Data Science Foundations II

Master the tools of the trade

Imerse yourself in the most challenging aspects of data science. Learn to develop sophisticated machine learning models. including neural networks, dive into natural language processing, and gain expertise in time series analysis. These courses will equip you with the skills to solve complex problems and develop data-driven solutions that meet the needs of today’s businesses. By mastering these tools and techniques, you’ll position yourself as a leader in the field of data science, capable of tackling the most pressing challenges with confidence and precision.

The Richmond // Flatiron School difference:

• Be mentored by a world-class data scientist
• Small group classes (max 5 students)
• 100% online programs

Prerequisites: Data Science Essentials, Data Science Foundations I

Curriculum

Industry-approved curriculum to support your journey into data science

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Introduction to Machine Learning - 3 weeks

This course introduces the fundamentals of AI and machine learning, covering core concepts like statistical learning theory and supervised learning. You'll explore models such as logistic regression, decision trees, and support vector machines, and learn to evaluate them using metrics like ROC AUCs. The course concludes with a project where you'll select and deploy the ideal model for a specific task, demonstrating your mastery of the data science pipeline.

What you'll learn: 

• Utilize foundational machine learning modeling like decision trees and supervised learning
• Prepare data for machine learning modeling with preprocessing (feature extraction) and normalization
• Utilize mathematics, statistics, & probability for data science methodologies to derive insights

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Machine Learning with Scikit-Learn - 3 weeks

This course covers both supervised and unsupervised machine learning models. You'll learn about distance metrics and k-Nearest Neighbors for classification, recommender systems using SVD, clustering techniques like k-means, and dimensionality reduction with PCA. The course concludes with a project where you'll build and demonstrate both a supervised (k-Nearest Neighbors) and an unsupervised (k-means) learning model, showcasing your skills in classification and clustering tasks.

What you'll learn: 

• Utilize foundational machine learning modeling like decision trees and supervised learning
• Prepare data for machine learning modeling with preprocessing (feature extraction) and normalization
• Integrate mathematics, statistics, & probability for data science methodologies to derive insights 

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Natural Language Processing, Time Series & Neural Networks - 3 weeks

This course teaches skills to build advanced models, focusing on natural language processing (NLP) with techniques like text classification and vectorization, time series analysis for managing and visualizing trends, and neural networks using Keras. The course culminates in a project where you'll build and showcase three models: a language model, a time series model, and a basic neural network.

What you'll learn: 

• Develop insights from language, time, and image data using neural networks and Natural Language Processing (NLP)
• Integrate mathematics, statistics, & probability for data science methodologies to derive insights

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Neural Networks & Similar Models - 3 weeks

This course builds on neural network fundamentals, teaching optimization techniques like normalization and regularization. You'll explore Convolutional Neural Networks (CNNs) for image classification, Recurrent Neural Networks (RNNs) for forecasting and sequence data, and advanced models like transformers and BERT. The course concludes with a project where you'll demonstrate your expertise by building an advanced neural network application.

What you'll learn: 

• Create an advanced neural network application
• Integrate mathematics, statistics, & probability for data science methodologies to derive insights

Tuition

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Upfront - Save 9%

$4,700

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Pay as You Go

$5,200

3 monthly payments of $1,733