Introduction to Data Literacy

Explore how businesses use information technology to deliver new services and improve operations, and why analytical skills matter for non-technical managers.

Explore quantitative and qualitative data, defined by numbers and words, and classify them as continuous, discrete, or categorical, then describe rankings, intervals, and ratios.

Explore quantitative and qualitative data, illustrating measurements in context with scales, rates, and units, and how qualitative descriptions map to categories or sentiment analysis.

Define the terms data and data sets, and explore various ways to describe data sets to improve understanding.

Master data aggregation with mean, median, mode, sum, min, max, count, and range; perform descriptive analysis in a spreadsheet, and cover sentiment analysis and named entity recognition.

Explore how data distributions shape insights, from uniform distribution and normal distribution (bell curve) to power law distribution seen in income and social networks.

Examine imbalance or asymmetry in data distributions, including skewed toward or away from the median, and identify bimodal and multimodal patterns with examples in salaries, ages, and pricing.

Apply data analysis techniques to understand datasets and inform decision making, building on course foundations in data literacy.

Compare options using data to inform decisions through hypothesis testing. Explore AB testing, two-sample t tests, population and sample concepts, and significance at 0.05.

Explore forecasting with data by predicting future values using spreadsheets and scatterplots. Learn to apply the forecast function to historical house price data and interpret results.

Apply data analysis techniques and methods to craft compelling stories in a business context, turning insights into clear narratives that support decision making.

Define a business problem by quantifying how modest throughput improvements on existing assembly lines can delay adding a third line for nine to twelve months, using data to persuade executives.

Define measurements by identifying key variables—assembly lines and assemblers per line—and compare process a to process b via an A/B test using time to assemble as the metric.

Conduct a comparative experiment to evaluate two assembly processes, collect per-device assembly times for 100 devices on each line, acknowledge unequal sample sizes, and analyze and present the results.

Compare two assembly processes using descriptive statistics, mean, standard deviation, and histograms to assess distribution. A two-sided t test shows a significant 0.6-minute difference, prompting adoption of the faster process.

Extend your data literacy by using spreadsheets, databases, and visualization tools—such as Excel, Google Sheets, MySQL, PostgreSQL, and Tableau—and study descriptive statistics, hypothesis testing, forecasting, and data storytelling.