From beginner to data pro

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Embark on an immersive journey that takes you from data science novice to expert in just ten months. This comprehensive bootcamp covers everything from the foundational principles of data analysis to the advanced tools and techniques used by top data scientists. You’ll learn Python, SQL, and data visualization before diving into more complex areas like machine learning, big data processing, and AI. With a strong emphasis on hands-on learning, you’ll apply your skills to real-world problems, culminating in a capstone project that demonstrates your readiness for the professional world. By the end of this bootcamp, you’ll have a portfolio that showcases your expertise and positions you for success in one of the most dynamic and rapidly evolving industries.

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The Charlotte x Flatiron School difference:

Be mentored by a world-class data scientist

Small group classes (max 5 students)

100% online programs

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Career services built-in

Career services is included with each capstone and bootcamp program. Designed by and for tech professionals, you'll experience a full technical interview process from start to finish, getting direct and personalized feedback each step of the way. Visit the Job Search Accelerator page to learn more about the full process.

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Prerequisites: No prior experience necessary. This bootcamp takes you from novice to pro.

Curriculum

Industry-approved curriculum to support your journey into data science

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Introduction to Python - 3 weeks

This introduction to Python course covers essential programming concepts for data science. You'll learn scripting basics, algorithms, and data structures like tuples and dictionaries, and explore Python libraries and functions. The course ends with a project where you'll develop a Python script to analyze data, preparing you to tackle real-world data science challenges.

What you'll learn:

• Apply the basics of programming language methodologies to real world scenarios
• Demonstrate foundational skills for scripting with a programming language, Python

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Introduction to Data Science - 3 weeks

In this course, you'll dive into data analysis and visualization using Python. You'll learn statistical measures, explore data types, and master techniques with the pandas library. You'll also use Seaborn and Matplotlib for visualization, handling qualitative, quantitative, and multivariate data. The course concludes with a project where you conduct a full exploratory data analysis, applying descriptive analysis and visualization skills, and object-oriented programming principles. By the end, you'll be skilled in data insights, advanced analysis, and effective visualization.

What you'll learn:

• Implement foundational statistical measurement with data using scripting
• Demonstrate gathering insights from data with visualizations
• Integrate object oriented programming (OOP) with Python for data cleaning and analysis

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Introduction to SQL - 3 weeks

This course covers SQL, data engineering, database administration, and data analysis. You'll learn math, probability, and statistics for data science and advance your Python skills for data cleaning and analysis. Starting with SQL basics, you'll progress to advanced topics like filtering, ordering, and joining data. The course concludes with a project where you apply SQL queries in a Python environment, demonstrating your understanding of database concepts and preparing you for real-world data science challenges.

What you'll learn:

• Utilize industry standard techniques to analyze data with, programming language (Python), structured query language (SQL), and the cloud
• Explore and manipulate data with mathematics, probability, and statistics
• Analyze data for a business problem with visualizations with a dashboard

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Cloud Computing, Generative AI, & Dashboards - 3 weeks

This course focuses on cloud computing for cost-effective, scalable data processing. You'll master technical components like PySpark to integrate Python, SQL, and Spark for handling structured and semi-structured data. Using libraries such as Numpy, Pandas, and PySpark, you'll work with big data and create stunning visualizations with Python libraries like Seaborn. The course also explores advanced data analysis using generative AI and interactive dashboards, culminating in a project that brings big data to life through visualizations.

What you'll learn: 

• Create a dashboard using data science methodologies with industry standard tool(s)
• Model exploratory data analysis with tools for multiple data sets with SQL and SQL table relations
• Utilize programming techniques to process large data samples with large-scale processing like PySpark with big data

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Inferential Statistics- 3 weeks

This course teaches statistical inference with Python, covering probability distributions, confidence intervals, and hypothesis testing. You'll apply these techniques to analyze proportions, means, categorical data, and multivariate datasets. The course concludes with a final project where you'll showcase your ability to analyze a multivariate dataset using various statistical inference methods.

What you'll learn: 

• Integrate statistical inference of data using the technical programming
• Implement methodologies for statistical inference
• Utilize mathematics, statistics, and probability for data science methodologies to derive insights

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Regression - 3 weeks

This course teaches regression techniques for analyzing real-world datasets. You'll master linear and multiple linear regression, learning diagnostics, model evaluation, and advanced techniques like transformations, interactions, and regularization methods such as Lasso and Ridge. The course concludes with a project where you'll build and interpret a multiple linear regression model.

What you'll learn: 

• Perform logistic regression with data sets using programming techniques, lasso, and ridge
• Compare statistical results for different types of regression with data sets, linear, transformations of linear, and multiple linear regressions
• Utilize mathematics, statistics, and probability for data science methodologies to derive insights

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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, and probability for data science methodologies to derive insights

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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, and probability for data science methodologies to derive insights 

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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, and probability for data science methodologies to derive insights

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‍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, and probability for data science methodologies to derive insights

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Data Science Capstone - 15 weeks

In this intensive course you will be tasked with developing 3 different projects. In these projects you will be expected to frame your projects around solving a business problem. You will be expected to bring all your skills from foundations together to build 3 different methods: a regression model, a classification supervised mode, and a classified unsupervised model.

What you'll do:

• Integrate data science process using at least one method of regression
• Integrate data science process using at least one method of non-regression supervised learning
• Integrate data science process at least one method of non-regression unsupervised learning

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Tuition:

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Upfront - Save 42%

$9,500*

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Pay as You Go - Save 36%

$10,500

12 monthly payments of $875

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Financed Tuition - Save 18%

$13,500

Monthly payments as low as $298**

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*To celebrate our ongoing partnership with GitHub, we're reducing the cost of our part-time and full-time bootcamps from $16.5K to $9.5K for a limited time, starting with our March 3, 2025 cohorts. Apply by February 24th, 2025 to be eligible for the March 3rd, 2025 cohort. Only the first 50 students to enroll for the March 3rd cohort will receive the promotion price. The total price is dependent on the financing option chosen. This offer excludes the Game Design and Game Programming Bootcamps as well as Essentials and Modular Bootcamp course pathway. Not available for Georgia residents.

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