Information Security Basics is designed to teach entry and mid-level IT staff the technological fundamentals of information security. The goal of this course is to provide trainees some preliminary knowledge of computer security to help in identifying and stopping various cyber threats. In addition to providing an introduction to information assurance, trainees will also learn general concepts (terminologies), an overview of TCP/IP, introductory network security, introductory operating system security, and basic cryptography.

For more information, visit teex.org/class/awr173

The Machine Learning course is designed to help individuals create systems that learn from large sets of data. It covers a wide range of topics, such as natural language processing, predictive algorithms, and statistical pattern recognition. This course provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more). Also, it shows how to build machine-learning models in Python using popular libraries like NumPy, Pandas, and Scikit-learn for prediction and classification tasks. This specialization allows individuals to master the key concepts and practical skills required to apply machine learning to real-world problems.

For more information, visit coursera.org/learn/machine-learning

The Data Scientist in Python path shows how to master the essential technical skills required to become a data scientist. It includes object-oriented and functional programming with Python and the use of its popular libraries such as Scikit-learn, Matplotlib, NumPy, and Pandas. The program covers Python programming for complex statistical analysis of large datasets, utilizing SQL queries and web-scraping to extract data from databases and websites, creating insightful data visualizations, and automating machine learning algorithms for predictive modeling processes. In addition, it expands on topics like the UNIX command line, Git, and GitHub for effective collaboration. Moreover, the course offers a hands-on learning experience to write code and get feedback directly in the browser. Besides, it shows many ways to apply newly acquired skills to several guided projects involving realistic business scenarios to learn and develop a data science portfolio.

For more information, visit dataquest.io/path/data-scientist

The Data Analyst in Python path goes beyond the fundamentals of Python and shows how to extract and prepare data by querying databases with SQL for insightful data visualization to perform descriptive and predictive statistical analysis. In addition, the course expands on a wide range of topics and offers a hands-on learning experience to write code and get feedback directly in the browser. Besides, it shows many ways to apply newly acquired skills to several guided projects involving realistic business scenarios and perform complex data analysis of large datasets.

For more information, visit dataquest.io/path/data-analyst