Data
Analytics

10-Week Part-Time Or 1-Week Accelerated Course

Talk to Admissions +44 2033089506
Communicate with stakeholders

The Big Picture

Confidently make — and defend — critical decisions using the results of your data analysis.

Collect, clean, and analyze data

Skills, Tools, & Strategies

Use Excel, SQL, and Tableau to spot trends and drive business decisions with real‐world data.

Create data visualizations

Analytics in Practice

Share your insights and tell compelling stories using data visualizations and dashboards.

Meet your support team

Our educational excellence is a community effort. When you learn at GA, you can always rely on an in-house team of experts to provide guidance and support, whenever you need it.

  • instructor

    Instructors


    Learn industry-grade frameworks, tools, vocabulary, and best practices from a teacher whose daily work involves using them expertly.

  • teaching assistant

    Teaching Assistants


    Taking on new material isn’t always easy. Through office hours and other channels, our TAs are here to provide you with answers, tips, and more.

  • producer

    Course Producers


    Our alumni love their Course Producers, who kept them motivated throughout the course. You can reach out to yours for support anytime.

See What You’ll Learn

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.
  • Practice applying the data analytics workflow to a sample business case.

Preparing Data in Excel

  • Discuss data cleaning best practices.
  • Review strategies to prepare and clean a data set.
  • Practice asking the “right” questions of our data.

Referencing Data in Excel

  • Use named ranges to easily reference data subsets.
  • 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.
  • Explore data using conditional formatting for categorization and analysis.

Visualizing Data in Excel

  • 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.
  • Practice applying statistical functions in Excel.

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

Foundations of Databases and SQL

  • Learn about data structures and the role of structured query language (SQL).
  • 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 and multiple Joins.
  • Write Boolean statements with SQL conditional operators (=, !=, >, <, IN, and BETWEEN).
  • Summarize data sets by employing aggregation commands (“Sum,” “Average,” “Count,” etc.).
  • Determine data trends by applying aggregation methods.

Using Logic and Functions in SQL

  • Explain the differences between NULL and zero.
  • Use SQL NULL to create Boolean functions and handle zeros.
  • Apply string functions to manipulate how data is presented.
  • Apply math functions to add value to the data you are working with.
  • Apply date logic to your SQL queries.

Writing Efficient, Dynamic Queries and Subqueries

  • Use CASE statements to structure data and create new attributes.
  • Combine multiple subqueries into one using “WITH AS.”
  • Optimize queries using WHERE, LIMIT, and COALESCE.

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 Text Manipulation in Tableau

  • Use bubble graphs and a variety of visualizations to identify and communicate data insights.
  • Create a number of calculated fields to clean and manipulate strings of text using LEFT, MID, FIND, and REPLACE.

Text and Sentiment Analysis in Tableau

  • Use the Twitter web data connector to bring tweet data into Tableau.
  • Analyze and visualize your data pull in Tableau in order to answer basic questions.

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.

Request a Detailed Syllabus

Get Syllabus

The goal of analysis is to find information in the data that’s going to help people make a better decision. This course is a powerful and accessible way for students to learn how to go from data to decisions.

Jim Byers / Business Intelligence Manager, HTC

Jim Byers, Business Intelligence Manager, HTC

Meet your instructors

Learn from skilled instructors with professional experience in the field.

Dave Bredesen

New York City

Dave Bredesen

Senior Product Manager,

Amazon

Carey Anne Nadeau

Washington, D.C.

Carey Anne Nadeau

Founder and CEO,

Open Data Nation

Irene Rix

Melbourne

Irene Rix

Analytics Consultant, Founder KnowThyData.io,

Jim Byers

Seattle

Jim Byers

Technical Program Manager - Big Data and Data Science Projects,

Disney

Roger Woodley

San Francisco

Roger Woodley

Senior Data Solutions Manager,

Cornerstone OnDemand

Learn In

Set as default location
Accelerated 1-Week

Jan 28 – Feb 2

Mon - Sat


9am - 5pm


£2,800 GBP

Mar 12 – May 16

Tue & Thu


6pm - 9pm


£2,800 GBP

Apr 30 – Jul 4

Tue & Thu


6pm - 9pm


£2,800 GBP

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Get Answers

Have questions? We’ve got the answers. Get the details on how you can grow in this course.

  • Why is this course relevant today?

    Data is now an integral part of every business. To be successful in today’s business landscape, all companies need to learn how to leverage data to make critical business decisions. It is a requirement for every employee to know how to analyze data. In this course, you will learn how to use large amounts of data to help your company make those critical decisions about strategy.

  • What practical skill sets can I expect to have upon completion of the course?

    This course will teach you how to use large amounts of data to make business decisions. Using Excel and Sql, you will learn how to collect, clean and analyze data from multiple sources including the web, a local file and a relational database. Additionally, you will be able to use this analysis to make business decisions. In this course, you will practice with real world data sets and problems to contextualize how analytics fit into the business world.

  • How does this course differ from the Data Science course?

    Unlike Data Science, students can come to Analytics with little-to-no programming skills and learn how to apply analytic skills to solve real world business problems. In Data Science, students need to have experience with a programming language prior to taking the course.

  • Are there any prerequisites for the course?

    No, there are no prerequisites for the course.

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