Qlik Sense Business Analyst Masterclass

Define the business objective with the business context and customer, then deliver a Qlik Sense analytics solution using an associative data model and the insights advisor.

Analyze data quality across fact and dimension tables, districts, customers, and payments, and learn to use synthetic keys, circular references, subset ratio, density metrics, and RFM segmentation for robust analytics.

Define fact and dimension tables and compare star and snowflake schemas to optimize joins and performance. Explain grain, dimensions and measures, and cardinality with referential integrity in a data model.

Familiarize yourself with the Qlik Sense interface, including the hub, streams, and QMC. Discover how its associative data model, drag-and-drop dashboards, and smart search drive self-service analytics with real-time filtering.

Load data via the data manager in ClickSense, upload eight CSV datasets from a zip, and create a new sales analysis application to start building a data model.

Apply a filter to load only required columns in the data model by omitting the mobile column from the customer dimension table. Preview results to confirm omission in the application.

Learn to join data in Qlik Sense using inner and outer joins and concatenate to combine store and district dimensions, drop unnecessary columns, and display district names.

Use the replace option in the Qlik Sense data manager to standardize inconsistent district values, converting West.Delhi, West-Delhi, and W.Delhi to West Delhi, and similarly standardizing South Delhi.

Create buckets in the data manager to classify numerical values into low, medium, and high ranges (0–200, 201–500, 501+), avoiding complex frontend ifs in click sense charts.

Learn how to avoid synthetic keys by ensuring unique column names across tables and using proper joins, such as inner or left outer joins, to prevent data model issues.

Identify and prevent circular referencing in a Qlik Sense data model by renaming ambiguous columns to prevent illogical joins between store, customer, and district tables, avoiding loops and warnings.

Inspect each field loaded in the data model viewer to verify tables, columns, and metadata for data quality and keys across fact and dimension tables, noting duplicates and synthetic keys.

Explore density and subset ratio as data quality indicators and business insights: density shows non-null tax info; subset ratio compares fact versus dimension values (71.43% vs 100%).

Capture the voice of the customer through a story brief guiding proposed Qlik Sense views: big picture, time machine, product truth, territory war, customer code, and basket and bill insights.

Explore nine data visualization functions in Qlik Sense, from change over time to magnitude, covering distribution, correlation, flow, ranking, deviation, and spatial maps, with drag-and-drop charts.

Map visualization functions to the proposed views for the emergency sales dashboard, covering executive overview, time and trend analysis, product performance, store geography, customer segments, and Sankey diagrams.

Explore master items for reusable dimensions, measures, and visuals. Set variables to control analysis, save and restore selections with bookmarks, and selection states using color-coded interactions in Qlik Sense.

Declare master items and create a calculated field named date ID num to convert the date column into a number, then load data for the data model viewer.

Create a calendar drill-down master dimension in Qlik Sense by building year, year quarter, and year month hierarchies, and load corresponding values when users drill down.

Declare the variable vtop to control the top-end slider in set analysis, default 10, and apply it in master measures for accurate previews and the final dashboard front end.

Build and customize the executive overview sheet in Qlik Sense by arranging KPIs, adding a date picker, sparklines, tree maps, and a district–store vertical bar chart to reveal sales insights.

Build the time and trend analysis sheet using a diverging bar chart, calendar heat map, combo chart, and line chart to show sales year-on-year deviation by year-month.

Explore the product performance sheet with two horizontal bar charts, a scatter plot, a distribution plot, and KPI shares of total sales and total orders.

Develop the store and geography performance sheet with four charts: district-year and year-store bars, store-name bar with average order value reference line, and a year-month-store line for orders.

Develop a customer and segment analysis sheet with scatter plot, horizontal bar chart, and distribution plot to compare segment wise total orders, total value, and average order value by year.

Explore payment and basket behavior with multi-chart visuals, including donut, bar, distribution, and Sankey charts, revealing cash as the preferred payment mode and regular customers driving revenue.

Learn to build an advanced analysis sheet in Qlik Sense with anomaly spike charts, k-means clustering, and NL insights generated by the click sense associative engine.