Data Classes Columbus, OH

In this comprehensive data science program, individuals will learn a variety of critical skills to become a data scientist. Students will learn to code with Python, create machine learning models, automate tasks like web scraping, and interact with databases using SQL. Topics include data analysis with Python's Numpy and Pandas packages, data visualization, predictive analytics, machine learning, SQL querying, Python automation, and web scraping. Students will learn hands-on by working in Python and SQL and will complete the program with the skills they need to enter the field of data science. This program offers flexible scheduling and provides a free retake for students to refresh the material.

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.

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.

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.

This course on data wrangling with Python offers a 3-day comprehensive introduction. The purpose of this course is to give an immersive foundation that equips students with the most critical data analysis tools. Students will learn how to work with NumPy, Pandas, and MatPlotLib to calculate descriptive statistics, work with alternative data sources, and scrape web pages. The course culminates with a real-life data wrangling project.

This 2-day course provides a deep-dive into using Python for data analysis. Students will get comfortable with the basics of Python programming and start working with Numpy Arrays, pandas, and MATPLOTLIB to perform data analysis and create data visualizations. This intensive course is meant to quickly get beginners in Python up to speed on performing data analysis and visualization in Python.

Develop a solid design foundation in this two-day beginner-level Tableau training class. Through a combination of hands-on learning, instructor guidance, and class discussions, students import data from multiple sources and use it to create strong visualizations and engaging stories. Specific topics include Tableau interfaces, understanding techniques and best practices, leveraging the Tableau paradigm, workflow management, data analysis, data breakdowns, basic calculations, and guided data presentation.

Take your Tableau skills to the next level in this three-day intermediate course. Using more in-depth Tableau features, such as enhanced data visualization, participants solve more advanced problems. Specific topics include a Tableau review, parameters, sets, groups, reference lines, animation features, forecasting, mapping, data sourcing, filed calculations, and advanced charting.

Combine basic and intermediate courses in this fast-paced, five-day Tableau bootcamp. Once they acquire a solid foundation, students move quickly to more advanced areas, through a combination of structured and guided learning. Key concepts include combining visualizations, using interactive dashboards, utilizing data stores, gaining data insights, effective presentation techniques, and sophisticated data charting approaches.