Data Literacy for Business Professionals

Name: Data Literacy for Business Professionals
Rating: 4.5 (4039 reviews)

Master the Fundamentals for Collecting, Exploring, and Presenting Data & Lead Data-Driven Projects from Start to Finish

Created byGrant Hall

Last updated 1/2022

English

German [Auto],English [Auto],

What you'll learn

Utilize and leverage data to improve business outcomes
Find appropriate data sources and prepare them for use in an analysis
Explore data visually to gather insights that can't be gathered with numbers alone
Effectively describe a dataset containing millions of rows with just a few numbers
Create and tell a story with data
Inspire action through data-driven results

Course content

6 sections • 28 lectures • 2h 32m total length

What is Data Literacy?2:53
Define data literacy, why it matters in a data-driven world, and describe three mastery levels from data illiterate to data expert, focused on collecting, interpreting, and communicating data.
Course Overview and Instructor Background3:29
Master data literacy by collecting and preparing data, then exploring, visually and numerically describing it, transforming datasets, and storytelling to drive action, using Excel and PowerPoint.

Considerations in Collecting Data5:55
Learn how to collect and prepare data responsibly under laws like GDPR and CCPA. Emphasize ethical practices, informed consent, anonymity, and confidentiality to gain public goodwill and valid data.
Sources of Data7:08
Assessing Data Quality and Generalizability4:44
Assess data quality and generalizability before analysis by examining data sources, measurement methods, outliers, and missing data to avoid garbage in, garbage out and ensure reliable results.
Simplifying and Adapting Data6:32
Learn practical ways to simplify and adapt data for analysis. Convert text to numbers, use averages and multiple questions, and apply appropriate metrics such as moving averages and ROI.

Using Visualizations to Explore Data4:23
Discover how data visualizations reveal patterns, clusters, and outliers, show the shape of datasets, and improve communication beyond raw numerical summaries.
Using Excel to Create Visualizations0:49
Line Charts6:59
Bar Charts6:34
Master bar charts for visualizing qualitative and quantitative data, analyze distributions and comparisons, and use descending order, horizontal and stacked variants to reveal insights by region, gender, and product.
Pie Charts4:11
Discover best practices for pie charts, compare donut and bar chart alternatives, and learn when sorting by size improves readability and highlighting a segment clarifies proportions.
Box Plots10:18
Explore box plots to interpret data using the five-number summary, box and whisker diagram, and interquartile range, and identify outliers and distribution shape.
Dot Plots6:28
Histograms6:49

Introduction to Describing Data Numerically3:55
Using Excel in This Section0:40
Welcome to the Describing Data Numerically section of the course!
Throughout this section I will be working with several datasets that I have provided to all of you in Excel. I highly recommended that you download the "Describing Data Numerically - Blank" Excel file and follow along as you watch the videos. I specifically recommend pausing the video and trying to arrive at the same numbers I do by writing your own formulas within Excel.
To help you get started with some of the Excel formulas I have also attached a "Describing Data Numerically Excel Cheat Sheet" file that walks through a basic example of all the formulas that will be used in this section. I recommend you leverage that cheat sheet to help you as you work through this section of the course.
As always, if you want to check your work there is the "Describing Data Numerically - Answers" Excel file that is available for download.
Now, let's move onto the videos!
Methods to Describe Center7:20
Practice with Central Tendency Measures6:39
Explore how mean, median, and mode—the measures of central tendency—are used and why outliers and skew can mislead the mean, with house prices to sharpen news interpretation.
Methods to Describe Variability7:05
Practice with Variability Measures9:39
Analyze customer data using variability measures to define a target market. Identify age and distribution through quartiles and regions to target young professional females in the east.
Re-scaling Data with Z-Scores6:30
Explore rescaling data with z-scores to interpret how many standard deviations a value is from the mean, compare distributions, and assess probabilities.
Z-Score Practice and Interpretations10:10

The Difference of Effective Communication4:20
Learn how to present data-driven marketing insights clearly and actionably, using an ROI case study to compare radio, print, and TV, and emphasize a strong call to action.
Telling the Story Section Excel File0:32
Highlight Key Information in Visualizations5:46
discover how visualizations clarify data by highlighting key information, captioning takeaways, organizing values from largest to smallest, and graying out distractions to improve interpretation.
Provide Context with Numerical Descriptions4:45
Use context with numerical descriptors to convert averages and standard deviations into actionable inventory decisions, illustrated by a pizza shop case study.

Requirements

No prior data analysis experience is necessary. The foundation will be built in this course.
A background in Excel would be beneficial but not required.

Description

The data revolution is coming!

Around the globe companies are relying on data to make optimal decisions and bring about better solutions for their customers.

To stay ahead of the curve and not be left behind you need to develop data literacy - the ability to understand data and guide a data driven analysis process. By the end of this course, you will be prepared to lead a data driven project from start to finish. Data literacy will enable you to stand out among peers as you are able to drive robust data-driven decision making to your organization.

With over 20 lectures and two and a half hours of video, you will be exposed to many topics including how to collect and prepare a dataset, ways to explore data visually, how to effectively summarize a dataset with millions of rows into just a few numbers, and how to present all your results effectively at the end of a project. You will also have the opportunity to practice what we learn as you go along in the course! Downloadable Excel files will be available so students can apply the concepts as they learn them and arrive at the same results I do in each lecture.

The course is broken down into the four steps you must take when working on any data analysis project:

Collecting and Preparing Data
Exploring Data Visually
Describing Data Numerically
Telling the Story

I look forward to seeing you in the course and helping you develop data literacy!

Who this course is for:

Business leaders and employees across the world who want to build the fundamental skills that will enable them to make data-driven business decisions

Data Literacy for Business Professionals

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 6min

Collecting and Preparing Data4 lectures • 24min

Exploring Data Visually8 lectures • 47min

Describing Data Numerically8 lectures • 52min

Telling the Story4 lectures • 15min

Conclusion2 lectures • 8min

Requirements

Description

Who this course is for: