Overview

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Data Science techniques are very powerful predictive tools for all types of organizations. Recent advancements in data collection, data science libraries and more powerful computers have put advanced data science within reach of all size organizations.

This series of courses uses easy to learn, state of the art, free tools of Python, Scikit-Learn and Tableau to perform advanced data science. Most examples use real dataset so the skills that you are learning produce real results.

Courses cover numerous useful topics. Data preprocessing shows students how to easily normalize data and how domain space reduction can improve results. Supervised Learning algorithms like K-Nearest Neighbor, Regression, Decision Tree and Random Forest are covered. Unsupervised Learning algorithms like K-means, DBSCAN and Hierarchical clustering are also covered. Tableau is used to show students how to visualize data.

These courses do require basic programming skills, except for the Data Visualization course. The Data Visualization course has no prerequisite skills requirements.

Syllabus

Courses under this program:
Course 1: Data Preprocessing for Data Science

Learn how to prepare and transform data for analysis and machine learning. The course includes techniques for data cleaning, normalization, domain reduction, and the application of various dimensionality reduction methods such as PCA and t-SNE to enhance data usability and visualization.

Course 2: Supervised Learning

Learn how to build supervised learning models using Python and Sklearn (Sci-Learn). This course includes the most popular supervised learning models, including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Regression, Random Forest and Decision Trees. With Sklearn and Python all of these models can be quickly created using just a few lines of code.

Course 3: Unsupervised Learning

Unsupervised learning is a category of machine learning algorithms that use unlabeled data and no prior training. Many algorithms fall into this category. This class will cover the more popular ones of K-Means, DBSCAN and Hierarchical Clustering. The course ends with a capstone project.

Course 4: Data Visualization

Explore the art and science of Data Visualization with our comprehensive course, designed to transform complex data into impactful visual stories. This course is ideal for anyone looking to enhance their data presentation skills using Tableau, focusing on practical applications like pandemic trend tracking, climate change mapping, and supply chain optimization. Engage with real-world examples, hands-on projects, and interactive content to master data visualization and make informed decisions in any field.

Courses

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4 weeks, 5-7 hours a week, 5-7 hours a week
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The Data Preprocessing for Data Science course is a comprehensive introduction to the essential steps in preparing data for analysis and machine learning. This course covers key techniques and tools used to clean, transform, and reduce data, ensuring it is in the best possible shape for creating accurate and reliable models. This course will provide you with practical experience using Python and popular libraries like NumPy and scikit-learn.
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5 weeks, 5-7 hours a week, 5-7 hours a week
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Unsupervised learning is a category of machine learning algorithms that use unlabeled data and no prior training. Many algorithms fall into this category. This class will cover the more popular ones of K-Means, DBSCAN and Hierarchical Clustering. These algorithms can cluster data into groups. Some algorithms allow the user to decide how many groups that form, while other algorithms decide for the user. The course has weekly coding labs and ends with a capstone project. This allows students to demonstrate their understanding of the algorithms.