Year in review December 27, 2023 | By Caroline Morris

With political, social and climate crises mounting throughout the world, its been a tough year for optimism. Yet that is what Michael Miebach offered at the 2023 United Nations General Assembly this September. In a speech before the U.N. Security Council, Mastercards CEO laid out how well-planned public-private partnerships can help advance humanitarian causes. He also emphasized that one of the most valuable contributions private entities can make is not necessarily money, but their expertise.

For Mastercard, that expertise is the technology and tools to analyze data, and the company is using it to put issues of sustainability, equity and inclusion into a broader context and zero in on individuals who need the most help.

When responsibly used, data can be an enormous force for good in the world. Here are a few examples of how that evolved over the past year.

Creating safe, happy homes for those in need

After the war broke out in Ukraine in 2022, refugees were pouring across the border into Poland. Among the displaced was Alona, below, who traveled with her young son. But even in asylum, they faced significant challenges as they tried to adapt to a new country in which many cities offered scarce work and housing options.

Alona found Where to Settle, Mastercards platform to help Ukrainian refugees figure out the ideal place to live by presenting users with the estimated cost of living, potential job opportunities and housing offers in different locations. Now, in their new hometown of Sochaczew, Alona and her son are thriving.

And the platform is expanding beyond refugees. Polish students are taking advantage of the app as they move for university. The idea is that Where to Settle which Fortune magazine recognized in itsannual Change the World listof companies mobilizing the creative tools of capitalism to help solve social problems will eventually be available wherever Mastercard is available.

Empowering informal workers

In impoverished countries like Mozambique, informal workers those whose sources of income are not recorded by the government struggle to make ends meet, and they do not possess the resources or know-how to grow their businesses. However, Data for Workforce Nurturing, or D4WN, is empowering these workers to earn a living wage. Pulling data outputs from Com-Hector, a virtual assistant that helps low-income workers with advertising and business management, and Biscate, a digital job board for the informal sector where clients can find workers, D4WN provides business insights to users so they can build their businesses and gain financial resilience and independence.

D4WN was one of nine awardees around the world to winData.orgs$10 millionInclusive Growth and Recovery Challenge, which sought innovative examples of data for social impact, with support from the Mastercard Center for Inclusive Growthandthe Rockefeller Foundation.

Keeping humans at the center of AI

This year, Mastercard hosted its second annual Impact Data Summit, bringing together leaders in the world of AI, tech and data science to talk about how data can help solve some of humanitys biggest challenges, including climate action, gender equality and inclusive economic growth.

Participants acknowledged that, for all of its promise, AI can misconstrue or misrepresent data to exacerbate existing social issues or even spark new problems. The key to leveraging technology is keeping people at the center, from using inclusive data to developing impactful public-private partnerships to making sure that AI solutions serve human good above all else.

See the original post here:

How data science is changing the world for the better in 2023 - Mastercard

How SURGE substantially improves knowledge relevance through targeted augmentation while retaining language fluency

Generative pre-trained models have shown impressive fluency and coherence when used for dialogue agents. However, a key limitation they suffer from is the lack of grounding in external knowledge. Left to their pre-trained parameters alone, these models often generate plausible-sounding but factually incorrect responses, also known as hallucinations.

Prior approaches to mitigate this have involved augmenting the dialogue context with entire knowledge graphs associated with entities mentioned in the chat. However, this indiscriminate conditioning on large knowledge graphs brings its own problems:

Limitations of Naive Knowledge Graph Augmentation:

To overcome this, Kang et al. 2023 propose the SUbgraph Retrieval-augmented GEneration (SURGE) framework, with three key innovations:

This allows providing precisely the requisite factual context to the dialogue without dilution from irrelevant facts or model limitations. Experiments show SURGE reduces hallucination and improves grounding.

Follow this link:

Optimizing Retrieval-Augmented Generation (RAG) by Selective Knowledge Graph Conditioning - Towards Data Science

The world of big data is only getting bigger: Organizations of all stripes are producing more data, in various forms, year after year. The ever-increasing volume and variety of data is driving companies to invest more in big data tools and technologies as they look to use all that data to improve operations, better understand customers, deliver products faster and gain other business benefits through analytics applications.

Enterprise data leaders have a multitude of choices on big data technologies, with numerous commercial products available to help organizations implement a full range of data-driven analytics initiatives -- from real-time reporting to machine learning applications.

In addition, there are many open source big data tools, some of which are also offered in commercial versions or as part of big data platforms and managed services. Here are 18 popular open source tools and technologies for managing and analyzing big data, listed in alphabetical order with a summary of their key features and capabilities. The tools were chosen based on information from sources such as Capterra, G2 and Gartner as well as vendor websites.

Airflow is a workflow management platform for scheduling and running complex data pipelines in big data systems. It enables data engineers and other users to ensure that each task in a workflow is executed in the designated order and has access to the required system resources. Airflow is also promoted as easy to use: Workflows are created in the Python programming language, and it can be used for building machine learning models, transferring data and various other purposes.

The platform originated at Airbnb in late 2014 and was officially announced as an open source technology in mid-2015; it joined the Apache Software Foundation's incubator program the following year and became an Apache top-level project in 2019. Airflow also includes the following key features:

Databricks Inc., a software vendor founded by the creators of the Spark processing engine, developed Delta Lake and then open sourced the Spark-based technology in 2019 through the Linux Foundation. The company describes Delta Lake as "an open format storage layer that delivers reliability, security and performance on your data lake for both streaming and batch operations."

Delta Lake doesn't replace data lakes; rather, it's designed to sit on top of them and create a single home for structured, semistructured and unstructured data, eliminating data silos that can stymie big data applications. Furthermore, using Delta Lake can help prevent data corruption, enable faster queries, increase data freshness and support compliance efforts, according to Databricks. The technology also comes with the following features:

The Apache Drill website describes it as "a low latency distributed query engine for large-scale datasets, including structured and semi-structured/nested data." Drill can scale across thousands of cluster nodes and is capable of querying petabytes of data by using SQL and standard connectivity APIs.

Designed for exploring sets of big data, Drill layers on top of multiple data sources, enabling users to query a wide range of data in different formats, from Hadoop sequence files and server logs to NoSQL databases and cloud object storage. It can also do the following:

Druid is a real-time analytics database that delivers low latency for queries, high concurrency, multi-tenant capabilities and instant visibility into streaming data. Multiple end users can query the data stored in Druid at the same time with no impact on performance, according to its proponents.

Written in Java and created in 2011, Druid became an Apache technology in 2018. It's generally considered a high-performance alternative to traditional data warehouses that's best suited to event-driven data. Like a data warehouse, it uses column-oriented storage and can load files in batch mode. But it also incorporates features from search systems and time series databases, including the following:

Another Apache open source technology, Flink is a stream processing framework for distributed, high-performing and always-available applications. It supports stateful computations over both bounded and unbounded data streams and can be used for batch, graph and iterative processing.

One of the main benefits touted by Flink's proponents is its speed: It can process millions of events in real time for low latency and high throughput. Flink, which is designed to run in all common cluster environments, also includes the following features:

A distributed framework for storing data and running applications on clusters of commodity hardware, Hadoop was developed as a pioneering big data technology to help handle the growing volumes of structured, unstructured and semistructured data. First released in 2006, it was almost synonymous with big data early on; it has since been partially eclipsed by other technologies but is still widely used.

Hadoop has four primary components:

Initially, Hadoop was limited to running MapReduce batch applications. The addition of YARN in 2013 opened it up to other processing engines and use cases, but the framework is still closely associated with MapReduce. The broader Apache Hadoop ecosystem also includes various big data tools and additional frameworks for processing, managing and analyzing big data.

Hive is SQL-based data warehouse infrastructure software for reading, writing and managing large data sets in distributed storage environments. It was created by Facebook but then open sourced to Apache, which continues to develop and maintain the technology.

Hive runs on top of Hadoop and is used to process structured data; more specifically, it's used for data summarization and analysis, as well as for querying large amounts of data. Although it can't be used for online transaction processing, real-time updates, and queries or jobs that require low-latency data retrieval, Hive is described by its developers as scalable, fast and flexible.

Other key features include the following:

HPCC Systems is a big data processing platform developed by LexisNexis before being open sourced in 2011. True to its full name -- High-Performance Computing Cluster Systems -- the technology is, at its core, a cluster of computers built from commodity hardware to process, manage and deliver big data.

A production-ready data lake platform that enables rapid development and data exploration, HPCC Systems includes three main components:

Hudi (pronounced hoodie) is short for Hadoop Upserts Deletes and Incrementals. Another open source technology maintained by Apache, it's used to manage the ingestion and storage of large analytics data sets on Hadoop-compatible file systems, including HDFS and cloud object storage services.

First developed by Uber, Hudi is designed to provide efficient and low-latency data ingestion and data preparation capabilities. Moreover, it includes a data management framework that organizations can use to do the following:

Iceberg is an open table format used to manage data in data lakes, which it does partly by tracking individual data files in tables rather than by tracking directories. Created by Netflix for use with the company's petabyte-sized tables, Iceberg is now an Apache project. According to the project's website, Iceberg typically "is used in production where a single table can contain tens of petabytes of data."

Designed to improve on the standard layouts that exist within tools such as Hive, Presto, Spark and Trino, the Iceberg table format has functions similar to SQL tables in relational databases. However, it also accommodates multiple engines operating on the same data set. Other notable features include the following:

Kafka is a distributed event streaming platform that, according to Apache, is used by more than 80% of Fortune 100 companies and thousands of other organizations for high-performance data pipelines, streaming analytics, data integration and mission-critical applications. In simpler terms, Kafka is a framework for storing, reading and analyzing streaming data.

The technology decouples data streams and systems, holding the data streams so they can then be used elsewhere. It runs in a distributed environment and uses a high-performance TCP network protocol to communicate with systems and applications. Kafka was created by LinkedIn before being passed on to Apache in 2011.

The following are some of the key components in Kafka:

Kylin is a distributed data warehouse and analytics platform for big data. It provides an online analytical processing (OLAP) engine designed to support extremely large data sets. Because Kylin is built on top of other Apache technologies -- including Hadoop, Hive, Parquet and Spark -- it can easily scale to handle those large data loads, according to its backers.

It's also fast, delivering query responses measured in milliseconds. In addition, Kylin provides an ANSI SQL interface for multidimensional analysis of big data and integrates with Tableau, Microsoft Power BI and other BI tools. Kylin was initially developed by eBay, which contributed it as an open source technology in 2014; it became a top-level project within Apache the following year. Other features it provides include the following:

Pinot is a real-time distributed OLAP data store built to support low-latency querying by analytics users. Its design enables horizontal scaling to deliver that low latency even with large data sets and high throughput. To provide the promised performance, Pinot stores data in a columnar format and uses various indexing techniques to filter, aggregate and group data. In addition, configuration changes can be done dynamically without affecting query performance or data availability.

According to Apache, Pinot can handle trillions of records overall while ingesting millions of data events and processing thousands of queries per second. The system has a fault-tolerant architecture with no single point of failure and assumes all stored data is immutable, although it also works with mutable data. Started in 2013 as an internal project at LinkedIn, Pinot was open sourced in 2015 and became an Apache top-level project in 2021.

The following features are also part of Pinot:

Formerly known as PrestoDB, this open source SQL query engine can simultaneously handle both fast queries and large data volumes in distributed data sets. Presto is optimized for low-latency interactive querying and it scales to support analytics applications across multiple petabytes of data in data warehouses and other repositories.

Development of Presto began at Facebook in 2012. When its creators left the company in 2018, the technology split into two branches: PrestoDB, which was still led by Facebook, and PrestoSQL, which the original developers launched. That continued until December 2020, when PrestoSQL was renamed Trino and PrestoDB reverted to the Presto name. The Presto open source project is now overseen by the Presto Foundation, which was set up as part of the Linux Foundation in 2019.

Presto also includes the following features:

Samza is a distributed stream processing system that was built by LinkedIn and is now an open source project managed by Apache. According to the project website, Samza enables users to build stateful applications that can do real-time processing of data from Kafka, HDFS and other sources.

The system can run on top of Hadoop YARN or Kubernetes and also offers a standalone deployment option. The Samza site says it can handle "several terabytes" of state data, with low latency and high throughput for fast data analysis. Via a unified API, it can also use the same code written for data streaming jobs to run batch applications. Other features include the following:

Apache Spark is an in-memory data processing and analytics engine that can run on clusters managed by Hadoop YARN, Mesos and Kubernetes or in a standalone mode. It enables large-scale data transformations and analysis and can be used for both batch and streaming applications, as well as machine learning and graph processing use cases. That's all supported by the following set of built-in modules and libraries:

Data can be accessed from various sources, including HDFS, relational and NoSQL databases, and flat-file data sets. Spark also supports various file formats and offers a diverse set of APIs for developers.

