Data Analytics Foundations

Overview

In this course, you’ll learn to harness the volume & complexity of information to help businesses make better decisions. This is data analytics, and it powers insights across almost every industry, even ones you might not think of:

from fashion and government, to tech, sports and healthcare.

This course is the first in a series designed to prepare you for an entry level data analyst role. You don’t need any prior experience with analytics software, programming, or even data to succeed in this course.

Whether you’re looking to start a career in data analytics or level up in your current role, this course is for you. It’s designed to take you from no prior experience to leading your own end to end projects.

And, if you’re already working as a data analyst or in a similar role, you’ll find new strategies and insights to continue growing in your career. Starting out, you’ll learn what data is & the many forms it can take.

Then, you’ll get hands on with spreadsheets, a powerful tool for analyzing and visualizing data. You’ll explore real-world datasets throughout the video demos and the interactive labs, including hotel bookings, baby names, and home sales.

Finally, you’ll learn a structured approach for data analytics projects that works across industries. Plus, throughout this course, you’ll get hands-on with large language models, which are changing the nature of work.

They are not a replacement for your perspective, but they can augment your skills, serving as a thought partner for your practice. In this course, you’ll use LLMs to interpret data visualizations, run analyses, and more.

Data analytics is both analytical and creative. While you will crunch numbers, and that’s fun in its own right, you’ll also craft compelling stories to inspire action.

You’ll discover new things every day, work with people from all backgrounds, and see the real world impacts of your expertise.

Syllabus

• Data and the data analyst role
This module introduces key concepts in data analytics, focusing on data types, formats, roles, and the ecosystem surrounding data. It explores various data representations, differentiates between types of data, and introduces common data file formats. Additionally, it delves into the responsibilities of different data roles and career opportunities in the field. The module also covers the strengths and weaknesses of large language models (LLMs) and their applications in data analytics.
• Using spreadsheets for data analytics
This module provides a practical guide to using spreadsheets effectively, both in everyday life and in a business context. It covers the foundational skills needed to operate spreadsheets, particularly Google Sheets. You will explore various use cases, learn how to address different business problems with specific data types, and gain hands-on experience with essential spreadsheet tasks. It also includes a comparison of file types and their connection to structured and unstructured data, as well as techniques for organizing and analyzing data within spreadsheets.
• Data visualization in spreadsheets
This module delves into the art of data visualization, a crucial skill for data analysts to effectively communicate insights and drive decision-making. You will learn the role of visualizations in data storytelling, explore various graph and chart types, and develop the ability to create and interpret visual data representations. The module emphasizes best practices for selecting the most appropriate visualization for different analyses. You will also look at using LLMs to help interpret and create data visualizations.
• The data analytics lifecycle
This module provides a detailed exploration of the data analysis lifecycle, emphasizing the systematic approach required to turn raw data into actionable insights. You will learn about each stage of the lifecycle—from defining the problem to evaluating the effectiveness of decisions—and how to gather business context and stakeholder requirements to refine business questions. The module also covers the process of determining the appropriate type of analysis, the impact of domain knowledge, and tools like the Rumsfeld Matrix to ensure comprehensive analysis and decision-making.

Taught by

Sean Barnes

Subjects

Data Science