#Data structures and algorithms courses | Explore Tumblr Posts and Blogs

scholarhatedu · 8 months

Text

Best Data Structures and Algorithms Course

Data Structures and Algorithms are the backbone of computer science. They are essential for solving complex problems efficiently, making them indispensable skills for programmers and software developers. To master these concepts, it's crucial to choose the right course. In this article, we'll explore some of the best-known courses available online, and we'll also introduce you to scholarhat's Best Data Structures and Algorithms Course, a program designed to take your understanding to the next level.

1.Udemy - Data Structures and Algorithms: Deep Dive Using JavaUdemy Data Structures and Algorithms CourseThis course provides a comprehensive understanding of Data Structures and Algorithms using Java. It covers a wide range of topics with hands-on coding exercises. 2. Scholarhat - Best Data Structures and Algorithms Course

scholarhat Best Data Structures and Algorithms Course

When it comes to mastering Data Structures and Algorithms, scholarhat's Best Data Structures and Algorithms Course stands out as an exceptional choice. With a curriculum meticulously designed by experts, this program combines theoretical knowledge with hands-on coding exercises. Moreover, the course emphasizes practical application, ensuring you're not only learning the theory but also applying it in real-world scenarios. With experienced instructors and a supportive community, this course offers an unparalleled learning experience.

3. LinkedIn Learning - Learning Data Structures and AlgorithmsLinkedIn Learning Data Structures and Algorithms CourseThis course offers a practical approach to learning Data Structures and Algorithms, with a focus on real-world applications.

4. GeeksforGeeks - Data Structures and AlgorithmsGeeksforGeeks Data Structures and Algorithms CourseGeeksforGeeks offers a plethora of tutorials, articles, and practice problems on Data Structures and Algorithms. It's a valuable resource for self-paced learning.

5. Codecademy - AlgorithmsCodecademy Algorithms CourseCodecademy provides an interactive platform for learning algorithms. The course is suitable for beginners and covers essential algorithmic concepts.

6. LeetCode - Data Structures and AlgorithmsLeetCodeWhile not a traditional course, LeetCode is a popular platform for practicing Data Structures and Algorithms through coding challenges. It's an excellent resource to reinforce your skills.

7. YouTube - MyCodeSchoolMyCodeSchool YouTube ChannelMyCodeSchool offers a series of video tutorials on Data Structures and Algorithms. The tutorials are clear, concise, and perfect for visual learners.

8. Hackerrank - Data StructuresHackerrank Data Structures CourseHackerrank offers a series of tutorials and challenges to help you master various data structures.

9. Princeton University - Algorithms Part I and Part II (Coursera)Princeton University Algorithms Part I Princeton University Algorithms Part IIThis series of courses covers fundamental algorithms and data structures, taught by renowned professors from Princeton University. Conclusion

Selecting the right course to master Data Structures and Algorithms is crucial for any aspiring programmer. The courses mentioned above offer excellent options for learners at various levels of expertise. However, for an exceptional learning experience, consider enrolling in scholarhat's Best Data Structures and Algorithms Course. With its industry-leading curriculum and personalized support, it's the perfect choice to advance your skills in this critical area of computer science.

Enroll in scholarhat's course now. Take the first step towards becoming a Data Structures and Algorithms expert!

#DSA #dsacourses #bestdsacourses #best data structures and algorithms course

5 notes · View notes

limatsoftsolution · 3 months

Text

Data Structures and Algorithms Course: A 6-Month Learning Journey with Li-Mat | Bangalore

LI-MAT's Data Structures and Algorithms Course is not merely an educational program but a transformative 6-month journey. Beyond imparting knowledge, the course equips students with practical skills, instills confidence, and validates their expertise with a prestigious certification. Read More -

#data structures #algorithms #course #certification #job placement services #it training courses #web development #bangalore #india #technology #software

