Data Science

Learn data science & change things

Quality learning comes at the intersection of diligence and curriculum. With that vision in mind, Flatiron School has brought together passionate, experienced instructors and ambitious students to achieve incredible outcomes since 2012. And now we’re bringing that vision to data science with our immersive Data Science program.

What You'll Learn: Data Science & Machine Learning

Our career-ready Data Science curriculum provides the technical skills, expertise, and tools necessary to think and work as a data scientist. Working in our WeWork classroom with our seasoned instructors, you’ll master a mix of software engineering and statistical understanding, then apply both skills in new and challenging domains.

Our robust Data Science program ensures not only job readiness for today’s growing job market, but the aptitude to continue learning and stay relevant in your career for years to come.

Prework

Our data Science Program moves quickly and our passionate students embrace that challenge. While no necessary to apply, we require you to demonstrate some data science knowledge prior to getting admitted, then complete a prework course before Day 1. To help you prepare for our bootcamp, we provide a free introductory course. The prework ensures that you come in prepared and able to keep pace with the class.

Python for data science

Our first module introduces the fundamentals of Python for data science. You’ll learn basic Python programming, how to use Jupyter Notebooks, and will be familiarized with popular Python libraries that are used in data science, such as Pandas and NumPy. Additionally, you’ll learn how to use Git and Github as a collaborative version control tool. At the end of this module, you’ll be able to build a basic linear regression model and evaluate the results. Finally, we’ll conclude with a heavy focus on visualizations as a way to convert data to actionable insights.

Module 1 Topics

• Variables
• Booleans and Conditionals
• Lists
• Dictionaries
• Looping
• Functions
• Data Cleaning
• Pandas
• NumPy
• Matlotlib/Seaborn for Data Visualization
• Git/Github

Data Engineering for Data Science

In this module, you’ll learn about data structures, relational databases, ways to retrieve data, and the fundamentals of SQL for data querying for structured databases, as well as NoSQL (and MongoDB) for non-relational databases. Furthermore, we’ll cover the basics of HTML, XML, and JSON in order to access data from various sources using APls, as well as Web Scraping.

Module 2 Topics

• Data structures
• Relational Databases
• SQL
• Object-Oriented Programming
• NoSQL databases
• MongoDB
• JSON
• HTML/XML
• Accessing Data Through APIs
• CSS Web Scraping

Probability, Sampling AB Testing

This is a basic module that introduces the fundamentals of probability theory, where you’ll learn about principles like combinations and permutations. You’ll continue with statistical distributions and learn how to create samples with known distributions. By the end of this course, you’ll apply your knowledge by running Monte Carlo simulations and AB tests. 

Module 3 Topics

• Combinatorics
• Probability Theory
• Statistical Distributions
• Bayes Theorem
• Naive Bayes Classifier
• Sampling Methods
• Monte Carlo Simulation
• Hypothesis Testing
• AB Testing

Statistical Modeling

We’ll cover how and when regression models can be used to transform data into insights. You’ll learn about both linear and logistic regression and the algorithm behind regression models. By the end of this module, you’ll be able to evaluate the results of regression models and extend them to interaction effects and polynomial features. To compare the performance of models built, you’ll dive deeper into model evaluation and the bias-variance trade-off.

Module 4 Topics
Linear Algebra
Linear Regression and extensions
Polynomials
Interaction effects
Logistic regression
Optimization Cost Function
Gradient Descent
Maximum Likelihood Estimation
Time Series Modeling
Regularization and Model Validation

Machine Learning and Big Data

In Module 5 you’ll learn how to build and implement machine learning’s most important techniques and will take your first steps into classification algorithms through supervised learning techniques such as Support Vector Machines and Decision Trees. Additionally, you’ll learn how to build even more robust classifiers using ensemble methods like Bagged and Boosted Trees, as well asRandom Forests. Next, you’ll move onto unsupervised learning techniques such as Clustering, and dimensionality reduction techniques like Principal Component Analysis.

Module 5 Topics

• Distance Metrics
• K Nearest Neighbors
• Clustering
• Decision Trees
• Ensemble Methods
• Dimensionality Reduction
• Pipeline Building
• Hyperparameter Tuning
• Grid Search
• Scikit-Learn

Deep Learning and Natural Language Processing

In the final module, you’ll learn how to use regular expressions in Python and how to manage string values, analyze text, and perform sentiment analysis. Additionally, you’ll get an in-depth overview of deep learning techniques, densely connected neural networks for high-performing classification performance, convolutional neural networks for image recognition, and recurrent neural networks for sequence modeling. You’ll also learn about techniques to evaluate performance and to optimize and regularize model performance.

Module 6 Topics

• Neural Networks
• Convolutional Neural Networks
• Ngrams
• POS Tagging
• Text Vectorization
• Context-Free Grammars
• Neural Language Toolkit
• Regular Expressions
• Word2Vec
• Text Classification

Final Projects

In our final project, you’ll work individually to create a large-scale data science and machine learning project. This final project provides an in-depth opportunity for you to demonstrate your learning accomplishments and get a feel for what working on a large-scale data science project is really like. You and your fellow students will each pitch three different ideas and then decide on your final project with your instructors. Instructors advise on projects based on difficulty and feasibility given the time constraints of the course. At the end of the course, you’ll receive a grade based on various factors. Upon project completion, you’ll understand how to construct a project that gathers and builds statistical or machine learning models to deliver insights and communicate findings through data visualisation and storytelling techniques.