Data Science Bootcamp
Data Science Bootcamp
This bootcamp is a deep dive into the fundamentals of data science and machine learning in Python. Throughout the course, you will gain a comprehensive understanding of the entire data science process from end-to-end, including data prep, data analysis and visualization, as well as how to properly apply machine learning algorithms to various situations or tasks. You’ll also walk away with a portfolio of projects showcasing your data science acumen to prospective employers!
WEEK 1-4
Learn the Python fundamentals needed for data science.
Learn how to load, clean, and manipulate data using the Python library Pandas. Additionally, you will learn the strengths and weaknesses of using Python to manipulate data.
Build visualizations to not only understand your data, but also how to communicate results to stakeholders.
Learn how to use Python to implement key statistical techniques and understand statistics better by experimenting with Python on real-world datasets. This week concludes with a project to showcase your knowledge.
WEEK 5-8
What is machine learning and why should you use the Python Scikit-Learn for Machine Learning. Topics include types of machine learning, how to format your data to be acceptable for an algorithm, and how to train an algorithm.
Learn about tree based machine learning algorithms, how to tune them to maximize their performance, and the strengths and weaknesses of each algorithm. Additional topics include feature selection for machine learning, and comparing machine learning algorithms.
Learn about the logistic regression algorithm and get a visual understanding of how the algorithm works. Additional topics include: logistic regression for multiclass classification, L1 and L2 regularization, and hyperparameter tuning the algorithms learned so far.
What is unsupervised learning and what are its applications. You’ll learn about a host of clustering algorithms, how to tune them, and the strengths and weaknesses of each. This week concludes with a machine learning project to showcase your knowledge.
WEEK 9-12
What is unsupervised learning and what are its applications. You’ll learn about a host of clustering algorithms, how to tune them, and the strengths and weaknesses of each. This week concludes with a machine learning project to showcase your knowledge.
Learn what gradient boosting algorithms are, why they are so performant, and how to get started with Kaggle competitions.
Working with databases is an essential part of being a data analyst, data scientist, and data engineer.
Learn about why deep learning has transformed industries, various deep learning frameworks, and when to use deep learning techniques. Topics include recurrent neural networks (RNN) and Convolutional Neural Networks (CNN).
Graduation Requirements
To keep you on track towards graduation, we have a set of graduation requirements to track your progress! Daily attendance is essential to your experience. And before progressing to the more difficult levels of our curriculum, we require students to pass our belt exams: a 24 hour exam where you must design a machine learning algorithm in the technologies you’re currently learning. In addition, students are expected to do:
• 90% completion of non-optional assignments
• 3 satisfactorily passed exams
• 80% attendance of Discussion Lectures
Career Services
Guidance, strategy and prep you need to land the job
Portfolio Building & Application Guidance
– Github Portfolio Production
– Resume Development & Curation
– Sample Applications
– Hiring manager communication
Interview Prep & Negotiations
– Sample Job Interviews
– Technical Job Skills Tests
– Target compensation management
– Contract Negotiation