But its biggest calling card is speed: Spark's developers claim it can perform up to 100 times faster than traditional counterpart MapReduce on batch jobs when processing in memory. As a result, Spark has become the top choice for many batch applications in big data environments, while also functioning as a general-purpose engine. First developed at the University of California, Berkeley, and now maintained by Apache, it can also process on disk when data sets are too large to fit into the available memory.

Another Apache open source technology, Storm is a distributed real-time computation system that's designed to reliably process unbounded streams of data. According to the project website, it can be used for applications that include real-time analytics, online machine learning and continuous computation, as well as extract, transform and load jobs.

Storm clusters are akin to Hadoop ones, but applications continue to run on an ongoing basis unless they're stopped. The system is fault-tolerant and guarantees that data will be processed. In addition, the Apache Storm site says it can be used with any programming language, message queueing system and database. Storm also includes the following elements:

As mentioned above, Trino is one of the two branches of the Presto query engine. Known as PrestoSQL until it was rebranded in December 2020, Trino "runs at ludicrous speed," in the words of the Trino Software Foundation. That group, which oversees Trino's development, was originally formed in 2019 as the Presto Software Foundation; its name was also changed as part of the rebranding.

Trino enables users to query data regardless of where it's stored, with support for natively running queries in Hadoop and other data repositories. Like Presto, Trino also is designed for the following:

NoSQL databases are another major type of big data technology. They break with conventional SQL-based relational database design by supporting flexible schemas, which makes them well suited for handling huge volumes of all types of data -- particularly unstructured and semistructured data that isn't a good fit for the strict schemas used in relational systems.

NoSQL software emerged in the late 2000s to help address the increasing amounts of diverse data that organizations were generating, collecting and looking to analyze as part of big data initiatives. Since then, NoSQL databases have been widely adopted and are now used in enterprises across industries. Many are open source technologies that are also offered in commercial versions by vendors, while some are proprietary products controlled by a single vendor.

In addition, NoSQL databases themselves come in various types that support different big data applications. These are the four major NoSQL categories, with examples of the available technologies in each one:

Multimodel databases have also been created with support for different NoSQL approaches, as well as SQL in some cases; MarkLogic Server and Microsoft's Azure Cosmos DB are examples. Many other NoSQL vendors have added multimodel support to their databases. For example, Couchbase Server now supports key-value pairs, and Redis offers document and graph database modules.

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.

Editor's note: This unranked list is based on web research of big data tools that cover a diverse range of capabilities and features. Our research included data from respected research firms, including Gartner and Capterra, as well as vendor websites.

Visit link:

18 Top Big Data Tools and Technologies to Know About in 2024 - TechTarget

Its not a surprise that AI and data science professionals remain in demand given the explosion of AI models on the market, and the rapid-fire advancements since. But just as companies are still struggling to figure out business use cases for LLMs, they also struggle to identify corresponding job roles. To make matters worse, there are additional obstacles popping up along the way.

Theres the acceleration of AI-adjacent talent wars: Its not just AI, says Babak Hodjat, CTO of AI at Cognizant Technology Solutions.

What types of job roles fall into the category of AI-adjacent talent?

At Persado, according to Frank Chen, the companys head of natural language processing, that list includes:

Research scientists who conduct cutting-edge research to develop new models and techniques for generative AI tasks.

Machine learning/NLP/data/software engineers who build and deploy GenAI models.

Data scientists and analysts who experiment and analyze model results.

Content specialists who create guidelines to control the generated content, collaborate with the AI development team to refine the generated content, evaluate the quality of generated content, and provide feedback to improve GenAI models.

Experienced UX/UI designers who ensure the designed AI interface aligns with user needs.

Related:Prompt Engineering: Passing Fad or the Future of Programming?

But job competition is also heating up elsewhere.

In 2024, well also see the war for cyber and software development talent grow more contentious as a result of major privacy concerns and savings-driven budget reallocations born from the generative AI boom, Hodjat says.

No doubt more job roles will emerge while others will soon fade away as AI matures, and companies become more adept at using it.

Data science and AI continue to be industries with strong growth projections, but there are a few jobs in particular that should be in demand for the foreseeable future, says Richard Gardner, CEO of Modulus, which touts a client list including NASA, NASDAQ, Goldman Sachs, Merrill Lynch, JP Morgan Chase, Bank of America, Barclays, Siemens, Shell, Yahoo!, Microsoft, Cornell University, and the University of Chicago.

