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This interactive course aims to equip students with an in-depth comprehension of data science principles and methodologies, with a strong emphasis on practical applications. Participants will develop tangible skills and hands-on experience in utilizing data science methods to derive significant insights from varied datasets. Encompassing topics such as data analysis, data engineering, machine learning, data visualization, and business analytics, the course primes students for real-world challenges across diverse industries.
Course Learning Highlights:

  • Python Programming - Data Science Libraries
  • Data Analysis - Pandas for Python, Exploratory Data Analysis (EDA) techniques
  • Data Visualization - Visualization tools and techniques, Matplotlib and Seaborn
  • Data Engineering - Tools and techniques
  • Machine Learning - Supervised Learning and unsupervised learning, Model evaluation and hyperparameter tuning
  • Business Analytics - Descriptive analytics, Predictive analytics, Healthcare analytics, Improving decision-making skills

Course Learning Outcomes:
  • Learn to apply statistical methods to address a variety of real-world problems
  • Manipulate extensive datasets to prepare them for thorough data analysis
  • Evaluate data visualizations based on their design effectiveness
  • Learn to analyze data using different techniques and algorithms
  • Implement algorithms in real-world scenarios, refine obtained models, and report on predicted accuracy using these models
Software: Anaconda Navigator will be used
Hardware: A computer with a multi-core processor, with 16GB RAM memory and minimum 512GB SSD of storage
Course Typically Offered: Online in Summer and Winter
Prerequisite: Previous knowledge of Python, Calculus I, Linear Algebra and Statistics or equivalent is required before taking this course
Next Steps: Upon completion, consider additional coursework in our specialized certificate in Machine Learning Methods to continue learning
Contact: For more information about this course, please contact

Course Number: CSE-41401
Credit: 3.00 unit(s)

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