Data-driven Product Management with R

Set up a reproducible data analysis environment in R and R Studio to collaborate across teams. Learn to use scripts, projects, and packages to share analyses and ensure reproducibility.

Learn to open and run a script in the studio, view data and charts, and automate a data analysis workflow from installation to outputs.

Learn to manage R packages as collections of functions and data, install, load, and update them, and invoke functions to create charts using the plot package.

Create and share a reproducible data analysis environment with R Studio projects, leveraging a working directory, backups, and portable project settings.

Drive collaborative decision making by building a master dataset that combines data from engineering and sales into a single source of truth, enabling data-driven insights with stack bar chart visualization.

Imports data from spreadsheets into R, creating data frames from three csv files of Hustle funds sales, usage, and detailed usage; use read_csv, pipes, and notebooks.

Combine interdepartmental data to create a master data set in R using inner, full, left, and right joins with customer ID as the primary key.

Master data-driven visualization with bar charts, exploring frequency and value distributions across geos, then use stacked and grouped bars to reveal upsell opportunities.

Automate data curation with tidyverse to clean and standardize CSV and Excel data from tools like Salesforce and Splunk, producing reliable weekly reports and preparing data for analysis.

Standardize variable names to improve readability by renaming by name or by position, apply global naming conventions with the Janitor package, and automate data curation tasks.

Learn to reformat variables and fix encoding in R by using mutate to convert characters to categorical factors and dates, and round decimals for readability.

Extract columns using the select function to subset variables, control their order, and apply criteria such as name, position, exclusion, contains, regex, and numeric types for sharing or resource optimization.

Slice and dice data by filtering rows, extracting and reordering columns, and exporting subsets to CSV to target EU customers with over 100 active users.

Develop your instincts about customers by studying data methodically, building a geo-specific, tabular customer profile that links attributes to cause and effect for data storytelling.

Explore exploratory data analysis to study customer attributes and lay a foundation for future analysis. Focus on enumeration, description, and correlation to build a deep instinct about your customers.

Enumerate all variables in your dataset to ensure intuitive names and complete data, and use the names function to list 18 variables in the hustle funds table.

Describe all variable properties by listing their statistical attributes, central tendencies, and outliers to prepare for correlation analysis. Use the sdr and describe functions to inspect the hustle funds table.

Explore how to study correlations between variables using a pair's plot, interpret positive and negative relationships, and read correlation coefficients to uncover patterns in numeric data.

Apply counts and group summaries to understand aggregate customer attributes, counting customers by geo and by industry vertical, and summarize total and mean active users per vertical.

Learn to create pivot tables that display cross tabulations of two variables, using pivot wider and pivot longer with names_from and values_from to craft data-driven customer profiles.

Automate the creation of business-ready tables in R using the grammar of tables package, with titles, subtitles, groupings, and summary rows for clear, actionable insights.

Learn to build customer profiles and segments or cohorts, use leading indicators like time to value to predict churn, and visualize risk with a faceted scatterplot.

Derive leading business indicators from existing data to predict outcomes, such as time to value and renewal prediction. Segment customers by these indicators for actionable insights beyond traditional attributes.

Visualize customer segments with scatterplots to study time to value across license size, colored by product, shaped by geo, and sized by active customers, plus faceted plots.

Group customers into time-based cohorts and analyze their feature usage over time to reveal retention and adoption trends. Use cohort charts to guide feature decisions and roadmap priorities.

Generate a cohort table from a usage dataset by creating cohort and behavior datasets, grouping by customer and month, and visualizing with a heat map to study retention.

Create cohort charts from cohort tables to observe how engagement changes across cohorts over time, using milestone dates and user interactions to guide future roadmap decisions.

Explore the statistical basis of outlier analysis and learn to identify and visualize outliers using the AQR method, box plots, and histograms.

Identify outliers with the IQR method in R, implement the formula in code, and examine group-level price data by product and geography to reveal meaningful outlier patterns.

Learn to visualize outliers with histograms, using single-variable histograms to detect anomalies and multivariable and faceted histograms to add geographic and price context.

Visualize outliers with box plots that show the median, first and third quartiles, and range for a single variable, and use multivariable, faceted variants to compare products and geographies.

Learn to annotate box plots with labeled outliers using paste and text functions in R, creating static and faceted visuals that show customer name, geography, product, and price.

Learn to apply the grammar of graphics to build layered, self-explanatory data visualizations that tell concise data stories with theme, plot, and tone.

Learn to turn hand-built data into visuals by creating vectors and data frames, then generate a bar chart of April to May sales from a water-cooler conversation.

Learn to prepare a study dataset from the master dataset to support data visualization, define the theme, select variables, and summarize revenue by geo and product.

Learn ggplot's grammar of graphics and layered design, combining geometric objects, aesthetics, mappings, and statistics with base and annotation layers to tailor plots for different audiences.

Build base layer of a two-layer plot from story dataset as a grouped bar chart with product on x axis, total active use on y axis, and geography as fill.

Learn to build a two-layer plot in R by creating a layered dataset and adding an annotation layer with readable, aligned revenue labels in millions.

Label stacked bar charts with the position_stack function and place labels in the middle by setting 0.5. Build a two-layer chart in one code chunk; layering improves readability and collaboration.

Explore non-traditional aesthetics in data visualizations with R to enrich scatterplots by using size, shape, and color to reveal customer segments, while avoiding clutter.

Create visual themes that align charts with corporate standards and apply them consistently across bar plots and scatterplots to adjust backgrounds, grid lines, axes, and title formatting.

Learn to use R notebooks to automate report generation, embed natural language with code-generated charts, and export HTML or PDF for timely, reproducible, data-driven product insights.

Learn to build a markdown report with headings, bullet lists, a table of contents, and formatted text—italic, bold, code snippets, superscript, subscript, and hyperlinks.

Learn to set up R notebooks with a YAML header, embedded code chunks, and markdown text to create reports, export to HDMI, PDF, Word, and PowerPoint, and control chunk options.