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Applied Data Science & Machine Learning

Course Summary

The Applied Data Science & Machine Learning training course is designed to take a deeper look at data analysis techniques and presents opportunities for students to practice with relevant data.

The course begins by introducing topics like data cleansing, performance, and operationalizing analytics. Next, students will get more instruction about how Python can be used to analyze data so that they can apply techniques through exercises and hands-on labs. Throughout the course, students will explore a company-specific dataset. The course concludes by building on the Introduction to Data Science & Machine Learning course and takes a deeper dive into Data Science, Data-Driven, and Visualization techniques.

Learn intermediate data analysis techniques and how programming languages can be used to further analyze data.
Developers and developer teams looking to learn some powerful data analysis tools.
Data Engineer - Data Scientist - Software Developer
Skill Level
Fast Track - Targeted Topic - Workshops
3 Days
Related Technologies


Productivity Objectives
  • Describe advanced statistical techniques to support Machine Learning (ML) analytics and conduct multiple tests for confidence.
  • Describe the difference between good and bad data and implement data cleansing techniques.
  • Improve the results of your data analysis by implementing dimensionality reduction.
  • Explain multiple ML analysis techniques in detail and apply those techniques to your own data sets to develop qualitative intent.
  • Apply ML analysis concepts to new projects in order to make better use of data.

What You'll Learn:

In the Applied Data Science & Machine Learning training course, you'll learn:
  • Python Numerics
    • NumPy
    • Pandas
  • Data-Driven Deeper Dive
    • Mean and Median
    • T Tests
    • Z Scores
    • Confidence Intervals
    • Bias and Variance
  • Data Science Deeper Dive
    • ML Checklist
  • Data Cleansing
    • Field validation
    • Value validation
    • Time Series Data
    • Tools
  • Performance
    • Industry Trends
    • GPUs
    • Cloud
    • Clusters
  • Data Visualization
  • Dimensionality Reduction
  • Algorithm Selection
    • Linear Regression
    • Nearest Neighbors
    • Support Vector Machines
    • Bayesian Classifier
    • Decision Trees
  • Operationalizing Models
    • Optimization
    • Deployment
    • Testing
    • Continuous Training
    • Model Updates
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”


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