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UX Design
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How GenAIs build diverging color schemes
- GenAIs build diverging color schemes for Pantone Mocha Mousse.
- Diverging color schemes are used in data visualization to represent data with a critical midpoint value and two ends of importance.
- GenAI systems like Gemini and Copilot create customized diverging color schemes based on the Pantone 2025 Color of the Year, Mocha Mousse.
- Pantone's Color of the Year concept is applied in their Pantone Matching System (PMS) and Pantone Fashion, Home + Interiors (FHI) System.
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Medium
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Image Credit: Medium
SQL for Fraud Detection: Spotting Suspicious Patterns Like a Pro! ️♂️
- This query will return any transactions that exceed $10,000.
- This query will help you find users who made more than 5 transactions in a single day.
- This query helps identify transactions made in locations the user hasn’t visited in the last 30 days.
- This query helps identify cases where different users made the same transaction amount within 1 day of each other.
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Medium
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Sunflowers: A Symbol of Beauty and Utility.
- Sunflower originated in North America and was domesticated by Native American tribes around 4,500 years ago.
- Sunflower quickly gained popularity in Europe after being introduced by Spanish explorers in the 1500s.
- It was widely grown in Russia in the 18th century, particularly for its oil.
- Today, sunflower is cultivated worldwide, with major producers including Russia, Ukraine, China, and the United States.
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HRM Asia
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HR Tech Asia 2025: Transforming C-Suite leadership for tomorrow’s workforce
- HR Tech Asia 2025 is organizing the C-Suite Leadership conference track to equip senior executives with the tools they need to succeed.
- The conference track will address leadership challenges and provide strategies for organizational excellence and value creation.
- The lineup includes sessions on empathy in leadership, people analytics, performance management, and employee purpose.
- The event aims to redefine the HR landscape and offers senior executives the opportunity to learn and network.
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Medium
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Image Credit: Medium
Why Mathematics Should Be Compulsory for Kenyan Filmmakers.
- Kenya's film industry often relies on hype over measurable metrics, but data is essential for its growth and success.
- A lack of data in the industry leads to challenges such as investor hesitation and piracy issues.
- Investors seek predictability, which is hindered by the industry's unclear return on investment and misdirected incentives.
- Piracy costs Kenya's film industry billions annually, and a lack of granular data exacerbates the problem.
- Global examples show that data-driven strategies work, providing insights for audience preferences, targeted distribution, and piracy mitigation.
- A blueprint for data-driven growth in Kenyan film includes mandating box office reporting, training data journalists, and leveraging existing frameworks like the Film Fund.
- Using data creatively can enhance storytelling and global appeal for Kenyan filmmakers, ultimately leading to industry success.
- By adopting metrics and leveraging data, Kenya can maximize its cinematic potential and create sustainable growth.
- Data-enabled creativity allows filmmakers to craft stories that resonate globally, while also addressing local market needs.
- In conclusion, data should be embraced as a creative tool to propel Kenya's film industry to new heights and foster accountability.
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Cloudblog
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Image Credit: Cloudblog
Accelerate analytics with AI-assisted data preparation in BigQuery, now GA
- Gartner reports that a significant amount of time is spent on data preparation for analytics-based tasks.
- BigQuery data preparation, assisted by Gemini, facilitates data wrangling by suggesting cleaning and transformation methods.
- This integration helps automate data pipelines and enables various users to efficiently prepare data for analysis.
- BigQuery data preparation is now generally available and can be integrated with BigQuery pipelines for end-to-end pipeline creation.
- Gemini offers comprehensive transformation capabilities and assists in data standardization and schema mapping.
- Visual data pipelines and error table support enhance data quality enforcement and ease of deployment.
- BigQuery pipelines allow the connection of data processing tasks, including data preparation, in defined sequences.
- Data preparation generates SQL code for easier review, integration, and collaboration in CI/CD processes.
- Customer testimonials from GAF, mCloud Technologies, and Public Value Technologies highlight the benefits of BigQuery data preparation.
- BigQuery data preparation streamlines data management, improves productivity, and empowers users with its AI capabilities.
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Medium
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From 92 Million Rows to a Usable Tool: Designing Data Solutions Without a BI Stack
- In 2025, the article discusses using Product Thinking, Design Thinking, and Business Intelligence to simplify complex data problems.
- The author, not initially involved in the project, bridged the gap between raw data and user needs.
- The process involved understanding requests, categorizing asks, and profiling user personas like Validators, Accountants, and Need to Discuss.
- Challenges arose in providing solutions that catered to different user needs and avoiding the 'Build Trap'.
- A tiered delivery approach was proposed with L1, L2, and L3 views based on user personas.
- Efforts were made to optimize performance and user experience through design choices like Balance Sheet views and structured data.
- The impact was seen in improved user experience and usability within Excel, even without BI tools.
- The focus on design thinking, user empathy, and product strategy led to a valuable and accessible solution.
