Skip to main content

About Us

 


About Solutions for Data Science 

Solutions for Data Science is a platform for sharing practical ideas, learning, and innovation across data science, business intelligence, artificial intelligence, machine learning, and modern analytics. We write about real-world problem solving using data, dashboards, automation, and intelligent systems that help individuals and businesses make better decisions.

Our content goes beyond traditional BI and reporting. Along with topics like data visualization, Power BI, Power Query, DAX, machine learning, and cloud analytics, we also explore fast-evolving areas such as Agentic AI, Generative AI, MLOps, DevOps, and the growing impact of Quantum Computing on the future of technology and decision intelligence.

At Solutions for Data Science, the goal is simple: make complex technology easier to understand and more useful in real life. Whether it is building dashboards, automating workflows, understanding AI agents, or learning how next-generation computing may transform industries, we focus on clear explanations, practical use cases, and ideas that matter.

The platform is inspired by hands-on industry experience across BI, AI, data engineering, and analytics transformation. It is built for learners, professionals, developers, and curious minds who want to stay updated not only with today’s tools, but also with the technologies shaping tomorrow.


Comments

Post a Comment

Popular posts from this blog

Diploma In Data Analytics with Excel and Power BI

I will Be teaching the below mentioned course in association with Sanyukta Trust Sanyukta Trust is proud to introduce a special course designed to transform you into Data Analytics Expert! In this course, you will benefit by learning how to use MS Excel and Power Bi (Business Intelligence) to read, clean, transform, visualize and analyze the data.  Join our AIBTE (All India Board of Technical Education) Government recognised Online course of Diploma in Data Analytics with Microsoft Excel and Power Business Intelligence (Bi) Session: 16 sessions plus assignment submissions  Course Highlights: - Introduction to Data Analytics & MS Office Tools - In depth knowledge of MS Excel & Power Bi - Clean and Transform data with Power Query - Data Modelling with Dax/Power Pivot  - 3D maps  - Interactive Charts and Data Visualization with Slicers - Interactive Dashboards Days: Twice a Week - Saturday - 5pm to 6.30pm - Sunday - 11am to 12.30pm ...
15 comments
Read more

20 Time Intelligence Dax Measures

20 Time Intelligence DAX measures in Power BI with examples: Year-to-Date Sales: css Copy code YTD Sales = TOTALYTD( [Total Sales] , Calendar [Date] ) Month-to-Date Sales: css Copy code MTD Sales = TOTALMTD( [Total Sales] , Calendar [Date] ) Quarter-to-Date Sales: css Copy code QTD Sales = TOTALQTD( [Total Sales] , Calendar [Date] ) Previous Year Sales: mathematica Copy code Previous Year Sales = CALCULATE ( [ Total Sales ] , SAMEPERIODLASTYEAR ( Calendar [ Date ] ) ) Year-over-Year Growth: css Copy code YoY Growth = DIVIDE( [Total Sales] - [Previous Year Sales] , [Previous Year Sales] ) Rolling 3-Month Average Sales: sql Copy code 3 M Rolling Avg Sales = AVERAGEX(DATESINPERIOD(Calendar[ Date ], MAX (Calendar[ Date ]), -3 , MONTH ), [Total Sales]) Cumulative Sales: scss Copy code Cumulative Sales = SUMX (FILTER(ALL(Calendar), Calendar [Date] <= MAX (Calendar[Date])), [Total Sales] ) Running Total Sales: scss Copy code Running Total Sales = SUMX (FILTER(ALL(Calendar), Calen...
Post a Comment
Read more

40 Power Query Editor features in Power BI

40 Power Query Editor features in Power BI along with examples: 1. Filter Rows: Remove rows based on conditions. Example: Remove rows with a null value in the "CustomerName" column. 2. Remove Duplicates: Eliminate duplicate rows. Example: Remove duplicate entries based on the "OrderID" column. 3. Sort Rows: Arrange rows in ascending or descending order. Example: Sort data by "Date" column in descending order. 4. Replace Values: Substitute one value with another. Example: Replace "N/A" with "Unknown" in the "Status" column. 5. Split Columns: Divide a column into multiple columns. Example: Split "FullName" into "FirstName" and "LastName." 6. Merge Queries: Combine data from multiple sources. Example: Merge customer and order data based on the "CustomerID." 7. Group By: Aggregate data based on a specific column. Example: Group sales data by "ProductCategory" and calculate the sum ...
Post a Comment
Read more