive actions to optimize outcomes; (3) models provide testable predictions to validate our assumptions and hypotheses about things (otherwise, it’s not science); and (4) models can help answer questions that are not otherwise answerable (e.g., we can pose “what if” scenarios safely in a model environment that we would not be able or allowed to test in a real life situation). 

In data science, we use observation (data, evidence) to inform and inspire our models, we use machine learning (algorithms that learn from patterns in the data) to build testable models, and we use the scientific method to verify, validate, and/or refine our models. The ideal goal of these activities is discovery from data, specifically actionable insights discovery. 

This two-part workshop on modeling and machine learning in data science follows two main threads: foundational concepts and practical examples. These two threads will sometimes mix and intertwine, which will help to reinforce the importance of both aspects of modeling. As someone once said, “In theory, theory and practice are the same. In practice, they are not.” We will prove that! Numerous examples of machine learning modeling (and other types of modeling), both common and novel, will be presented. The ultimate goals of the workshop are to help develop modeling intuition, data and analytic literacy, scientific reasoning, data-driven curiosity, critical thinking, and an experimental mindset that will contribute to establishing a strong foundation for a career in data science.

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Course Schedule

Session 1: Theoretical and Foundational Concepts of Modeling and ML (4 hours)

  • Training Overview and Data Science Preliminaries

  • Introduction to Modeling Concepts

  • Supervised vs. Unsupervised Modeling

  • Insights Discovery and Generalization

  • Supervised Learning Concepts 

  • Predictive vs. Prescriptive Modeling

  • What does Cognitive have to do with it?

  • The Two Most Important Things in Data Science

  • Optimization and Feedback Loops in Modeling

  • Cold-Start Modeling: When the Data Becomes the Model (Unsupervised ML)

  • Machine Learning vs. Deep Learning

  • Common Business Modeling Examples

  • The OODA Loop in Decision Science and Data Science

  • When Predictive Modeling Fails

  • Ethical Modeling 

  • Enriching Your Models with Smart Data (Semantic Tags, Labels, Annotations)

  • Exploiting High-Variety Data to Achieve Better Model Outcomes

  • Steps to Data Analytics Mastery 


Session 2: Typical and Novel Applications of ML Algorithms (4 hours)


  • A Fishy Example of Cost-Sensitive Classification

  • A 12-step Analytics Program in Healthcare and Medicine

  • ML and AI Making Big Moves in Marketing Analytics

  • Exploratory Data Analysis: Successes, Insights, and Lessons

  • Data Literacy Exercises: Strengthening Your Data Science Abilities

  • Surprise Discovery in Regression Analysis

  • Neural Networks in Climate Modeling

  • ICA vs. PCA: The Cocktail Party Problem 

  • Graph Mining: Connecting the Dots that Aren't Connected

  • Forecasting 2.0: Beyond Traditional Forecasting

  • Clustering Analysis: Down to Earth, and Up to Space

  • Association Mining for Predictive Modeling

  • The Ways of Bayes: Classification, Markov Models, Missing Value Imputation, Causal Analysis

  • Precursor Analytics with Statistical Clustering

  • The Internet of Context: Forecasting-as-a-Service

  • Matching ML Algorithms to Business Analytics Problems

  • The Keys to a Successful Data Science Career

Training starts in:

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Who will be interested in this course?

  • Data scientists, data analysts, business intelligence practitioners, data users, and other analytics-related professionals are the target audience for this training. Generally, this training is for anyone:

  • Anyone who seeks to understand how machine learning works and how ML models can deliver actionable insights, decision support, and value to their organization

  • Anyone who wants to become more knowledgeable and proficient in identifying machine learning opportunities and in contributing to ML modeling applications

  • Anyone who seeks to learn the power of machine learning models in thought and action to progress in your own career journey (e.g. from data analyst to data scientist).

Which knowledge and skills you should have?

  • There are no specific coding skills required, though familiarity with computational concepts will be helpful, particularly related to modeling.

  • Basic quantitative skills and math skills are essential. Some knowledge of calculus and linear algebra is helpful, but not required.

  • Some experience with machine learning will make this workshop easier to follow, but all that is required is basic knowledge of the concepts. The workshop will explain some of the most common ML algorithms in sufficient detail for your own use and will demonstrate their application in the context of the practical examples presented in the workshop.

What is included in your ticket?

  • Access to the 2 live QA and blended training sessions with the Instructor

  • Access to the on-demand recording

  • Certification of completion