0 notes

markcraft3636 · 6 months

Text

What is ServiceNow |Introduction | User Interface| Application & Filter Navigation | Complete Course

youtube

ServiceNow is planned with intelligent systems to speed up the work process by providing solutions to amorphous work patterns. Each employee, customer, and machine in the enterprise is related to ServiceNow, allowing us to make requests on a single cloud platform. Various divisions working with the requests can assign, prioritize, correlate, get down to root cause issues, gain real‑time insights, and drive action. This workflow process helps the employees to work better, and this would eventually improve the service levels. ServiceNow provides cloud services for the entire enterprise. This module consists User Interface and Navigation. The Objective of this module is to make beginners learn how to navigate to applications and modules in ServiceNow, using the Application and Filter Navigators. To Create views and filters for a table list and to update record using online editing.

#introduction #ServiceNow #Courses #Free Training #Tutorials #Programming #Data Structure #Algorithms #Computer Science #Tips #Demos #ServiceNow Fundamentals #What is service now #Service now tutorial for beginners #Servicenow Online Tutorial #Service now introduction #Youtube

0 notes

jcmarchi · 7 months

Text

Answering billions of reporting queries each day with low latency

New Post has been published on https://thedigitalinsider.com/answering-billions-of-reporting-queries-each-day-with-low-latency/

Answering billions of reporting queries each day with low latency

Posted by Jagan Sankaranarayanan, Senior Staff Software Engineer, and Indrajit Roy, Head of Napa Product, Google

Google Ads infrastructure runs on an internal data warehouse called Napa. Billions of reporting queries, which power critical dashboards used by advertising clients to measure campaign performance, run on tables stored in Napa. These tables contain records of ads performance that are keyed using particular customers and the campaign identifiers with which they are associated. Keys are tokens that are used both to associate an ads record with a particular client and campaign (e.g., customer_id, campaign_id) and for efficient retrieval. A record contains dozens of keys, so clients use reporting queries to specify keys needed to filter the data to understand ads performance (e.g., by region, device and metrics such as clicks, etc.). What makes this problem challenging is that the data is skewed since queries require varying levels of effort to be answered and have stringent latency expectations. Specifically, some queries require the use of millions of records while others are answered with just a few.

To this end, in “Progressive Partitioning for Parallelized Query Execution in Napa”, presented at VLDB 2023, we describe how the Napa data warehouse determines the amount of machine resources needed to answer reporting queries while meeting strict latency targets. We introduce a new progressive query partitioning algorithm that can parallelize query execution in the presence of complex data skews to perform consistently well in a matter of a few milliseconds. Finally, we demonstrate how Napa allows Google Ads infrastructure to serve billions of queries every day.

Query processing challenges

When a client inputs a reporting query, the main challenge is to determine how to parallelize the query effectively. Napa’s parallelization technique breaks up the query into even sections that are equally distributed across available machines, which then process these in parallel to significantly reduce query latency. This is done by estimating the number of records associated with a specified key, and assigning more or less equal amounts of work to machines. However, this estimation is not perfect since reviewing all records would require the same effort as answering the query. A machine that processes significantly more than others would result in run-time skews and poor performance. Each machine also needs to have sufficient work since needless parallelism leads to underutilized infrastructure. Finally, parallelization has to be a per query decision that must be executed near-perfectly billions of times, or the query may miss the stringent latency requirements.

The reporting query example below extracts the records denoted by keys (i.e., customer_id and campaign_id) and then computes an aggregate (i.e., SUM(cost)) from an advertiser table. In this example the number of records is too large to process on a single machine, so Napa needs to use a subsequent key (e.g., adgroup_id) to further break up the collection of records so that equal distribution of work is achieved. It is important to note that at petabyte scale, the size of the data statistics needed for parallelization may be several terabytes. This means that the problem is not just about collecting enormous amounts of metadata, but also how it is managed.