- The article invites sharing experiences of building valuable solutions within unexpected constraints.
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Medium
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Simplify Your Power BI Reports: How to Use Field Parameters to Save Space
- Field Parameters in Power BI can help simplify reports and save space.
- Field Parameters allow for dynamic swapping between dimensions or measures in visualizations.
- Using Field Parameters can reduce the number of visuals and create a cleaner, more intuitive report.
- An example scenario involves an overcrowded Power BI report on student data. Field Parameters can be used to transform the report and uncover patterns.
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Siliconangle
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Image Credit: Siliconangle
Sourcetable gets $4.3M in funding to help everyone become a spreadsheet power user
- Sourcetable Inc. has launched an AI-powered spreadsheet to democratize data analysis for office workers.
- The startup secured $4.3 million in funding led by Bee Partners and other investors.
- Sourcetable aims to bridge the gap between 'power users' and average spreadsheet users with its AI capabilities.
- Users can interact with the spreadsheet using natural language or voice commands.
- The product is described as the first 'self-driving' spreadsheet with autopilot features.
- Sourcetable can perform tasks like creating financial models, charts, pivot tables, data cleaning, and more with simple commands.
- It can interpret data context, work with messy data, and ask for clarification if instructions are unclear.
- Sourcetable targets the vast majority of spreadsheet users who lack advanced skills in data analysis.
- The company emphasizes AI automation to make data analysis accessible to all users.
- Sourcetable ensures accuracy through real-time evaluation loops and offers access to various LLM models for users.
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Medium
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Image Credit: Medium
Mastering the Basics of Python for Data Science
- Keywords are reserved words in Python that have specific meanings and cannot be used for variable names. Examples include if, else, for, while, import, and return.
- Python’s dynamic typing makes it easy to assign values without explicitly declaring their type. It supports various data types such as int, float, str, bool, complex, and more.
- Python uses indentation to define blocks of code, ensuring readability and consistency. Forgetting to indent properly results in syntax errors.
- Strings are powerful in Python. You can perform slicing, concatenation, and use methods like upper(), lower(), replace(), and split().
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15 Likes
Medium
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Image Credit: Medium
11 Skills Employers Are Looking for in AI (and 4 They Aren’t)
- Companies are prioritizing 11 essential skills for AI and data science roles.
- These skills include strong SQL abilities, practical knowledge of machine learning and data cleaning, experience with deploying models in the cloud, effective data visualization, communication skills to present findings, problem-solving abilities, understanding of responsible AI use, staying up-to-date with the field, showcasing real-world projects and practical experience, focusing on applying models rather than deriving them from scratch, and fluency in relevant programming languages.
- Employers are less concerned about linear algebra proofs and calculus-based derivations, a long list of programming languages on a resume, and formal degrees.
- The market is seeking individuals who can deliver value, solve real problems, and communicate effectively.
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14 Likes
Medium
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Unveiling the Power of Data: A Journey Through Statistical Concepts
- Statistics is the science of collecting, organizing, analyzing, and interpreting data, enabling us to uncover hidden patterns and make evidence-based predictions.
- Statistical tools have become the analytical backbone of data science, machine learning, and AI, driving critical applications across industries.
- Data, measured differently, guides the selection of appropriate statistical techniques and provides insights through descriptive statistics.
- Statistical thinking empowers professionals to make better decisions in a data-driven world, with various applications in healthcare, retail, finance, manufacturing, and more.
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16 Likes
Medium
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Image Credit: Medium
Understanding Linear Regression: The Basics Made Easy
- Linear regression is a technique to find the equation of a line that best represents the data points.
- Programming exercises in the Google MLCC provide practical experience in implementing linear regression.
- Linear regression is used to predict values based on the relationship between variables.
- Linear regression serves as the foundation for more complex machine learning algorithms.
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6 Likes
Medium
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Humans are the smartest beings on earth but have you ever thought that human has biases that may…
- The Bayesian Technique is a method that can be used to address biases in decision-making.
- The technique involves considering the prior probability, likelihood, and total probability to make an informed decision.
- For example, when determining whether Steve is more likely to be a librarian or a farmer based on his personality traits, the base rate plays a significant role.
- Although people may have a bias toward labeling Steve as a librarian due to representativeness heuristic, the base rate of farmers outweighs the likelihood of librarians having those traits.
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Medium
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Image Credit: Medium
Unlocking Time: Using Snowflake Time-Series Functions to Power Game Analytics (and Beyond)
- Snowflake's native time-series functions can transform raw game telemetry into actionable insight.
- Time-series analysis enables tracking how player actions evolve over time, revealing patterns like session length trends, retention decay, churn behavior, and engagement spikes.
- Understanding time-based player interactions is essential for optimizing monetization and improving retention, especially for games with dynamic content and live events.
- Time-series analytics in Snowflake provides a more powerful and simpler way to analyze data compared to relying on static or aggregated data snapshots.
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