Have you captured all research papers for your study?

 Started writing articles with LinkedIn platform as it has many readers. 

Have you captured all research papers for your study? | LinkedIn. 

Promtology: Prompt Patterns for Prompts

Generated from Co-Pilot

Design patterns are like blueprints for solving common problems in software development. They aren't code snippets, but rather established templates or strategies that describe how to structure your code to make it more maintainable, flexible, and efficient.

Similar to design pattern in software development, there are pattens for other domains as well. Due to the increase usage of GenAI tools such as ChatGPT, Gemini or Co-Pilot, it is important identify the prompt patterns. By reading few research papers, following list of prompt patterns were identified.

Prompt Patterns contains 17 prompt patterns that are categorized into six categories.

Data Warehousing Best Practices

Continuing on the Data warehouse article series, this is the next article, Data Warehousing Best Practices for SQL Server at www.mssqltips.com.  I have updated the list of articles as well at Data is everywhere, but?: All Data Warehouse Related Articles. 

All Data Warehouse Related Articles

Writing is my passion. Writing has opened me many avenues over the years. Thought of combining all the data warehouse related article into a one post in different areas in data warehousing. 

DESIGN

What is a Data Warehouse? (mssqltips.com)

Things you should avoid when designing a Data Warehouse (sqlshack.com)

Infrastructure Planning for a SQL Server Data Warehouse (mssqltips.com)

Why Surrogate Keys are Needed for a SQL Server Data Warehouse (mssqltips.com)

Create an Extended Date Dimension for a SQL Server Data Warehouse (mssqltips.com)

SQL Server Temporal Tables Overview (mssqltips.com)

Data Warehousing Best Practices for SQL Server (mssqltips.com)

 SSIS

•       Testing Type 2 Slowly Changing Dimensions in a Data Warehouse (sqlshack.com)

•       Implementing Slowly Changing Dimensions (SCDs) in Data Warehouses (sqlshack.com)

•       Incremental Data Extraction for ETL using Database Snapshots (sqlshack.com)

•       Use Replication to improve the ETL process in SQL Server (sqlshack.com)

•       Using the SSIS Script Component as a Data Source (sqlshack.com)

•       Fuzzy Lookup Transformations in SSIS (sqlshack.com)

•       SSIS Conditional Split overview (sqlshack.com)

•       Loading Historical Data into a SQL Server Data Warehouse (mssqltips.com)

•       Retry SSIS Control Flow Tasks (mssqltips.com)

•       SSIS CDC Tasks for Incremental Data Loading (mssqltips.com)

 SSAS

•       Multi-language support for SSAS (sqlshack.com)

•       Enhancing Data Analytics with SSAS Dimension Hierarchies (sqlshack.com)

•       Improve readability with SSAS Perspectives (sqlshack.com)

•       SSAS Database Management (sqlshack.com)

•       OLAP Cubes in SQL Server (sqlshack.com)

•       SSAS Hardware Configuration Recommendations (mssqltips.com)

•       Create KPI in a SSAS Cube (mssqltips.com)

•       Monitoring SSAS with Extended Events (mssqltips.com)

SSRS

•       Exporting SSRS reports to multiple worksheets in Excel (sqlshack.com)

•       Enhancing Customer Experiences with Subscriptions in SSRS (sqlshack.com)

•       Alternate Row Colors in SSRS (sqlshack.com)

  Cloud

•       Migrate On-Premises SQL Server Business Intelligence Solution to Azure (mssqltips.com)

Other

•       Dynamic Data Masking in SQL Server (sqlshack.com)

•       Data Disaster Recovery with Log Shipping (sqlshack.com)

•       Using the SQL Server Service Broker for Asynchronous Processing (sqlshack.com)

•       SQL Server auditing with Server and Database audit specifications (sqlshack.com)

•       Archiving SQL Server data using Partitions - SQL Shack

•       Script to Create and Update Missing SQL Server Columnstore Indexes (mssqltips.com)

•       SQL Server Clustered Index Behavior Explained via Execution Plans (mssqltips.com)

•       SQL Server Maintenance Plan Index Rebuild and Reorganize Tasks (mssqltips.com)

•       SQL Server Resource Governor Configuration with T-SQL and SSMS (mssqltips.com)

 

Dataset : T20 Outcome of Extra Delivery & Free Hit

 

Cricket is a fascinating game as it provides much entertainment for spectators worldwide. With the instruction of the T20 format, most countries except New Zealand and Zimbabwe other countries have franchise cricket. Franchise cricket has spanned the global mark of T20 cricket. 

With cricket, there are common beliefs such as extra delivery that would be delivered once a bowler sends a wide or no ball, a free hit will have a very high impact, etc. 

The following dataset is collected to verify these hypotheses. Data collection is not yet completed as well are you need to wait for results for a couple of months. 

T20 OutComeofthe ExtraDelivery FreeHit | Kaggle

Violin Plot - Compare Data

Contrasting and comparing are part of data analysis to make vital decisions. A violin chart integrated with Box Plot is one of the charts that can be used to compare data. Let us see how we can utilize the Orange data mining tool to compare data using the Violin chart. 