SELECT customer_id, campaign_id, SUM(cost) FROM advertiser_table WHERE customer_id in (1, 7, ..., x ) AND campaign_id in (10, 20, ..., y) GROUP BY customer_id, campaign_id;

This reporting query example extracts records denoted by keys (i.e., customer_id and campaign_id) and then computes an aggregate (i.e., SUM(cost)) from an advertiser table. The query effort is determined by the keys’ included in the query. Keys belonging to clients with larger campaigns may touch millions of records since the data volume directly correlates with the size of the ads campaign. This disparity of matching records based on keys reflects the skewness in data, which makes query processing a challenging problem.

An effective solution minimizes the amount of metadata needed, focuses effort primarily on the skewed part of the key space to partition data efficiently, and works well within the allotted time. For example, if the query latency is a few hundred milliseconds, partitioning should take no longer than tens of milliseconds. Finally, a parallelization process should determine when it’s reached the best possible partitioning that considers query latency expectations. To this end, we have developed a progressive partitioning algorithm that we describe later in this article.

Managing the data deluge

Tables in Napa are constantly updated, so we use log-structured merge forests (LSM tree) to organize the deluge of table updates. LSM is a forest of sorted data that is temporally organized with a B-tree index to support efficient key lookup queries. B-trees store summary information of the sub-trees in a hierarchical manner. Each B-tree node records the number of entries present in each subtree, which aids in the parallelization of queries. LSM allows us to decouple the process of updating the tables from the mechanics of query serving in the sense that live queries go against a different version of the data, which is atomically updated once the next batch of ingest (called delta) has been fully prepared for querying.

The partitioning problem

The data partitioning problem in our context is that we have a massively large table that is represented as an LSM tree. In the figure below, Delta 1 and 2 each have their own B-tree, and together represent 70 records. Napa breaks the records into two pieces, and assigns each piece to a different machine. The problem becomes a partitioning problem of a forest of trees and requires a tree-traversal algorithm that can quickly split the trees into two equal parts.

To avoid visiting all the nodes of the tree, we introduce the concept of “good enough” partitioning. As we begin cutting and partitioning the tree into two parts, we maintain an estimate of how bad our current answer would be if we terminated the partitioning process at that instant. This is the yardstick of how close we are to the answer and is represented below by a total error margin of 40 (at this point of execution, the two pieces are expected to be between 15 and 35 records in size, the uncertainty adds up to 40). Each subsequent traversal step reduces the error estimate, and if the two pieces are approximately equal, it stops the partitioning process. This process continues until the desired error margin is reached, at which time we are guaranteed that the two pieces are more or less equal.

Progressive partitioning algorithm

Progressive partitioning encapsulates the notion of “good enough” in that it makes a series of moves to reduce the error estimate. The input is a set of B-trees and the goal is to cut the trees into pieces of more or less equal size. The algorithm traverses one of the trees (“drill down” in the figure) which results in a reduction of the error estimate. The algorithm is guided by statistics that are stored with each node of the tree so that it makes an informed set of moves at each step. The challenge here is to decide how to direct effort in the best possible way so that the error bound reduces quickly in the fewest possible steps. Progressive partitioning is conducive for our use-case since the longer the algorithm runs, the more equal the pieces become. It also means that if the algorithm is stopped at any point, one still gets good partitioning, where the quality corresponds to the time spent.

Prior work in this space uses a sampled table to drive the partitioning process, while the Napa approach uses a B-tree. As mentioned earlier, even just a sample from a petabyte table can be massive. A tree-based partitioning method can achieve partitioning much more efficiently than a sample-based approach, which does not use a tree organization of the sampled records. We compare progressive partitioning with an alternative approach, where sampling of the table at various resolutions (e.g., 1 record sample every 250 MB and so on) aids the partitioning of the query. Experimental results show the relative speedup from progressive partitioning for queries requiring varying numbers of machines. These results demonstrate that progressive partitioning is much faster than existing approaches and the speedup increases as the size of the query increases.

