Data Science Bootcamp

Introduction to Python

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

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

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

Cloud Computing, Generative AI, & Dashboards

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. (SQL and SQL table relations)
• Utilize programming techniques to process large data samples with (PySpark and Big Data)

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Inferential Statistics

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, & probability for data science methodologies to derive insights

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Regression

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, & probability for data science methodologies to derive insights

Introduction to Machine Learning

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, & probability for data science methodologies to derive insights

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Machine Learning with Scikit-Learn

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, & probability for data science methodologies to derive insights

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Natural Language Processing, Time Series & Neural Networks

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, & probability for data science methodologies to derive insights

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‍Neural Networks & Similar Models

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, & probability for data science methodologies to derive insights

Data Science Capstone

In this intensive capstone you will be tasked with developing 3 different portfolio projects and you will be expected to frame your projects around solving a business problem. You will bring together all of 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

You’ll receive a certificate of completion upon passing each program. Once you complete all programs — Essentials through Capstone — you’ll be ready to jump head-first into the job search with the help of your dedicated Flatiron School career coach.