Some job titles are already familiar such as prompt engineers and AI content editors. But those roles do not have standard descriptions or pay scales. For example, job boards are filled with ads for AI content editors at a mere $20 to $40 an hour. These are typically posted by employers who think these jobs require simple skills and little effort. But that is blatantly untrue. Depending on the complexity of the subject matter, it can actually take humans longer and exert more effort to edit and fact-check GenAI outputs than it does to write the thing from scratch.

Related:Hire or Upskill? The Burning Question in an Age of Runaway AI

Prompt engineer job roles also see a wide swing in job descriptions and pay scales. Sometimes vague job descriptions and low pay offers are due to an employers lack of experience in working with AI or a general cluelessness of what use cases they have for AI. Other times, its the opposite. The employer knows exactly what technical and linguistic skills they need from this group of job candidates and the pay offered better reflects the level of complexity.

In any case, according to ZipRecruiter, as of Nov 29, 2023, the average annual pay for a Prompt Engineering in the United States is $62,977 a year. Just in case you need a simple salary calculator, that works out to be approximately $30.28 an hour.

Eventually the job of prompt engineer will likely disappear as AI agents can already write their own prompts. But for now, most companies are looking to hire prompt engineers to get the most out of AI while also keeping token costs down.

As many would expect, the demand for large language model (LLM) engineers and natural language processing (NLP) engineers is on the rise.

Related:The IT Jobs AI Could Replace and the Ones It Could Create

The new and highly specialized role known as the LLM Engineer is primarily found within organizations that have reached an advanced stage in their AI journey, having conducted numerous experiments but now facing challenges in the operationalization of their AI models at scale, says Kjell Carlsson, head of data science strategy and evangelism at Domino Data Lab.

Glassdoor reports that as of November 2023, the estimated total pay for a LLM engineer is $164,029 per year in the United States area, with an average salary of $129,879 per year. The range of total pay is between $123k and $224k. ZipRecruiter pegs average total pay for this group at $142,663 per year, or $69 per hour.

NLP engineers are seeing an uptick in demand for AI and non-AI based projects but is itself continuing to evolve.

For example, Natural Language Processing Engineers, which essentially work to make applications that process human language, are currently in high demand for chatbots, but, over time, will continue to see demand for sentiment analyses and content recommendation systems, Gardner says.

Glassdoor reports the pay range for NLP engineers to be between $130k and $180k per year. Talent.com reports a wider pay spectrum of between $100k and $210k a year.

A range of other jobs are emerging, too. Topping this list is AI agent jobs and skills.

The agent view of AI [autonomous AI agents] will take on an increasingly significant role in AI-enablement projects, becoming a sought-after skill, Hodjat says. Various orchestration frameworks and platforms will vie to become the standard and software engineering will move towards adopting LLM-based agents into the fabric of software systems.

Other emerging job roles are harder to define and peg to a salary range.

Some of the most sought-after AI positions today include machine learning engineer, AI engineer, and AI architect, says Shmuel Fink, Chair Master of Science in data analytics, Touro University Graduate School of Technology. Nevertheless, several other AI roles are also gaining prominence, such as AI ethicist, AI product manager, AI researcher, computer vision engineer, robotics engineer, and AI safety engineer. Moreover, there are positions that require industry-specific expertise, like a healthcare AI engineer.

But back at the ranch, employees in any job role will become more valuable if they possess AI skills. As they gain those skills, some specialized job roles will evolve while others disappear. The one thing that is certain is that theres far more AI-driven and AI-adjacent change to come.

Original post:

Hot Jobs in AI/Data Science for 2024 - Hot Jobs in AI/Data Science for 2024 - InformationWeek

Sorting is the process of organizing elements in a structured manner. QuickSort is one of the most popular sorting algorithms that uses nlogn comparisons to sort an array of n elements in a typical situation. QuickSort is based on the divide-and-conquer strategy.

QuickSort is a sorting algorithm that uses a divide-and-conquer strategy to sort an array. It does so by selecting a pivot element and then sorting values larger than it on one side and smaller to the other side, and then it repeats those steps until the array is sorted. It is useful for sorting big data sets.

Well take a look at the quicksort algorithm in this tutorial and see how it works.

