Data Analytics MasterClass(PostgreSQL, BI, Python)

Learn to build dashboards in click view using visualizations such as boxes, donut charts, block charts, and bar charts, transforming human resources and sales data into actionable insights.

Explore employee and forecast datasets, categorize ages and salaries into buckets, analyze joining by quarter and year, and preview dashboard design and future hiring projections.

Load and extract the dataset by setting the delimiter to comma, preview employee and forecast tables, concatenate them into a single employee table, and refine the data model for visualisations.

Explore forecast logic for employee data by applying focus to include incoming staff and exclude resignations, derive forecast numbers, and update the forecast table for dashboards.

Create a polished dashboard title by adding a text object, adjusting font size and style, and displaying today's date with a custom date format.

Create two KPI boxes showing total and on-hand employees, counting unique employee IDs from the actual dataset while excluding forecast data, and customize captions, colors, and font size.

Demonstrates how to update forecast records for resignations and new hires, adjust focus data, and refresh the dashboard to maintain an accurate headcount.

Calculate net pay KPI by summing employee salaries, filtering by join status and excluding resignations to forecast cash flow and allocate funds for upcoming months.

Learn to compare forecast vs actual percentages of employee net pay, apply percentage formatting and color cues, and build expressions for focus and actual in a dashboard.

Create a donut chart from a pie chart and a block chart to visualize employees by gender and by region, with clear labels and interactive region selections.

Create a bar chart of employees by quarter and region to visualize hiring patterns and regional variations, revealing that quarter three shows higher hiring and suggesting visa issuance effects.

Analyze employees by age group using a bar chart with 10-year intervals. Rename the expression to age group and compare salary by group to inform dashboard presentation.

Conclude by previewing how to analyze data and derive insights from a CEO's dashboard, and how to reflect richer data into dashboards using PostgreSQL, BI, and Python.

Learn how data analytics informs restaurant investment by examining revenue drivers and consumer behavior, then apply business intelligence to analyze local data and identify target audiences with a dashboard.

Explore a data walkthrough of a local community survey, analyzing calories per day, family size, price range, dining reasons, and main and secondary cuisines to build meal-focused dashboards.

Create a food survey dashboard by loading data, designing a centered line separator, and formatting text and colors to clearly split the layout for meals and females.

Create KPI boxes to visualize gender-based participation and breakfast patterns in a BI dashboard, using filters to count male and female participants and compute breakfast and coffee percentages.

Analyze main course and drinks preferences with bar charts and gender-based insights, including mojito, pizza, and salmon, to inform promotions that boost restaurant revenue.

Explore a scatterplot of calories versus price to reveal gendered purchasing power, showing higher calorie groups and healthier, higher-priced options, with females as the key audience.

Explore how data analytics targets female dining motives on a specific day using bar charts, then translate insights into appealing restaurant names and advertisements.

Create front-end visualizations by adding a logo and a dynamic sales distribution title to a dashboard, adjusting background and font size, and implementing a year expression.

Build KPI boxes to compute current year revenue from unit price times quantity, compare with last year, format values, and apply conditional colors for changes.

Learn how to create and customize a block chart to analyze sales by region, highlight current year revenue versus last year, and identify top territories.

Create a combo chart to compare sales by our business partner against target numbers, using revenue and target expressions, and filter to top 10 employees.

Create a 100 percent stacked bar chart of sales composition by product category, comparing current year, last year, and targets, with year and category selections to slice the dashboard.