Data Analytics Classes Knoxville, TN

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.

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 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.

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.

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.

This course teaches the fundamentals of SQL querying. This 1-day program teaches students how to execute a simple query, perform a conditional search, work with functions, and organize data. Further, students will learn how to retrieve data from multiple sources and export query results via a text or XML file.

This 1-day course builds on the fundamentals of SQL querying. This intermediate program teaches advanced querying techniques. The coursework will cover the use of subqueries to perform advanced querying, manipulating table data and table structure. Students will learn how to work with views, index data and manage transactions.

This 3-day introductory course teaches the basics of SQL databases. Through the use of labs and lectures, students will learn the fundamentals of data modeling, the use of relational databases, and the normalization of raw data. Students will also learn how to design relationships, evaluate query performance, and use stored procedures.