As the tech industry expands, the employment market for skilled Data Science professionals is growing rapidly. Start training for the most in-demand jobs in Data Science and Big Data in less than 8 months.
Learn Data Science Online to tackle even the most daunting of data sets and transform them into the fuel for industry innovation utilizing computer science and applied mathematics theories and techniques, such as the modeling and decision trees employed in Big Data and Machine Learning.
Data Science Skills
Data Science skills are in-demand across an array of industries. Training can help you learn them.
But, what skills can you expect to learn from our data science program?
Learn to effectively query existing databases for insights as well as creating your own databases.
Learn the basics of Python programming. Its easy-to-learn syntax will help you create effective data structures using packages like pandas and numpy.
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.
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.
Available Now in Our Partner Schools
Through our network of accredited partner schools, you will receive Woz U curriculum created by industry experts and employers to enhance your career development.
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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