Data science

Data science

Right now, if you go to FanGraphs and sort by position player fWAR with 200 or more PAs, you get a list that maybe isn't that surprising. Or, rather, the placement of some guys might be surprising, but anyway... Aaron Judge, Bobby Witt Jr., Shohei Ohtani - those guys are phenomenal but they were better last year. Cal Raleigh is having a legendary season but has been an All-Star-plus quality guy for years now.

Trading can be exciting, but it is also unpredictable. Many traders lose money because they start trading live without testing their strategy. This is where backtesting comes in. It allows traders to test their strategies on historical trading data before risking real money. By understanding how a strategy would have worked in different market conditions, traders can make smarter decisions and reduce risks.

Netflix emphasizes that the more you use the platform, the more personalized it will become. Source. Are you sure your feeds - Netflix, Amazon, whatever social media you prefer - is providing you with personalized content? (More about the difference between personalization and customization here.) Are you being given content that aligns with your actual interests, or is the algorithm steering you around?

Google recently introduced a columnar engine for its globally distributed database, Spanner, intending to resolve the long-standing conflict between online transaction processing (OLTP) and analytical query processing (OLAP). The new feature, currently in preview, allows Spanner (Enterprise and Enterprise Plus editions) to handle both workloads simultaneously on a single database, eliminating the need for separate data warehouses and complex ETL (Extract, Transform, Load) pipelines.

Can you create a histogram of game total scores to see the distribution of scoring? Could you make a box plot comparing home vs away team scores? Let's create a scatter plot of temperature vs total score to see if weather affects scoring. Can you show me the distribution of betting spreads and how they relate to actual game results? Could you create a visualization showing win/loss records by team?

Want to use this as your default charts setting? Save this setup as a Chart Templates Switch the Market flag for targeted data from your country of choice. Open the menu and switch the Market flag for targeted data from your country of choice. Need More Chart Options? Right-click on the chart to open the Interactive Chart menu. Use your up/down arrows to move through the symbols.

Cheering at the stadiums and buying replica jerseys shifted to new ways to consume sports. Live matches on the Sportsbet betting platform, social media, fantasy leagues, highlights, and apps are capturing the attention of today's fans. Teams and brands understand that to keep fans engaged, they need to meet them wherever they are. This triggered an entirely new approach based on data about fans' behaviours, which proved to be just as valuable as the sports themselves.

Pew Research asked U.S. adults if certain behaviors in public, such as cursing or smoking, were acceptable. The above are the results for four age groups. For every behavior, the percentage of people who said it was rarely or never acceptable increased with age. Television and movies (and my own experiences) would tell you that sounds about right, but for some reason the clear trend surprised me. A quiz with the behaviors lets you get in on the action to see how crotchety you are.

Over the course of several years designing and delivering enterprise data platforms for a global pharmaceutical leader, I witnessed firsthand how data had evolved from a backend enabler to a frontline business asset. The organization was no longer just looking to report historical performance; it needed to predict outcomes, personalize patient engagement, customer engagement, brand performance and make regulatory decisions in near real time.

The ADF test results confirm that both the gas base fee and blob gas base fee time series are stationary, with test statistics of -6.3719 and -10.5237.

Snowpark Connect facilitates Apache Spark code execution directly within Snowflake warehouses, eliminating the need for separate Spark clusters and associated complexities like data movement.

Customers have been using Spark for a long time to process data and get it ready for use in analytics or in AI. The burden of running in separate systems with different compute engines creates complexity in governance and infrastructure.

Over a quarter of data and analytics professionals worldwide estimate that poor-quality data costs companies over $5 million annually, with 7% putting the figure at $25 million or more.

With the 2.1 release, Apache Flink also now supports Process Table Functions (PTFs), the most powerful kind of function for Flink SQL and Table API.

Ataccama closes that gap by turning complex data logic into plain language. Business users can now trace a data point's origin and understand how it was profiled or flagged without relying on technical experts.

In traditional systems, side effects lead to increased complexity, debugging challenges, and unpredictable behavior. CocoIndex adopts a pure data flow programming approach, ensuring reliability.

MySQL 9.0.0 introduces a new Vector datatype, JavaScript Stored Programs, updated library versions, and enhancements to the Event Scheduler, while deprecating old SHA-1 security.

Venn diagrams use overlapping circles to show the relationship between two or more things, facilitating comparisons across various fields.

A Common Table Expression (CTE) is a named, temporary result set defined within a single SQL statement, which helps in improving query readability and maintainability.

Both cache() and persist() store an RDD/DataFrame/Dataset in memory (or disk) to avoid recomputation. cache() is shorthand for persist(StorageLevel.MEMORY_ONLY), while persist() offers more control.