As the tech industry expands, the employment market for skilled data science professionals is growing rapidly. Complete your training for the most in-demand jobs in Data Science and Big Data in less than 8 months.
Learning delivery partners use powered by Woz training to deliver online data science training preparing you for industry-preferred certification exams. Tackle even the most daunting of data sets, transforming them into the fuel for industry innovation using computer science and applied mathematics theories and techniques such as the modeling and decision trees employed in Big Data and Machine Learning.
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Data Science for Big (Data) Careers
Calling all problem solvers! Does analyzing data to provide insights and make decisions sound like an exciting way to spend the day? How about many days? Find out if becoming a data analyst is the career for you. Check out our career coach for additional information on roles, salary and market data, and position descriptions.
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.
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.
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.
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