QuickSort is a fast sorting algorithm that works by splitting a large array of data into smaller sub-arrays. This implies that each iteration splits the input into two components, sorts them and then recombines them. The technique is highly efficient for big data sets because its average and best-case complexity is O(n*logn).

QuickSort was created by Tony Hoare in 1961 and remains one of the most effective general-purpose sorting algorithms available today. It works by recursively sorting the sub-lists to either side of a given pivot and dynamically shifting elements inside the list around that pivot.

As a result, the quicksort method can be summarized in three steps:

More on Data ScienceBubbleSort Time Complexity and Algorithm Explained

Lets take a look at an example to get a better understanding of the quicksort algorithm. In this example, the array below contains unsorted values, which we will sort using quicksort.

The process starts by selecting one element known as the pivot from the list. This can be any element. A pivot can be:

For this example, well use the last element, 4, as our pivot.

Now, the goal here is to rearrange the list such that all the elements less than the pivot are to its left, and all the elements greater than the pivot are to the right of it. Remember:

Lets simplify the above example,

As elements 2, 1, and 3 are less than 4, they are on the pivots left side. Elements can be in any order: 1,2,3, or 3,1,2, or 2,3,1. The only requirement is that all of the elements must be less than the pivot. Similarly, on the right side, regardless of their sequence, all components should be greater than the pivot.

In other words, the algorithm searches for every value that is smaller than the pivot. Values smaller than pivot will be placed on the left, while values larger than pivot will be placed on the right. Once the values are rearranged, it will set the pivot in its sorted position.

Once we have partitioned the array, we can break this problem into two sub-problems. First, sort the segment of the array to the left of the pivot, and then sort the segment of the array to the right of the pivot.

Lets cover a few key advantages of using quicksort:

Despite being the fastest algorithm, quicksort has a few drawbacks. Lets look at some of the drawbacks of quicksort.

The subarrays are rearranged in a certain order using the partition method. You will find various ways to partition. Here, we will see one of the most used methods.

Lets look at quicksort programs written in JavaScript and Python programming languages. Wellstart by creating a function that allows you to swap two components.

Now, lets add a function that uses the final element (last value) as the pivot, moves all smaller items to the left of pivot, and all larger elements to the right of the pivot, and places the pivot in the appropriate location in the sorted array.

Next, lets add the main function that will partition and sort the elements.

Lets finish off by adding a function to print the array.

Here is the full code of quicksort implementation for JavaScript:

Now, lets look at quicksort programs written in Python.Well start by creating a function which is responsible for sorting the first and last elements of an array.

Next, well add the main function that implements quick_sort.

Lets finish off by adding a function to print the array.

Here is the full code of quicksort implementation in Python.

Lets look at the space and time complexity of quicksort in the best, average and worst case scenarios. In general, the time consumed by quicksort can be written as follows:

Here, T(k) and T(n-k-1)refer to two recursive calls, while the last term O(n) refers to the partitioning process. The number of items less than pivot is denoted by k.

When the partitioning algorithm always chooses the middle element or near the middle element as the pivot, the best case scenario happens. Quicksorts best-case time complexity is O (n*logn). The following is the best-case recurrence.

This occurs when the array elements are in a disordered sequence that isnt increasing or decreasing properly. QuickSorts average case time complexity is O(n*logn). The following is the average-case recurrence.

The worst-case situation is when the partitioning algorithm picks the largest or smallest element as the pivot element every time. The worst-case time complexity of quicksort is O (n2). The following is the worst-case recurrence.

The space complexity for quicksort is O(log n).

The sorting algorithm is used to find information, and since quicksort is the fastest, it is frequently used as a more efficient search approach.

QuickSort may have a few drawbacks, but it is the fastest and most efficient sorting algorithm available. QuickSort has an O (logn) space complexity, making it an excellent choice for situations where space is restricted.

Although the worst-case running time is always the same, quicksort is often faster than HeapSort (nlogn). QuickSort takes up less space than heap sort due to the fact that a heap is nearly a full binary tree with pointers overhead. So, when it comes to sorting arrays, quicksort is preferred.

An error occurred.

More on PythonHow to Implement Binary Search in Python

There might be a few drawbacks to quicksort, but it is the fastest sorting algorithm out there. QuickSort is an efficient algorithm that performs well in practice.

In this article, we learned what quicksort is, its benefits and drawbacks and how to implement it.

See more here:

QuickSort Algorithm: An Overview - Built In