Conclusion

Napa’s progressive partitioning algorithm efficiently optimizes database queries, enabling Google Ads to serve client reporting queries billions of times each day. We note that tree traversal is a common technique that students in introductory computer science courses use, yet it also serves a critical use-case at Google. We hope that this article will inspire our readers, as it demonstrates how simple techniques and carefully designed data structures can be remarkably potent if used well. Check out the paper and a recent talk describing Napa to learn more.

Acknowledgements

This blog post describes a collaborative effort between Junichi Tatemura, Tao Zou, Jagan Sankaranarayanan, Yanlai Huang, Jim Chen, Yupu Zhang, Kevin Lai, Hao Zhang, Gokul Nath Babu Manoharan, Goetz Graefe, Divyakant Agrawal, Brad Adelberg, Shilpa Kolhar and Indrajit Roy.

0 notes

algotutor23 · 7 months

Text

Learn from the Best: Discover the Top Data Structures and Algorithms Course Online

In the fast-paced world of technology, understanding data structures and algorithms is a fundamental skill for any aspiring programmer or computer scientist. These concepts are the building blocks of computer science and play a crucial role in solving complex problems efficiently. Whether you’re a beginner or looking to brush up on your skills, there are numerous online courses available that can help you master data structures and algorithms. But how do you choose the best one? In this blog, we’ll explore some of the top data structures and algorithms courses available online, including AlgoTutor, so you can embark on your learning journey with confidence.

#Data Structures and Algorithms Course

0 notes

onetickcdc · 1 year

Text

#Best Data Structures and Algorithms Courses in Faridabad

0 notes

limatsoftsolutionsworld · 2 months

Text

Data structures and algorithms courses in Bangalore

Explore coding mastery with our Data Structures and Algorithms Courses in Bangalore. Guided by expert instructors, delve into essential concepts for software development excellence. Gain proficiency in algorithmic problem-solving and data structures, equipping yourself for success in the tech industry. Elevate your coding skills in the dynamic learning environment of Bangalore, a thriving tech hub.

Visit Us - https://www.limatsoftsolutions.co.in/data-structures-and-algorithms-course

Facebook - https://www.facebook.com/people/Li-Mat-Soft-Solutions-Pvt-Ltd/100063951045722/

Instagram - https://www.instagram.com/limatsoftsolutions/

LinkedIn - https://in.linkedin.com/company/limatsoftsolutions

Twitter - https://twitter.com/solutions_limat

Phone - +91 879 948 8096

#Data structures and algorithms courses in Bangalore #Data Structures and Algorithms Course #Digital Marketing Course Kanpur #Best DSA Course in c++#Summer Training Internship in Kolkata

0 notes

shailendra01 · 1 year

Text

https://www.worldranklist.com/preview/bookmarking/496309/Data-Structures-and-Algorithms-Certification-Training-ScholarHat

This course is meant for people with an interest in software engineering who want to advance their careers by landing an interview with one of the most prestigious organisations in the world, such as Microsoft, Adobe, Uber, Google, or Amazon. You will learn everything needed to succeed in these key interviews, from coding to problem-solving techniques, during this programme.

#data structures course #learn data structures and algorithms #data structures and algorithms course

0 notes

dokyeomini · 2 years

Text

gonna deal with this assignment tomorrow

#honestly i feel so out of place at this course #also i took the 'unofficial' prerequisite to this class before but dropped it bc it was too time consuming #and now im like .... i guess that's why i feel like im missing smth #it was algorithms and data structures

0 notes

blogpreetikatiyar · 2 years

Text

The spring framework is a flexible java framework for creating enterprise-grade applications that reduce the complexity of development. In March 2004, the spring framework was released for production for the first time, and the robust and open-source framework has gained immense popularity since then, so much so that it has been dubbed the "framework of frameworks". Spring is a loosely coupled Java application framework that is open-source. Spring is regarded as the inversion of the Java platform's control container due to its lightweight nature. We at Cipher schools is a best online learning platform.

