Data Analytics & Management

For those with basic database management knowledge, this course introduces important considerations in database application development and various technologies that have made possible database publishing on the Internet.

For those with SQL knowledge, this course covers advanced SQL statements used in inserting, retrieving, and updating a database. Students learn how to use advanced features of SQL commands and other advanced topics.

Learn tools for distributed data storage and large data set processing. This course focuses on architectures and technologies, including NoSQL and Hadoop.

Data governance is an integrated framework of policies and technology applied to ensure effective data management. This course will help students to gain an understanding of various components that allow an enterprise to manage data.

Designed for students with limited or no prior programming, statistics, or data analytics knowledge, this data science overview course is ideal for absolute beginners, business analysts, or non-tech managers interested in the field.

In this course, participants will gain a basic understanding of how data science can best be applied to business situations.

In this class, students will learn how to interpret data as well as how to apply data cleaning techniques using SAS, a powerful statistical programming language.

Learn the iterative process of exploratory data analysis (EDA), data analysis techniques, data exploration, and visualization. Course tools include R for data analysis and Tableau for data visualization.

This course covers AI fundamentals and concepts as pertaining to finance. The course will review python programming basics such as Basic Input-Output Operations, Basic Operators, Conditional Execution, Loops, and Lists.

This course covers the key knowledge domains in data science, including data development and management, machine learning and natural language processing, statistical analysis, data visualization, and inference.

Learn machine learning origins, principles, and practical applications, as well as implementation via the Python programming language. Students will learn to train a model, evaluate its performance, and improve its performance.

Learn machine learning origins, principles, and practical applications, as well as implementation via the R programming language. Students will learn to train a model, evaluate its performance, and improve its performance.

This hands-on course helps you use predictive analytics for improving business performance using techniques such as data mining, statistics, modeling, machine learning, and artificial intelligence.

Python is a high-level, dynamically typed, and portable programming language that excels when the cost of software development outweighs performance considerations.

Get an introduction to core concepts in probability and statistics that will be useful throughout your data science career. This course also introduces students to important Python tools used by data scientists. 

This course introduces Pandas, one of the core Python data analysis packages, and uses it as the basis for performing various types of data analysis tasks. Essential data visualization techniques will also be covered. 

Numerical computing plays an essential role in data processing. Get an introduction to core numerical methods using Python. The Python package for numerical computing, Numpy, is used for implementing these methods.

Learn relational database technology, data modeling, SQL, the translation of logical designs to physical storage structures, database integrity, storage and client/server management, and introduction to query optimization.