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Data Science for Managers

This intensive course will give you the skills to make informed, data-driven decisions and teach you to understand your own organization better through a deeper grasp of data science, AI, and machine learning.

Registration Information Coming Soon

Invest In Your Future

Massive amounts of data are now generated in all areas of business, but a new skillset and mindset are required to transform these raw numbers into real-world, bottom line results. Rather than simply using data as a reporting tool for what has already happened, learn how to find actionable insights that drive your organization to a more successful future.

Most managers have forgotten their advanced mathematics, so we emphasize visualizations of mathematical concepts instead of complicated proofs. Most participants are not professional programmers, so we present basic programming concepts and build to complete solutions.

Novice programmers will learn to read programming code provided in solutions. More advanced students will learn to build solutions using scikit-learn APIs. Teaching assistants are available to provide one-on-one assistance with practical problems. All participants will leave the course able to build, evaluate, and work with real data and real models.

Meet the Instructors

Ken Younge

Lead Instructor Kenneth Younge is Associate Professor and chair of the Technology and Innovation Strategy Lab at GIX partner EPFL in Switzerland. He has started four companies and served as director of development, consultant, CTO, and president. He currently teaches data science for business, computational methods for management, data science for logistics, and technology and innovation strategy to masters, doctoral and eMBA students. His research focuses on computational economics and digital transformation and his lab collaborates with a range of Swiss and US companies on ongoing research projects. This course is based on Professor Younge’s experience, research and consulting work with industry helping executives, managers, engineers, and other professionals understand when machine learning works, when it does not, and how to spot new opportunities to adopt data-driven models—a proven path for advancing careers.

Shwetak PatelGuest Speaker Shwetak Patel is the Washington Research Foundation Entrepreneurship Endowed Professor in Computer Science & Engineering and Electrical Engineering at the University of Washington and Interim Executive Director of the Global Innovation Exchange. He is an accomplished researcher in the areas of human-computer interaction, ubiquitous computing, and sensor-enabled embedded systems. He has also founded several companies, including Zensi, Inc., a residential energy monitoring startup, which was acquired by Belkin, Inc in 2010; a power wireless sensor platform company called SNUPI Technologies, which was acquired by Sears in 2015; and mobile health device company Senosis Health, acquired by Google in 2018. He received his Ph.D. in Computer Science from the Georgia Institute of Technology and is a recipient of many awards, including a MacArthur “Genius” Fellowship (2011) and the Presidential PECASE Award (2016).

Learn How to:

Separate AI reality from hype
Strategically plan a digital transformation
Predict when machine learning will (and won't) work
Understand bias, variance, and bias/variance tradeoffs
Leverage data analysis, visualization, and graphing

Curriculum & Preparation

This deep dive into data science provides a balance of theory and practice through a project-based approach.

  • Course Topics
    • Data sampling, measurement, and wrangling
    • Exploratory data analysis
    • Data description, visualization, and graphing
    • Bias, variance, and the bias-variance tradeoff
    • Model validation and model cross-validation
    • Hyperparameter tuning and information leakage
    • Model evaluation and comparison
    • Model weighting of costs and benefits
    • Ensemble learning and meta-learning
    • Predictive labeling and data augmentation
    • Data-driven business models
    • Big Data, Map-Reduce, and Spark
    • Virtual machines and cloud computing
    • Strategic planning for a digital transformation
    • The management of talent and strategic human capital
  • Schedule

  • Data Analysis Methods & Models
    • Normalizing and standardizing data
    • Linear and log-linear models
    • Non-parametric models, splines and locally-linear models
    • Nearest neighbor and similarity models
    • Agglomerative clustering and K-means clustering
    • Decision trees, bagging, boosting, and random forests
    • Dimension reduction, PCA, t-SNE, and manifold projections
    • Support vector machines
    • Text as data and natural language processing (NLP)
    • Word embeddings and latent topic modeling
    • Feed-forward neural networks
    • Convolutional neural networks
    • Recurrent neural networks, LSTMs, bi-lateral LSTMs
    • Generative adversarial networks
    • Reinforcement learning
  • Prerequisites

    No prior training in Data Science is required, but some knowledge of Python will help you get the most out of the course. We suggest completing the 7-hour Python tutorial by Kaggle prior to the course to help you follow along with the demos and project solutions covered in class.

    For the best class experience you should:

    be familiar with linear algebra (although we use very little math)
    be familiar with statistics (although we will review the basics)
    be conversant in English (the course will be given in English)
    bring a laptop (Mac, Windows, Linux, or Chromebook)

    Several highly-qualified graduate student teaching assistants will be available to work with you one-on-one to answer questions about the programming code.

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