The java spring framework is used by a large number of Java applications. The Java Enterprise Edition platform also has extensions that allow developers to build Web Applications. The Spring framework is currently used by Amazon, eBay, Netflix, and others. Spring also helps create secure, scalable, and robust web applications for businesses. The Spring framework consists of a number of sub frameworks, such as Spring Web Flow, Spring ORM, and Spring MVC. Kotlin and Groovy are also supported by Spring.

For the development of products, Spring Framework is a good choice when the requirement includes criteria such as dependency injection (DI), lightweight product structure, POJOs or plain old Java objects, AOP or aspect-oriented programming, unit testing, modular development, etc. A number of features in Spring Framework set it apart from other product development frameworks, including Spring web services, Spring test content framework, JDBC abstraction layer, Spring MVC framework, IoC container configuration management, and transaction management, among others.Spring framework is known for being non-invasive, meaning it doesn't force a developer to extend any predefined class or interface provided by Spring API one of its wonderful features. Struts, on the other hand, allow you to extend Action Class right? If the programmer uses the struts framework, he will be forced to extend the base class provided by the struts API

#python programming example #python for beginners #python programming for beginners #python tutorial for beginners #python for beginners notes #react js courses #google data structures course #google data structures and algorithms course

0 notes

rktiwari00-blog · 2 years

Photo

https://live.emancipation.co.in/s/store/courses/description/DSA---Live-Only-Free-Trial

#dsa #data structures #algorithmes #course #live course

1 note · View note

markcraft3636 · 6 months

Text

ServiceNow | What is Update Sets | Compare , Revert and Merge Update Sets | Complete Course

youtube

0 notes

techjedi · 2 years

Text

21-07-2022

What is Algorithm and its Uses and how to Use it? | TechJedi |

#Algorithm #Online courses #certification course #data structures

0 notes

scoonsalicious · 1 month

Text

Unwanted: Chapter 22, Untold - Pt. 1

Pairing: Bucky Barnes x Avenger!Fem!Reader

Summary: When your FWB relationship with your best friend Bucky Barnes turns into something more, you couldn’t be happier. That is, however, until a new Avenger sets her sights on your super soldier and he inadvertently breaks your heart. You take on a mission you might not be prepared for to put some distance between the two of you and open yourself up to past traumas. Too bad the only one who can help you heal is the one person you can no longer trust.

Warnings: (For this part only; see Story Masterlist for general Warnings) Language, alcohol consumption, strippers,

Word Count: 1.5k

Previously On...: Tony expressed his concerns about you going on this mission.

A/N: When Tony Met Pocket!

NOTE! The tag list is a fickle bitch, so I'm not really going to be dealing with it anymore. If you want to be notified when new story parts drop, please follow @scoonsaliciousupdates

Banner By: The absolutely amazing @mrsbuckybarnes1917!

Thank you to all those who have been reading; if you like what you've read, likes, comments, and reblogs give me life, and I truly appreciate them, and you!

Taglist: (Sadly, tag list is closed; Tumblr will not let me add anyone new. If you want to be notified when I update, please Follow me for Notifications!) @jmeelee @cazellen @mrsbuckybarnes1917 @blackhawkfanatic @buckybarnessimpp @hayjat @capswife @itsteambarnes @marygoddessofmischief @sebastians-love @learisa @lethallyprotected @rabbitrabbit12321 @buckybarnesandmarvel @fanfictiongirl77 @calwitch @fantasyfootballchampion @selella @jackiehollanderr @wintercrows @sashaisready @missvelvetsstuff @angelbabyyy99 @keylimebeag @maybefoxysouls @vicmc624 @j23r23 @wintercrows @crist1216 @cjand10 @pattiemac1@les-sel @dottirose @winterslove1917 @harperkenobi @ivet4 @casey1-2007 @mrsevans90 @steeph-aniie @bean-bean2000 @beanbagbitch @peachiestevie @wintrsoldrluvr @shadowzena43

