Practical Data Literacy for Leaders

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Module 1 – The Basics

Lecture 1: What is Data Literacy?

Lecture 2: The Practical Project for this Course

Lecture 3: Download the Course Resources

Chapter 3: Module 2 – How to effectively read data

Lecture 1: Step 1 – Understand your goal and determine business questions

Lecture 2: Step 2 – Get the right data

Lecture 3: Step 3 – Understand the data and its sources

Lecture 4: Step 4 – Clean the data/ understand the data quality

Lecture 5: The Data Quality Dimensions

Lecture 6: Data Accuracy

Lecture 7: Data Validity

Lecture 8: Data Timeliness

Lecture 9: Data Completeness

Lecture 10: Data Uniqueness

Lecture 11: Data Consistency

Lecture 12: EXERCISE: Practice on Data Quality

Lecture 13: SOLUTION: Practice on Data Quality

Lecture 14: Chart games – how people will try to manipulate you! Part 1

Lecture 15: Chart games – how people will try to manipulate you! Part 2

Lecture 16: Chart games – how people will try to manipulate you! Part 3

Lecture 17: Chart games – how people will try to manipulate you! Part 4

Lecture 18: EXERCISE: Avoiding the chart game

Lecture 19: SOLUTION: Avoiding the chart game

Chapter 4: Module 3 – How to effectively work with data

Lecture 1: Step 1 – Summarize the data

Lecture 2: Data Centrality – Mean number

Lecture 3: Data Centrality – Median number

Lecture 4: Data Centrality – Mode number

Lecture 5: Data Dispersion

Lecture 6: Data Dispersion – Range

Lecture 7: Data Dispersion – Standard Deviation

Lecture 8: Data Dispersion – Inter-Quartile range

Lecture 9: Data Replication

Lecture 10: Data Shape – Histograms

Lecture 11: EXERCISE: Summarize the data

Lecture 12: Step 2 – Drill-Down on the data

Lecture 13: EXERCISE: Drill-down on the data

Chapter 5: Module 4 – How to effectively analyze data

Lecture 1: Step 1 – Avoid Bias

Lecture 2: Confirmation Bias

Lecture 3: Outliers Bias

Lecture 4: Selection Bias

Lecture 5: Rush-to-Solve Bias

Lecture 6: Availability Bias

Lecture 7: Anchor Bias

Lecture 8: Step 2 – Find the 80/20's in your data

Lecture 9: Step 3 – Derive questions from the data

Lecture 10: Step 4 – Bring in the business SMEs (Subject Matter Experts)

Lecture 11: Step 5 – Choose the correct KPIs/data points

Lecture 12: Step 6 – Make sound data-driven decisions

Lecture 13: EXAMPLE – Data Driven Company

Chapter 6: Module 5 – How to effectively present data

Lecture 1: Presenting data effectively intro

Lecture 2: Step 1 – Know your audience

Lecture 3: Step 2 – What is your goal?

Lecture 4: Step 3 – Choose the critical KPIs to present

Lecture 5: Step 4 – Choose the right format

Lecture 6: Step 5 – Choose the right charts/visualizations

Lecture 7: Step 6 – Focus on the key data points

Lecture 8: Step 7 – It is all about storytelling

Lecture 9: EXERCISE: Presentation preparation

Chapter 7: Module 6 – Important Data and Analytics terminology

Lecture 1: Module Intro

Lecture 2: Big Data

Lecture 3: Business Intelligence (BI)

Lecture 4: Data Architecture

Lecture 5: Data Dictionary

Lecture 6: Data Catalog

Lecture 7: Data Engineering

Lecture 8: Data Management

Lecture 9: Data Governance

Lecture 10: Dataset

Lecture 11: Data Quality

Lecture 12: Data Science

Lecture 13: Data Warehouse

Lecture 14: Data Mart

Lecture 15: Data Lake

Lecture 16: Data Visualization

Lecture 17: ETL

Lecture 18: Metadata

Chapter 8: What Next

Lecture 1: Thank You

Lecture 2: Bonus Lecture