Adarsh Ankur

Data Analyst | Advance Excel | Power BI | SQL | Python | Turning Complex Data into Clear Business Decisions

[Adarsh Ankur @adarshankur19]

Day 14 of the Databricks 14-Day AI Challenge.

Explored AI-powered analytics today. Learned how Databricks Genie turns natural language into SQL and got an intro to Mosaic AI and generative AI use cases.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 13 of the Databricks 14-Day AI Challenge.

Focused on model comparison and feature engineering today. Worked on training multiple models, tuning parameters, and using Spark ML pipelines.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 12 of the Databricks 14-Day AI Challenge.

Started with MLflow today. Learned about experiment tracking, model logging, and how to use the MLflow UI.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 11 of the Databricks 14-Day AI Challenge.

Focused on statistical analysis and ML preparation today. Worked on descriptive stats, hypothesis testing, correlations, and feature engineering.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 10 of the Databricks 14-Day AI Challenge.

Focused on performance optimization today. Learned how to read query execution plans, apply partitioning, use OPTIMIZE and ZORDER, and leverage caching.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 9 of the Databricks 14-Day AI Challenge.

Focused on SQL analytics today. Learned about SQL warehouses, writing analytical queries, and building dashboards with visualizations and filters.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 8 of the Databricks 14-Day AI Challenge.

Focused on Unity Catalog and data governance today. Learned about catalog to schema to table hierarchy, access control, data lineage, and managed vs external tables.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 7 of the Databricks 14-Day AI Challenge.

Learned how Databricks Jobs differ from notebooks, how multi-task workflows work, and how scheduling and parameters are used.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 6 of the Databricks 14-Day AI Challenge.

Learned how Medallion Architecture works. Bronze for raw data, Silver for cleaned data, and Gold for business aggregates.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 5 of the Databricks 14-Day AI Challenge.

Learned advanced Delta Lake concepts like time travel, MERGE operations, OPTIMIZE, ZORDER, and VACUUM.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 4 of the Databricks 14-Day AI Challenge (12/01/26).

Focused on Delta Lake today. Learned about ACID transactions, schema enforcement, and how Delta differs from Parquet.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 3 of the Databricks 14-Day AI Challenge.

Learned how PySpark compares to Pandas, worked with different joins, explored window functions for running totals and rankings, and understood the basics of UDFs.

@databricks @codebasicshub @indiandataclub
#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 2 of the Databricks 14-Day AI Challenge.

Learned about Spark architecture (driver, executors, DAG), the difference between DataFrames and RDDs, and lazy evaluation.

@databricks @codebasicshub @indiandataclub

#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 1 of the Databricks 14-Day AI Challenge.

Learned why Databricks is used over Pandas and Hadoop, the basics of Lakehouse architecture, and how real companies like Netflix and Shell use it.

@databricks @codebasicshub @indiandataclub

#DatabricksWithIDC

[Adarsh Ankur @adarshankur19]

Day 21. Final day of the 21 Days of SQL Challenge.

I built a dashboard with CTEs combining service metrics, staff metrics, and patient demographics, then computed a weighted performance score.

@indiandataclub @dpdzero

#SQL #SQLWithIDC

[Adarsh Ankur @adarshankur19]

Day 20 of the #IDC 21 Days of SQL Challenge 🚀

Built trend analysis with SUM() OVER and AVG() OVER. Showed weekly admissions, running totals, 3-week moving avg satisfaction, and difference from service avg for weeks 10 to 20.

@indiandataclub @dpdzero

#sqlbasics #SQLWIthIDC

[Adarsh Ankur @adarshankur19]

Wrapped up Day 19 of the SQL Challenge.

Practised window functions. ROW_NUMBER, RANK, DENSE_RANK. Learned how to rank weeks within each service without losing row detail. Pulled the top 3 weeks per service by satisfaction.

@indiandataclub @dpdzero

#SQL #SQLWithIDC

[Adarsh Ankur @adarshankur19]

Day 18 of the #IDC 21 Days of SQL Challenge 🚀

Worked with UNION vs UNION ALL to combine inpatient and outpatient lists. Use UNION to dedupe. Use UNION ALL for speed and aggregate afterwards.

@indiandataclub @dpdzero

#SQL #DataAnalytics #SQLWithIDC

[Adarsh Ankur @adarshankur19]

Day 17 of the #IDC 21 Days of SQL Challenge 🚀

Used subqueries in FROM and SELECT to report each service’s total admissions, difference from the average, and a simple rank label.

@indiandataclub @dpdzero

#LearnSQL #DataAnalytics #SQLWithIDC

[Adarsh Ankur @adarshankur19]

Day 16 of the #IDC 21 Days of SQL Challenge 🚀

Used subqueries and EXISTS to find patients from services that had at least one week with refusals and whose service average satisfaction is below the hospital avg.

@indiandataclub @dpdzero

#LearnSQL #DataAnalytics #SQLWithIDC