Data Analytics

Designed With a Focus on Real-World Relevance

Concentrate on the most important tools for performing data analysis on the job. GA’s analytics advisory board curates the best practices and innovative teaching approaches of our entire expert network to meet evolving employer demands. Its work ensures that students graduate ready to tackle the challenges they’ll face in the field.

Translate Data Into Meaningful Insights

Designed for students with a variety of professional backgrounds and experience levels — including beginners — this course takes a deep dive into the data analytics process: collection, cleaning, analysis, and visualization. Get hands-on experience with Excel, SQL, and Tableau, and transform raw data into a critical decision-making tool.

Get a Head Start With Pre-Course Prep Lessons

Once you enroll, we equip you with online, self-paced preparatory lessons to set you up for success. Dive into the fundamentals of working with data in Excel. You’ll gain a common vocabulary to start collaborating with classmates on day one.

This is a part time course.

This course will teach you how to use large datasets to make critical decisions.

Unit 1: Exploring Data with Excel

Understanding the Value of Data

• Explore the value of data.
• Work your way through the data analytics workflow.
• Unlock the power of Microsoft Excel to utilize its analytical tools — no matter your experience level.

Preparing Data in Excel

• Learn best practices for collecting and cleaning data in Excel to ensure accurate analysis results.
• Manipulate real-world data sets using advanced nested logical functions (IF, OR, and AND).

Cleaning Data in Excel

• Clean large, unruly data sets by removing duplicate rows and performing text manipulations.
• Transform and rearrange columns and rows within spreadsheets to prepare data for analysis.
• Gain insight into data analysis by manipulating data formats.

Referencing Data in Excel

• Use the VLOOKUP and HLOOKUP functions to manipulate data sets.
• Look up values in other tables using the functions INDEX and MATCH.

Aggregating Data in Excel

• Summarize data using pivot tables.
• Execute Excel aggregation commands (SUM, AVERAGE, COUNT, etc.) and their conditional variants (COUNTIF, COUNTBLANKS, etc.) to summarize data sets.

Visualizing Data in Excel

• Derive insights from data by highlighting cells based on conditionals.
• Use scatter plots, bar graphs, and histograms to visualize data.
• Explore color theory and how it applies to data visualization.
• Build your own data dashboard using industry best practices.
• Apply visual design principles to your dashboard to present your findings clearly.

Applying Statistics for Data Analysis

• Understand the value of descriptive and inferential statistics.
• Learn to describe data and identify outliers using its mean, median, mode, range, and variance.
• Make predictions about larger populations using sample data.

Building Data Narratives

• Learn best practices for describing analysis techniques.
• Communicate insights and implications for stakeholders.
• Deliver short, effective presentations sharing your findings.
• Provide context for your audience by identifying and presenting the salient statistics.

Unit 2: Managing Data with SQL

Understanding the Fundamentals of Databases and SQL

• Practice the fundamentals of Structured Query Language (SQL).
• Use database schema to design appropriate queries using SQL.
• Explore the differences between relational databases (tabular data storage) and document-based databases (key-value pairs).

Querying Large Databases in SQL

• Collect data using standard SQL commands (Create, Update, Truncate, etc.).
• Filter data using advanced SQL commands (Where, Group By, Having, Order By, Limit).
• Obtain data by creating relationships between tables using Joins.
• Gather filtered data using SQL Boolean operators (AND and OR) and SQL conditional operators (=,!=,>,<,IN, and BETWEEN).

Aggregating Data in SQL

• Create relationships between tables and data points (including has_many and many_to_many) using Join tables.
• Write Boolean statements with SQL conditional operators (=,!=,>,<,IN, and BETWEEN) and null functions (“IS NULL,” “IS NOT NULL,” and “NVL”).
• Clean data using SQL mathematical functions (ABS, SIGN, MOD, etc.).
• Summarize data sets by employing aggregation commands (“Sum,” “Average,” “Count,” etc.).
• Determine data trends by applying aggregation methods.

Writing Efficient, Dynamic Queries

• Use CASE statements to structure data and create new attributes.
• Combine multiple subqueries into one using “WITH AS.”

Creating a Local Database

• Create a local database using data sets you bring into the classroom.
• Upload and export data using a local database.

Unit 3: Communicating Data Analysis with Tableau

Exploring the Fundamentals of Tableau

• Get hands-on, practical experience navigating the Tableau software interface.
• Connect your data to Tableau and identify the best ways to visualize and display your results.

Visualizing Data and Mapping in Tableau

• Use bubble graphs to visualize data.
• Display geocoded information for your data.

Designing Data Dashboards in Tableau

• Contextualize analysis results by creating Tableau dashboards.
• Incorporate charts and conditional formatting into your dashboard using supporting information specific to a particular data set.

Working with Data Across Excel, SQL, and Tableau

• Understand the use of specific data analysis software.
• Apply all the tools and skills you’ve acquired in Excel, SQL, and Tableau to analyze a data set from start to finish.

Final Project Presentations

• Identify strengths and areas for improvement in your analytical skills with feedback from peers, instructors, and guest panelists.