Let us assume that the following is the dataset that we need to compare. 

In this dataset, there are five variables to compare. If you simply use a Violin chart without any preprocessing, you cannot compare charts in one chart. Therefore, there are a few preprocessing tasks to be done.

In the above preprocessing tasks, separate processing tasks are used for each variable. Select Columns, Edit Domain, and Feature Constructor are used. You can download the Orange workflow at ODM/Violine-Blog.ows at main · dineshasanka/ODM (github.com) 

Finally, all data flows are connected and then used to Violin Plot, as shown below.

Now you can see from the above chart, it is easily can be compared. In the above example, X1 has higher values.

How Data Science Project Works - From the Koobiyo Teledrama

About five years ago, the Koobiyo teledrama was very popular due to the uncharacteristic nature of the teledrama. It was a political teledrama, one reason the teledrama became popular. However, this post is not to discuss the political side of the tele drama but to discuss the data science side of it. 

Jehan, the main character of this teledrama, has the idea of building a tool to predict the future problems of people. He uses decision tree architecture to achieve this and explains his idea to his companion, Priyantha as shown in the below video. Watch from the 540th second.

So his idea is very clear. By using data like Gender, Marital Status, Professional Qualification, Parents' details, Friends' details, etc. 

After building the predictive software, he presents his case to his good friend Hiruni and his developer too joins the discussion in the following video. Watch from the 40th second.

After observing the work done by Jehan, Hiruni was impressed, but she had a very vital question to ask. "Why do you think people will buy this". Jehan was a little perplexed by the question, and he stressed that this is the software that everyone will have at their fingertips. Obviously, people know their problems they need solutions, not to suffer from their own problems. 

This is something important to us as data scientists. We are very good at technical details, but we need to understand the solution part of the tool. Remember, people need a solution to their problem not a technical tool. 

Then Hiruni provide a solution to Jehan and asked him to provide this to a human resource person to predict the future problems of its own employees. 

Then the solution can be viewed in the 21st second of the following video.

In the live demo, Jehan proves that it is important to select the best people for the organization from this software. It is clear how we should think from the business point of view rather than focusing on technical aspect mainly. As a data scientist, we need to look at the solution side of it.

Article : Learn about Data Warehousing for Analytical Needs

I started another article series at www.mssqltips.com on data warehousing. Even though there are many tools to build data warehouses, I still find there is a lack of conceptual knowledge in the community. So trying to cover that gap. So, read this What is a Data Warehouse? article and send your comments. 

Who is a Data Scientist? A Dragon a Pegasus or a Unicorn?

 

Unicorns, Pegasus, or dragons are mythical creatures that are created by combining the strong attributes of different animals. In Sri Lankan culture, we can see different dragons in temples. It is believed that these dragons are to protect various people and places. 

These dragons have seven parts from different people, as expressed in the following poem. 

As per the song, the dragon is a combination of

·       trunk of an elephant

·       legs of a lion

·       ears of a pig

·       teeth of a crocodile

·       eyes of a monkey

·       body of a fish

·       wings of a bird

The combination of these most strengthened parts will make the dragon a strong animal to achieve his required tasks. 

Isn't that the same with a data scientist? Don't they need different strengths, as shown below?  

Does that mean data scientists are mythical? Read the full article here E-Magazine – Magazine that bridges Management and IT

Clustering as Pre-Processing Technique for Classification

Clustering is often used to identify natural groups in a dataset. Since the clustering technique does not depend on any independent variable, the clustering technique is said to be unsupervised learning. The classification technique is supervised as it models data for a target or dependent variable. This post describes clustering as a pre-processing task for classification. This post has used the Orange Data Mining tool to demonstrate the above scenario. 

Following is the complete orange data mining workflow, and this is available in the Git Hub as well. 

The attrition sample dataset is used to demonstrate the above scenario. This dataset contains 18 variables with 1470 instances. Then the Jaccard distance is used, and it is essential to remember that you may have to choose a different distance technique in order to improve the results. 

Then hierarchical clustering is used for three clusters as too many clustering will have smaller clusters and may cause overfitting. Following is the cluster distribution for the three clusters. 

After the clustering is completed, the dataset is selected for each cluster and unnecessary columns (Clsuter and Selected) are removed.  Then for each cluster, different classification techniques were executed. This scenario uses SVM, Logistic Regression, Neural Network, Random Forest, AdaBoost, Naive Bayes, Tree, and kNN classification techniques. 

When classification techniques were executed for the entire dataset, without clustering, the best classification technique was Logistic Regression with a Classification accuracy of 87%.  

Following are the classification accuracies and other evaluation parameters for each cluster once it is evaluated with all the above said eight different classification techniques.

The above table shows that C1 and C2 clusters have higher accuracy than the full dataset. However, the third cluster performance is not as good as the overall dataset classification.

This technique is better when there are large volumes of data if not clustering will reduce the data which can lead to model overfitting.