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.
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.
To make our program even more affordable, you have two payment options!
Learn data science fundamentals to get you started, like descriptive and inferential statistics and probability. Learn the basics of navigating spreadsheet programs like MS Excel and perform z-tests, t-tests, Chi-Squares, and data visualizations.
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.
Perform data wrangling and complete data recoding in Python and R. With clean data, create insightful visual representations like infographics using Tableau, R, and Python.
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.
Learn about the Hadoop ecosystem while using Hive, Pig, and Spark for big data analytics. For additional cloud computing knowledge, utilize Amazon Web Services.
Learn the foundation of building a database including query databases and join tables in MySQL. In addition, learn to query documents in NoSQL using MongoDB.
Get the key data science programming language: Python. Explore algorithms, data types, Boolean logic, data structures, best practices, debugging, and object-oriented programming. Databases master extracting and organizing data using SQL and NoSQL databases.
Pick real-world data of interest and questions to answer with that data. Practice Agile teamwork in wrangling, mining, and analyzing data for a completed project published on Kaggle and GitHub.