Learn Data Science online to tackle even the most daunting of data sets and transform them into the fuel for industry innovation. You’ll utilize computer science and applied mathematics theories and techniques, such as the modeling and decision trees employed in Big Data and Machine Learning.
UP TO $2,000 IN SCHOLARSHIPS AVAILABLE!
TOTAL COST: $9,995*
*Scholarship opportunities and financial aid options are also available.
Learn to effectively query existing databases for insights as well as creating your own databases.
Statistical Programming in R
Statistics are a breeze with R. Learn how to wrangle and visualize data in R, as well as analyze data using advanced statistics.
Learn the basics of Python programming. Its easy-to-learn syntax will help you create effective data structures using packages like pandas and numpy.
Point-and-click functionality easily wrangles data and creates beautiful visuals.
Effectively manage large datasets using cloud computing and understand the complications with the 4 Vs of big data.
Use Python to perform many machine learning algorithms, like k-nearest neighbors, random forests, and natural language processing.
SEU Tech’s Data Science Bootcamp will prepare you for a career in the ever-growing and in-demand field of data science in as little as 8 months!
Here’s what you’ll do:
Exclusive for Data Science Program students, you can count on career service assistance including resume support, interview training, and help with identifying possible career options.
Think like a programmer and start programming with the statistical software package R. Become familiar with R practices: complete t-tests, simple linear regression, and correlations while learning data types and data structures for loops. You will use the data wrangling library dplyr and data visualization library ggplot2.
Grasp the principles of creating and monitoring metrics (KPIs) and business best practices for applying them; theory and application of statistical process control; survey development, measurement reliability and validity utilizing agile development and Waterfall project management.
Build upon statistics fundamentals in Python, to learn advanced techniques in Python such as t-tests, Chi-Squares, and correlations. In R, you will perform Analyses of Variance (ANOVAs), Multivariate Analyses of Variance (MANOVAs), and covariate work. Deeply understand statistical power.
Breakdown the theory and applications of machine learning, focusing on clustering, random forests, decision trees, and more in Python. Instruction in other useful modeling practices in R and Python, such as linear regression, non-linear regression, and logistic regression is also provided. Plus, learn Natural Language Processing in Python.