Data Science Foundations II

Data Science Foundations II

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Continue your journey by diving into more complex machine learning models, natural language processing, and time series analysis. Gain the expertise to tackle real-world challenges and position yourself as a leader in data science.

$4,700.00 USD

$5,200.00 USD
30-Day Money-Back Guarantee

Financing and flexible payment options available. Learn more

Next Cohort Date

July 7, 2025
December 2, 2024

Program Features

Qualification

Industry Certificate

Duration

12 weeks

Commitment

20 hours weekly

Skill Level

Intermediate

Delivery

Weekly instruction, feedback and support

Start Date

First Monday of each month

Led By

Experienced industry instructors

Features

Flexible schedules and small class sizes (5 max)

Value

Affordable high quality education

Master the tools of the trade

In this advanced set of courses, you’ll immerse 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 SMU x 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.

For these advanced courses, you’ll need:

  • Intermediate Python programming skills, especially in handling larger datasets and complex data manipulations.
  • Familiarity with regression analysis and basic machine learning models.
  • A strong understanding of SQL and experience with dynamic data visualizations.

Curriculum

Industry-approved curriculum to support your journey into data science

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

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 

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

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

Flexible small group classes are the best way to learn from top industry instructors in a fun, collaborative environment, while still getting plenty of personalized feedback.

Upfront

$4,700

Pay as You Go

$5,200

3 monthly payments of $1,734

Course Mentors

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Data Science Foundations II FAQs

Learn more

Will I earn a certificate or some other credential when I complete the Foundations program?

Yes! Upon the completion of each program in the pathway, you will receive a Credly digital badge from SMU and a certificate from Flatiron School. Digital badges can be used in email signatures or digital resumes, and certificates can be displayed on portfolio websites and social media sites such as LinkedIn, Facebook, and Twitter.

Thousands of our community members use their program certificates and badges to demonstrate skills to potential employers — including our hiring partners — along with their LinkedIn networks. Our curriculum is powered by Flatiron School, whose programs are well-regarded by many top employers, who contribute to our curriculum, hire our community, and partner with us to train their own teams.

What are the prerequisites for the date science foundations program?

The data science essentials program.

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