Receive a tuition discount if you submit your application to join our Winter 2021 bootcamps between now through Friday, December 11, 2020.
Our Data Science Bootcamp is ranked as “Best Data Science Bootcamp” for 2019, 2020, and 2021 by Course Report.
The only bootcamp that teaches both Python and R with machine learning, data analytics, and data visualization
Curriculum drawn and updated through the engagement with corporate trainees, hiring partners, and leading industry experts
Four real-world and company-sponsored projects including a capstone to develop your programming and professional skills
Lifelong career support including one-on-one resume reviews, interview prep, and exclusive access to networking opportunities with our hiring partners
An active community of 2000+ working data scientists, like-minded peers, and data experts across various fields
In this program, students will learn beginner and intermediate levels of Data Science with an option to specialize in Big Data Technologies or Deep Learning. Students will also learn to build statistical models, linear regression, data classification and visualization using Python, R, Hadoop, Spark, and AWS.
Learn to work from the command line - a must have skill for all data scientists. Work with basic Linux commands, text editing, and Git for version control. MySQL is taught with extensive practice on data manipulation.
Dive deep into R programming language from basic syntax to advanced packages and data visualization (e.g. tidyr, dplyr, string manipulation, ggplot2, R Shiny). Create a data-centric application with interactive visualizations.
Basic Python programming, followed by versatile packages such as Numpy, Scipy, Matplotlib, Pandas, and Beautifulsoup. Exposure to NoSQL and MongoDB. Complete a Python web scraping project.
Descriptive statistics, hypothesis testing, missingness, imputation & KNN, simple linear regression, multiple linear regression, generalized linear models, PCA, ridge/lasso, trees, random forests, bagging, boosting, support vector machines, neural networks, time series analysis, unsupervised learning. Complete a Kaggle competition.
Deepen machine learning skills with scikit learn. Focus on data cleaning, feature extraction, natural language processing, modeling and model selection using regression, SVM, PCA, tree models, clustering and more.
Learn the concepts of high performance computing with parallel computing skills in Python and R. Introduction to MapReduce, Hadoop, Hive, Spark, and Spark MLlib.
Complete a capstone project. Resume review, tips of interview skills, and opportunities to interview with potential employers. Receive customized resume support, LinkedIn profile review, elevator pitch workshops and career guidance, and more.