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Cut through the deluge of data science material by focusing on the essentials. This course uses illustrations, code-based examples and case studies to demonstrate essential data science topics and the practical application of existing machine learning frameworks. By the end of the course, you will have trained and validated machine learning algorithms to make continuous-value as well as discrete-value predictions from data sources relevant in business and engineering. You will also be able to make statistically sound, data-driven decisions in business from sales and production data.

This course includes:

  • Data Topics
    • Bias-variance tradeoff
    • Regression: linear, logistic, and multivariate
    • Regularization: L1 and L2
    • Inferential statistics: moods median, t-tests, f-tests, ANOVA
    • Descriptive statistics: mean, median, mode, kurtosis, skew
    • Beyond regression coefficients: tree-based and resampling methods
    • Unsupervised learning: clustering and dimensionality reduction
  • Software topics
    • Unit tests
  • Sessions
    • S1: Regression and Analysis
    • S2: Inferential Statistics
    • S3: Model Selection and Validation
    • S4: Feature Engineering
    • S5: Unsupervised Learning: Clustering and Dimensionality Reduction
    • S6: Bagging: Decision Trees and Random Forests
    • S7: Boosting: AdaBoost and XGBoost
  • Labs
    • L1: Descriptive Statistics Data Hunt
    • L2: Inferential Statistics Data Hunt
    • L3: Feature Engineering
    • L4: Supervised Learners
    • L5: Writing Unit Tests
  • Projects
    • P1: Statistical Analysis of Tic-Tac-Toe Games
    • P2: Heuristical Tic-Tac-Toe Agents
    • P3: 1-Step Look Ahead Agents
    • P4: N-Step Look Ahead Agents

Quick Facts

Duration: 8 days, 20 class hours


Spring 4/18/2022 – 4/28/2022

Days: Monday – Thursday

Time: 5:00p.m. – 7:30p.m. PT

Format: Online instruction

      Cost: $1,000

      Prerequisites: Python Foundations

or equivalent background in Python


























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Meet the Instructor

Dr. Beckner is a GIX faculty member, an instructor for the University of Washington MS in Technology Innovation, a Data Science Advisor for the Pfaendtner Research Group, and partner at MFG Analytic, where he works with manufacturing clients to optimize their production processes using cloud-based tools. He received his Ph.D. in Chemical Engineering Data Science from the University of Washington and his B.S. in Chemical Engineering from the University of Texas at Austin. His consulting work inspired him to help organizations streamline their workflows and increase profit margins by training in-house employees to better understand and use data. Read his full biography here.

Instructor’s Take:Many modern machine learning techniques can be well understood with the topics this course teaches. Complex concepts like bias-variance trade-off, irreducible error, and regularization are demonstrated with visualizations and examples. You will make sense of your results with statistical analysis and procure actionable insights that will set a baseline for more advanced machine learning strategies.

I have been given a statement of work related to sensor fusion and I've started doing the data manipulation and exploration for that with Pandas and I definitely would not have been able to do that prior to taking these courses.

Matt, Past Participant

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