Start your career as a data scientist in Atlanta
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.
We’ve been building communities of learners since 2012. Build your network as well as your knowledge with a diverse, supportive group of peers committed to growth and change.
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.
Everyone deserves the opportunity to succeed, which is why we provide a variety of payment options — so you can focus on your education and your career, not your tuition.
Our grads launch new careers
Global Employment Rate
For job-seeking on-campus graduates included in the 2020 Jobs Report including full-time salaried roles, full-time contract, internship, apprenticeship, and freelance roles, and part-time roles during the reporting period (see our Jobs Report).
U.S. Campus Average Starting Salary
For job-seeking on-campus US graduates who accepted full-time salaried jobs during the reporting period and disclosed their compensation. The average starting salary for US on-campus graduates who accepted full-time contract, internship, apprenticeship, or freelance roles and disclosed compensation was $33/hr. Average pay for a part-time role was $24/hr (see our Jobs Report).
Where our graduates work
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.
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!
Variables, Booleans and Conditionals, Lists, Dictionaries, Looping, Functions, Data Structures, Data Cleaning, Pandas, NumPy, Matplotlib/Seaborn for Data Visualization, Git/Github, SQL, Accessing Data Through APIs, Web Scraping
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.
Combinatorics, 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.
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, Scikit-Learn
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.
Dimensionality Reduction, Clustering, Time Series Analysis, Neural Networks, Big Data, Natural Language Processing, Text Vectorization, Natural Language Toolkit, Regular Expressions, Word2Vec, Text Classification, Recommendation Systems
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.
Join the fastest-growing corner of the tech industry
More than ever before, companies are relying on data to make business decisions. Without data science, these industry trends stay undiscovered — no story to tell and no insights to share. In order to determine business goals, more and more companies are looking for data scientists to fill in the gaps. Data science is one of the fastest-growing and sectors of the tech industry.
Growth in Data Science Jobs Since 2012
The course will qualify you for a position as a data scientist or a data analyst. If you have a professional background in programming, you may also be able to get a position as a data engineer or a machine learning engineer.
Meet your teachers
Since day one over five years ago, we’ve taken teaching seriously. Great teachers inspire us to connect to topics on a profound level. With experience both in the field and in the classroom, our data science instructors are dedicated and thorough. Simply put: you learn from the best.
Lore earned her PhD in Business Economics and Statistics at KU Leuven, Belgium and has a thorough background building out R and Python data science curriculum.
After earning a Masters in Statistics from New York University, Fangfang worked as a data scientist in the public policy and start-up sectors. However, her love of teaching led her to join Flatiron School as a lead instructor.
Navigate tech's top opportunities with the help of our Career Services team
At Flatiron School, you won’t just learn data science. You’ll also learn "How to be a No-Brainer Tech Hire" and effective job seeker. With 1-on-1 career coaching, a robust employer network, and a proven job search framework, our Career Services team is committed to helping you launch a career in tech.
During your job search, you’ll meet weekly with your dedicated Career Coach. Coaches help with everything from résumé review to interview prep, and help you tell your story to land your first job.
We’ve built relationships with hiring managers at top companies across the world, creating a robust employer pipeline for Flatiron School grads. Our Employer Partnerships team is constantly advocating for our grads and helping you get in the door.
Through 1-on-1 guidance from our Career Coaching team and our tried-and-true job-search framework, you’ll gain the skills and support you need to launch your career.
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