Data Visualization Classes São Paulo

This BI course teaches students the basics of SQL, Python, and other data visualization tools. Students learn how to incorporate Tableau and Power BI for reporting and other key foundational skills to be able to provide data-driven reports.

In this comprehensive Excel bootcamp, participants learn beginner to advanced Excel techniques, including formulas & functions, VLOOKUP, macros, and more. Students learn how to work more efficiently, summarize, and analyze data with tables, charts, and PivotTables. By the end of this hands-on course, participants will be ready to use Excel for everyday business tasks. The course has flexible scheduling options and is available live online or in NYC. Tuition includes hands-on training, a free retake, course manual, and supporting video tutorials.

In this comprehensive 2-day course, participants can become an advanced Tableau user able to work with data to create visualizations and use the various tools Tableau Public has to offer. They will learn to identify datasets to create desired visualizations and then master the Tableau tools on a deeper level to customize their visualizations. By the end of this course, participants will have learned to create maps to represent geographical data, and build actions to control visualizations within sheets and dashboards. Tuition includes hands-on training, a free retake, and a course manual. The course offers flexible scheduling and is available online or in NYC.

In this data science bootcamp, students will build programming skills and data analysis skills using Python. This course is open to beginners and is meant to get individuals up and running with Python programming and data science to generate insights from data. Topics covered include programming fundamentals, working with data frames, data analysis, data visualization, and statistical analysis. This course offers flexible scheduling options and a free retake for students to refresh the materials.

In this course, students expand their Python programming skills into machine learning and algorithms that can independently learn patterns and make decisions. The course begins with linear and logistic regressions, the most time-tested and reliable tools for approaching a machine learning problem. Students then progress to algorithms with a different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. Participants also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.