Tumblr will not let me directly tag the following: @marcswife21 @erelierraceala @jupiter-107 @doublejeon @hiqhkey @unaxv @brookeleclerc

Boston, 2002

The bass inside the club was pounding, reverberating through the air and your skull as you made your way onto the floor. The day had already been unbearably long, and after your shift tonight, you still had a mountain of reading to do for your Introduction to Data Structures and Algorithms class. But, MIT courses didn’t come cheap, even at two classes a semester, and you needed every penny you could make from your shifts at Beantown Burlesque. It would make more sense, financially, to work a club closer to the college, but the idea of running into any of your classmates or, god forbid, your professors, made the extra time and money you spent commuting from Cambridge to inner Boston completely worth it.

Not that you expected a lot of tips tonight. It would have been better if you’d been scheduled to work the stage before they sent you to the floor; you were always requested for more lap dances after the patrons had seen you work the pole. You’d just have to work your ass off to entice a couple of lonely men into the VIP booth. But that always came with the additional task of fighting off requests for additional “services.” You may have been desperate for cash, but you were quite done with having your body sold for money, thank you.

You made your way over to the bar, hoping to get some intel on tonight’s patrons so you could shoot your best shot.

“How’s it goin’ tonight, Cherry Pie?” the bartender, Mac, asked, using the pseudonym you’d chosen for your stage name when you started at the club a year ago.

“No complaints yet, Mac,” you said, gratefully accepting the glass of water he offered you– it was important to stay hydrated, after all, “but then again, the night is very young.”

Mac let out a gruff laugh as he wiped down a glass. “You’re too young to be so cynical, Cherr,” he said.

You shrugged. That was an understatement. “Any good prospects tonight?” you asked, leaning your elbows on the bartop.

Mac nodded his chin toward a group of young men sitting close to the stage. “That group over there’s racked up a pretty big tab so far. Think they’re from the MIT alumni conference.” That piqued your interest. Beantown Burlesque might not be the ideal place to network, but you’d honestly take whatever you could get.

“They seem decent enough?” you asked Mac.

“About as decent as any group of blokes that come here,” he offered. “But they’ve been pretty respectful so far; no one’s tried to put hands anywhere they shouldn’t.”

“Good enough for me,” you told him. With a parting wave, you sauntered over to the group, making sure to put some extra sway in your hips. As you approached, you surveyed the collection of men. They all seemed to be centering their focus on one man in particular– he was dark haired with a goatee and wearing a pair of tinted glasses and looked vaguely familiar, though you couldn’t place where you might have seen him before. You clocked his expensive loafers and custom Armani suit, and the way the others around him laughed a little too loudly at what he was saying.

That’s the one, you thought to yourself. He had the money. If you were going to make your rent on time this month, he was the one you’d need to impress.

“You boys fancy some company tonight?” you asked once you approached the group. The man with the goatee leaned forward, a sure sign of interest, and looked at you over the lens of his glasses.

“Well, gorgeous,” he said with a smirk, “we're not ones to turn down an offer for good companionship, especially from someone as captivating as you. But let's be real, the question is whether you can keep up with us. Think you're ready for the challenge?”

Oh, this one was cocky. You could work with that. You trailed your fingertips along the tops of his shoulders as you made your way around to the table in front of him. Without breaking eye contact, you picked up the double shot of whiskey sitting there and downed the entire thing in one swig without flinching.

The other men in the group whooped and hollered at your display, but the man with the goatee just studied you with a peculiar look on his face. “What’s your name, sweetheart?” he asked.

“You can call me Cherry Pie,” you said as you began swaying your hips to the rhythm of the music coming through the speakers.

“I didn’t ask what they call you here,” he said, leaning back as you put your hands on his shoulder and began swaying in between his legs. “I asked for your name.”

