At Flatiron School, we teach today’s in-demand tech skills through our dynamic, immersive courses taught by experienced, passionate industry professionals online and on WeWork campuses around the world. But we don’t stop there — we pair an industry-leading curriculum with dedicated Career Services professionals who are committed to helping you find a job you love.
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Income Share Agreements (ISAs) are a form of deferred tuition, allowing students to focus on learning — and not on financing. With an ISA, following initial deposit, you pay nothing toward your tuition until after you’ve left the program and are earning at least the minimum income — regardless of job type or industry.
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What you'll learn: data science & machine learning
From Python to Machine Learning, our 15-week data science training program gives you the breadth and depth needed to become a well-rounded data scientist. You’ll also leave with an understanding of how to discover new techniques as your career progresses.
Every 3 weeks you’ll be introduced to a new module that builds off the learnings of the previous section while allowing you enough time to dive into each area for a thorough understanding of the subject matter.
The Data Science program moves quickly and our passionate students embrace that challenge. While no experience is 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. This prework ensures you come in prepared and are able to keep pace with the class.
Introduction to Data with Python and SQL
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. To organize your data, you’ll learn about data structures, relational databases, ways to retrieve data, and the fundamentals of SQL for data querying for structured databases. Furthermore, you’ll learn how to access data from various sources using APls, as well as perform Web Scraping.
Finally, we’ll conclude with a heavy focus on visualizations as a way to go from data to insights.
At the end of this module, students will use their newly learned skills to collect, organize and visualize data, with the goal to provide actionable insights!
Module 1 Topics
- Booleans and Conditionals
- Data Structures
- Data Cleaning
- Matplotlib/Seaborn for Data Visualization
- Accessing Data Through APIs
- Web Scraping
Statistics, A/B Testing, and Linear Regression
Having learned how to gather and explore data with Python and SQL you can now go deeper into analyzing that information with statistics. In this module, you’ll learn about the fundamentals of probability theory, where you will learn about probability principles such as combinations and permutations. You will go on and learn about statistical distributions and how to create samples when distributions are known. By the end of this module, you will be able to apply this knowledge by running A/B tests. Additionally, you’ll learn how to build your first (and important) data science model: a linear regression model.
Module 2 Topics
- Probability Theory
- Statistical Distributions
- Bayes Theorem
- Sampling Methods
- Hypothesis Testing
- A/B Testing
- Linear Regression
- Model Evaluation
Module 3 is all about machine learning, with a heavy focus on supervised learning. To start, you will go a little deeper into regression analysis, learning about extensions to linear regression, and a new form of regression: logistic regression. In building regression models, students will learn about penalization terms, preventing overfitting through regularization and using cross validation to validate regression model.
Next, you’ll learn how to build and implement the most important machine learning techniques. You’ll learn about classification algorithms such as Support Vector Machines and Decision Trees. Additionally, you’ll learn how to build even more robust classifiers using ensemble methods such as Bagged and Boosted Trees, and Random Forests.
Module 3 Topics
- Linear Algebra
- Logistic Regression
- Maximum Likelihood Estimation
- Optimization Cost Function
- Gradient Descent
- K Nearest Neighbors
- Decision Trees
- Ensemble methods
- Pipeline Building
- Hyperparameter Tuning
- Grid Search
Big Data, Deep Learning, and Natural Language Processing
After a full module on supervised learning, this module focuses on a variety of advanced Data Science techniques. You will start with learning about unsupervised learning techniques such as clustering techniques and dimensionality reduction techniques. Next, you will be introduced to threading and multiprocessing to be able to work with big data. In doing so, you’ll learn about PySpark and AWS, and how to use those tools to build a recommendation system. Next, you will get an in-depth overview of deep learning techniques, learning about densely connected neural networks, enabling high-performing classification performance. Next, students will learn how to use regular expressions in Python, and how to manage string values, analyze text and perform sentiment analysis.
Module 4 Topics
- Dimensionality Reduction
- Time Series Analysis
- Neural Networks
- Big Data
- Natural Language Processing
- Text Vectorization
- Natural Language Toolkit
- Regular Expressions
- Text Classification
- Recommendation Systems
Data Science Advanced Project
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 course’s time constraints. At the end of the project, you’ll receive a grade based on various factors.
Upon project completion, you’ll know 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.
Module 5 Topics