Data Classes 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 program, participants will learn the key skills and tools needed to succeed as a data analyst, including Excel, SQL, and Tableau. Attendees will learn how to use Excel to create graphs, summarize data with PivotTables, and analyze data. In SQL, attendees will learn the essentials of relational databases, queries, functions, and more. Finally, participants will learn Tableau to create visualizations to express their data. The program is open to beginners and has flexible scheduling options. Tuition includes hands-on training, a free retake, and training manuals for each course.

Earn a Python certification with hands-on training in data analysis, machine learning, and real-world projects to prepare you for the PCAP exam and beyond.

Build a strong foundation in Python, SQL, and machine learning through self-paced, expert-supported training designed for aspiring data scientists. You’ll gain real-world experience through hands-on projects and capstone work that prepares you for entry-level roles in data science and automation.

In this 8-day intensive Data Analytics bootcamp, students with little to no background in data analytics will learn how to use Microsoft Excel, SQL, and Tableau to summarize, display, and discover key insights hidden in data sets. By the end of this bootcamp, students will be able to use Excel to aggregate and summarize data, sort data with PivotTables, create charts and tables in Excel, use SQL to filter and sort large data sets, use advanced database queries, create visualizations with Tableau, and much more. Students will also be able to reinforce what they learn through the numerous projects they encounter throughout the course. This course also offers a free retake within six months to allow students to revisit any concepts needing to be refreshed and walk away with a much stronger grasp of the material.

This 35-hour program offers a comprehensive introduction to R. This course teaches the skills needed to process, manipulate, and analyze data, create visualizations, and generate reports. The program begins with an overview of programming with R. It then moves on to the treatment of basic data elements, the use of “dplyr” to manipulate data, and how to create basic and advanced visualizations such as violin and mosaic plots, and time-series diagrams.

This six-week course teaches students how to use key components of Apache Hadoop, Spark, MapReduce, and more. Using platforms such as Databricks, Amazon Web Services, and Docker, students will learn how to use Python and cloud computing to run exercises. Students will complete five units on topics such as Hadoop, MapReduce, Apache Hive, Apache Pig, Apache Spark, and Amazon Web Services.

Learn to problem solve, and effectively communicate, like an analyst. This course teaches you to use industry-standard tools to make ethical, data-driven decisions. Experience hands-on training to master SQL, Excel, Tableau, PowerBI, and Python – tools listed in virtually every data analytics job posting across industries.

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.