“You haven’t spent nearly enough to earn that, honey,” you said as you gyrated.

The man laughed at that, then, reaching for his wallet, pulled out a handful of crisp, one hundred dollar bills. He gently tucked them into the waistband of your bottoms. “How’s that?”

You looked at the bills tucked into your underwear. By your guess, there was about eight hundred dollars there. You just might make rent, after all. “It’s a start,” you shrugged, beginning your tried and true lap dance routine.

One of the other men in the group let out a loud laugh. “She’s sure got your number, Stark!”

At the name, your eyes shot to the man with the goatee’s face, and it suddenly clicked for you. “Holy shit,” you breathed. “You’re Tony Stark.”

Stark smiled. “Guilty as charged, sweetheart.”

“Your company’s network security sucks ass,” you told him, the words coming out of your mouth before you could stop them.

He quirked an eyebrow at that. “Excuse me?”

Fuck. “Uh, nothing, sorry. Forget I said anything.” You put a renewed vigor back into your dance.

“Um, no.” Stark said, grasping your wrist firmly enough to encourage you to stop dancing, but gently enough to let you know he posed you no threat. “I want to hear how a stripper knows the faults of my network security.”

You blushed at that. “I, uh, may have broken in the back door and temporarily held your system hostage for ten minutes last May,” you confessed.

“That was you?” Stark exclaimed. If you didn’t know any better, you’d think he sounded… impressed. “You paralyzed our entire operation!”

“Yeah… sorry about that.” Well, you could kiss any further tips goodbye, that was for sure.

“Why’d you relinquish control back to us?” he asked. “You could have held it for ransom; we would have paid whatever you asked for.”

Huh. You had never even considered doing that. “Well, um, actually, I did it as part of a final project? For my Engineering Ethics and Professionalism course at MIT?”

Stark cocked his head at you. “With Erickson?” You nodded, and Stark actually laughed. “He still a narcissistic son of a bitch?”

You chuckled and nodded. “Sexist, too. He nearly shat a brick when he had to watch a mere girl bring a Fortune 500 company to its knees.”

Stark laughed, heartily. “I’ll bet he did! What I wouldn’t have given to see his face!”

“I set up a camera to record it,” you told him. “I can make you a copy of the VHS, if you want. I needed to capture the moment for posterity.”

From there, the atmosphere and your position in the group shifted. You were no longer the entertainment. Tony (he insisted you call him that) invited you to join him as his equal, and for the next several hours, he picked your brain, testing your knowledge and asking you questions about yourself, much to the displeasure of the rest of his group. One by one, they departed, until it was just the two of you. You were having the time of your life. You figured you’d never again have the opportunity to sit back and just hang out with such an icon of the tech community, and you were going to make the most of it. Now, here you were playing a game of Never Have I Ever.

“Never have I ever sheared a sheep,” Tony said with a grin.

“Why, Mr. Stark,” you said, bringing your glass to your lips (you failed to mention that, technically, you weren’t legally old enough to drink), “you haven’t truly lived until you’ve shorn the raw wool from an unwilling ewe.”

“You’re shitting me,” Tony said, laughing.

You took the glass from your lips without drinking. “You got me,” you told him. I grew up in Dayton. Not a whole lotta opportunities for sheep shearing there.”

A mischievous glint came into Tony’s eyes. “Your shift’s got to be almost over,” he said. “What do you say, Cherry Pie? Wanna go shear a sheep?”

“(Y/N),” you told him. “My name’s (Y/N), and I would fucking love to.”

<- Previous Part / Next Part ->

#bucky barnes x reader #bucky barnes x you #james bucky buchanan barnes #bucky barnes x y/n #bucky x you #bucky x reader #bucky fanfic #bucky barnes #bucky barnes fanfic #bucky x female reader #bucky barnes fic #bucky barnes fanfiction #james buchanan barnes #mcu bucky barnes #james barnes

155 notes · View notes