At Flatiron School Atlanta, we offer more than just a data science program. We bring together passionate instructors, uncompromising education, verified outcomes, and an inclusive student community to fuel an authentic and open tech sphere.
From Python to Machine Learning, our 15-week data science training program gives students the breadth and depth needed to become well-rounded data scientists.
With deferred tuition and our dedicated career services team, students can enroll with security. Enroll today and only pay once you’er hired and earning at least $40,000. We just need a refundable deposit before you start.
With a proven framework for leading a successful job search, a robust employer network, and weekly 1:1 sessions with a dedicated career coach — our career services team is dedicated to helping students secure the career they want upon graduation.
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.
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.
Variables, Booleans and Conditionals, Lists, Dictionaries, Looping, Functions, Data Cleaning, Pandas, NumPy, Matlotlib/Seaborn for Data Visualization, Git/Github
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.
Data structures, Relational Databases, SQL, Object-Oriented Programming, NoSQL databases, MongoDB, JSON, HTML/XML, Accessing Data Through APIs, CSS Web Scraping
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.
Combinatorics, Probability Theory, Statistical Distributions, Bayes Theorem, Naive Bayes Classifier, Sampling Methods, Monte Carlo Simulation, Hypothesis Testing, AB Testing
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.
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
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.
Distance Metrics, K Nearest Neighbors, Clustering, Decision Trees, Ensemble Methods, Dimensionality Reduction, Pipeline Building, Hyperparameter Tuning, Grid Search, Scikit-Learn
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.
Neural Networks, Convolutional Neural Networks, Ngrams, POS Tagging, Text Vectorization, Context-Free Grammars, Neural Language Toolkit, Regular Expressions, Word2Vec, Text Classification
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.
Flatiron School has over five years of working with passionate students and helping them launch fulfilling careers in tech, we’ve developed a keen understanding of what goes into getting that first data scientist job – both on our end and what you’re empowered to do on yours.
During the job-search phase, students meet with a dedicated coach every week, to ensure an effective job search via resume review, mock interviews, and developing the right job-search collateral to tell their story.
At Flatiron School, you won’t just learn to code. You’ll also learn how to be an effective job seeker and no-brainer tech hire. Through one-on-one guidance from our Career Services team and our tried-and-true job search framework, you’ll launch your career in code far beyond the bootcamp.
For over six years, our Employer Partnerships team has been developing relationships with hiring partners across the country to help Flatiron School grads get in the door. Our dedicated Employer Partnerships team is the best in the industry — constantly evangelizing for our graduates at companies across the world.
After graduation, our students have gone on to contribute to some of the most exciting and impactful companies in Atlanta.
Every day is unique at Flatiron School, and our curriculum team and instructors develop new lessons that build off past experiences. But structured, consistent learning is also crucial to student success. Below is what you can expect on a daily basis in a Flatiron School classroom.
Students’ questions start a morning conversation, used to review new skills and program materials from the day before to ensure each student is up to speed.
Students learn key concepts from their expert instructor through interactive exercises and collaborative discussion.
Two students will work together to build statistical analyses and robust coding strategies.
Students will apply lessons learned from lecture to solve real business problems — take messy data, clean it, and gain actionable insights from the numbers.
Students end the day reviewing concepts and strategizing the next steps in their personal projects.
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: students learn from the best.
Learn from full-time, seasoned, passionate instructors who teach students both the hard and soft skills they need to change their lives.
Defer your tuition with the Flatiron School Income Share Agreement (ISA). After a refundable payment when you enroll, the remainder of your tuition is paid once you’ve left the program and are getting paid at least a minimum income.
Our school brings together people who see data science as a craft, and want to be great at it. Our students come from myriad backgrounds and previous career paths — insuring that a diversity of thought, experience, and perspective are not only invited, but actively sought. At Flatiron School, all you need is passion and an open mind.
The technical review assesses your basic grip of coding and how it interacts with us daily. Showing proactiveness by completing steps on Learn, our learning platform, also helps.
Receive your acceptance decision from Admissions. This usually happens within a couple of days, and would be your final step!
All scholarships to our in-person program are granted after a student is admitted. Scholarships are granted on a need and merit basis, with preference given to underrepresented minorities in tech, including women and military veterans. If you feel that describes you, you can share your story with us on the scholarship application once you are admitted into the program.
We recognize that sometimes “life happens” and that students who have been admitted to one class may need to defer and start with us at a later date. Students may defer up to three start dates beyond the class to which they are admitted. If you must defer farther out than that, we may ask you to repeat some or all of the admissions process to ensure your readiness for the later start date. Students may defer only once without reapplying.
Flatiron School’s application process is rigorous, and sometimes students who don’t get accepted the first time around are able to ‘study up’ and get accepted the second time around. As such, students are invited to re-apply after three months have passed from initial decision. Students are permitted a total of three application attempts, so re-applicants are advised to use that time building their skills (both professional and technical) and to submit a second application that is materially different from the first one, showcasing your hard work and improvements over that time.
Admissions are conducted on a rolling basis, so we continue to accept new applications until the course is filled. Therefore, there is no deadline to apply by – though the sooner you get your application in, the better we are able to prioritize it. Because our classes fill up well in advance of the start date, we recommend applying at least 8 weeks before your desired course date. This allows us 2-3 weeks to conduct the application process and accounts for time to complete the mandatory 100 hours of pre-work, which most students report takes at least 3 weeks.
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