Top-notch Data science courses in LinkedIn Learning

Big Data in the Age of AI

Course description

The hype about big data may have peaked several years ago, but big data is far from gone. Instead it forms the foundation for some of today’s most exciting technologies. Artificial intelligence (AI), machine learning, and data science rely on big data, or data that—by virtue of its velocity, volume, or variety—can’t be easily stored or analyzed with traditional methods. In this nontechnical course, Barton Poulson digs into the topic of big data, explaining how it works and shapes our modern data universe. Barton explains big data’s relationship to AI, data science, social media, and the Internet of Things (IoT). He goes over some of the ethical issues behind the use of big data. Plus, he covers techniques involved in analyzing big data, including data mining and predictive analytics.

Tableau and R for Analytics Projects

Course description

On its own, Tableau is a powerful tool that helps professionals analyze, display, and generally make sense of the data at their fingertips. With the addition of R—a free, open-source language for data science—you can glean even more insights from your data. In this course, learn how to combine the analytical strengths of R with the visualization power of Tableau to analyze and present data more effectively. Instructor Curt Frye demonstrates how to install R and RServe; create a connection between Tableau and R; perform several types of analyses in R, from linear regression to cluster identification; and incorporate those analyses into Tableau visualizations.

NLP with Python for Machine Learning Essential Training

Course description

Applied Machine Learning: Foundations

Course description

Anyone who can write basic Python is capable of fitting a simple machine learning model on a clean dataset. The competitive edge comes in the ability to customize and optimize those models for specific problems. The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem specific. In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem.

Advanced SQL for Data Scientists

Course description

Many data scientists know how to work with SQL—the industry-standard language for data analysis. But as data sizes grow, you need to know how to do more than simply read and write from a database. This course provides a more sophisticated approach to designing data models and optimizing queries in SQL. Instructor Dan Sullivan begins with the logical and physical design of tables—with particular focus on very large databases—and then presents a deep dive review of indexes, including specialized indexes and when to use them. The next section introduces query optimization and shows how to optimize basic, multi-join, and more complex queries. The course also covers SQL extensions, including user-defined functions and specialized data types. The techniques taught here enable more efficient analysis of large data sets using SQL, statistics, and custom business logic.