#SQL Query Optimization

[thedbahub]

Unraveling the Mystery: Why SQL Server Ignores Query Hints

Discover why SQL Server ignores query hints through practical T-SQL code examples and applications. Learn how to analyze and resolve ignored hints for optimized queries.

SQL Server query hints are powerful tools that allow developers to influence the optimizer’s behavior. However, sometimes these hints are unexpectedly ignored, leaving developers puzzled. In this article, we’ll explore practical T-SQL code examples and applications to understand why SQL Server might disregard a given query hint. Conflicting Query Hints One common reason for ignored query hints…

[rajaniesh]

Empower Data Analysis with Materialized Views in Databricks SQL

Envision a realm where your data is always ready for querying, with intricate queries stored in a format primed for swift retrieval and analysis. Picture a world where time is no longer a constraint, where data handling is both rapid and efficient.

[xaltius]

Unlocking the Power of Data: Essential Skills to Become a Data Scientist

In today's data-driven world, the demand for skilled data scientists is skyrocketing. These professionals are the key to transforming raw information into actionable insights, driving innovation and shaping business strategies. But what exactly does it take to become a data scientist? It's a multidisciplinary field, requiring a unique blend of technical prowess and analytical thinking. Let's break down the essential skills you'll need to embark on this exciting career path.

1. Strong Mathematical and Statistical Foundation:

At the heart of data science lies a deep understanding of mathematics and statistics. You'll need to grasp concepts like:

Linear Algebra and Calculus: Essential for understanding machine learning algorithms and optimizing models.

Probability and Statistics: Crucial for data analysis, hypothesis testing, and drawing meaningful conclusions from data.

2. Programming Proficiency (Python and/or R):

Data scientists are fluent in at least one, if not both, of the dominant programming languages in the field:

Python: Known for its readability and extensive libraries like Pandas, NumPy, Scikit-learn, and TensorFlow, making it ideal for data manipulation, analysis, and machine learning.

R: Specifically designed for statistical computing and graphics, R offers a rich ecosystem of packages for statistical modeling and visualization.

3. Data Wrangling and Preprocessing Skills:

Raw data is rarely clean and ready for analysis. A significant portion of a data scientist's time is spent on:

Data Cleaning: Handling missing values, outliers, and inconsistencies.

Data Transformation: Reshaping, merging, and aggregating data.

Feature Engineering: Creating new features from existing data to improve model performance.

4. Expertise in Databases and SQL:

Data often resides in databases. Proficiency in SQL (Structured Query Language) is essential for:

Extracting Data: Querying and retrieving data from various database systems.

Data Manipulation: Filtering, joining, and aggregating data within databases.

5. Machine Learning Mastery:

Machine learning is a core component of data science, enabling you to build models that learn from data and make predictions or classifications. Key areas include:

Supervised Learning: Regression, classification algorithms.

Unsupervised Learning: Clustering, dimensionality reduction.

Model Selection and Evaluation: Choosing the right algorithms and assessing their performance.

6. Data Visualization and Communication Skills:

Being able to effectively communicate your findings is just as important as the analysis itself. You'll need to:

Visualize Data: Create compelling charts and graphs to explore patterns and insights using libraries like Matplotlib, Seaborn (Python), or ggplot2 (R).

Tell Data Stories: Present your findings in a clear and concise manner that resonates with both technical and non-technical audiences.

7. Critical Thinking and Problem-Solving Abilities:

Data scientists are essentially problem solvers. You need to be able to:

Define Business Problems: Translate business challenges into data science questions.

Develop Analytical Frameworks: Structure your approach to solve complex problems.

Interpret Results: Draw meaningful conclusions and translate them into actionable recommendations.

8. Domain Knowledge (Optional but Highly Beneficial):

Having expertise in the specific industry or domain you're working in can give you a significant advantage. It helps you understand the context of the data and formulate more relevant questions.

9. Curiosity and a Growth Mindset:

The field of data science is constantly evolving. A genuine curiosity and a willingness to learn new technologies and techniques are crucial for long-term success.

10. Strong Communication and Collaboration Skills:

Data scientists often work in teams and need to collaborate effectively with engineers, business stakeholders, and other experts.

Kickstart Your Data Science Journey with Xaltius Academy's Data Science and AI Program:

Acquiring these skills can seem like a daunting task, but structured learning programs can provide a clear and effective path. Xaltius Academy's Data Science and AI Program is designed to equip you with the essential knowledge and practical experience to become a successful data scientist.

Key benefits of the program:

Comprehensive Curriculum: Covers all the core skills mentioned above, from foundational mathematics to advanced machine learning techniques.

Hands-on Projects: Provides practical experience working with real-world datasets and building a strong portfolio.

Expert Instructors: Learn from industry professionals with years of experience in data science and AI.

Career Support: Offers guidance and resources to help you launch your data science career.

Becoming a data scientist is a rewarding journey that blends technical expertise with analytical thinking. By focusing on developing these key skills and leveraging resources like Xaltius Academy's program, you can position yourself for a successful and impactful career in this in-demand field. The power of data is waiting to be unlocked – are you ready to take the challenge?

[himanitech]

Wielding Big Data Using PySpark

Introduction to PySpark

PySpark is the Python API for Apache Spark, a distributed computing framework designed to process large-scale data efficiently. It enables parallel data processing across multiple nodes, making it a powerful tool for handling massive datasets.

Why Use PySpark for Big Data?

Scalability: Works across clusters to process petabytes of data.

Speed: Uses in-memory computation to enhance performance.

Flexibility: Supports various data formats and integrates with other big data tools.

Ease of Use: Provides SQL-like querying and DataFrame operations for intuitive data handling.

Setting Up PySpark

To use PySpark, you need to install it and set up a Spark session. Once initialized, Spark allows users to read, process, and analyze large datasets.

Processing Data with PySpark

PySpark can handle different types of data sources such as CSV, JSON, Parquet, and databases. Once data is loaded, users can explore it by checking the schema, summary statistics, and unique values.

Common Data Processing Tasks

Viewing and summarizing datasets.

Handling missing values by dropping or replacing them.

Removing duplicate records.

Filtering, grouping, and sorting data for meaningful insights.

Transforming Data with PySpark

Data can be transformed using SQL-like queries or DataFrame operations. Users can:

Select specific columns for analysis.

Apply conditions to filter out unwanted records.

Group data to find patterns and trends.

Add new calculated columns based on existing data.

Optimizing Performance in PySpark

When working with big data, optimizing performance is crucial. Some strategies include:

Partitioning: Distributing data across multiple partitions for parallel processing.

Caching: Storing intermediate results in memory to speed up repeated computations.

Broadcast Joins: Optimizing joins by broadcasting smaller datasets to all nodes.

Machine Learning with PySpark

PySpark includes MLlib, a machine learning library for big data. It allows users to prepare data, apply machine learning models, and generate predictions. This is useful for tasks such as regression, classification, clustering, and recommendation systems.

Running PySpark on a Cluster

PySpark can run on a single machine or be deployed on a cluster using a distributed computing system like Hadoop YARN. This enables large-scale data processing with improved efficiency.

Conclusion

PySpark provides a powerful platform for handling big data efficiently. With its distributed computing capabilities, it allows users to clean, transform, and analyze large datasets while optimizing performance for scalability.

For Free Tutorials for Programming Languages Visit-https://www.tpointtech.com/

[carolunduke-04]

My Experience with Database Homework Help from DatabaseHomeworkHelp.com

As a student majoring in computer science, managing the workload can be daunting. One of the most challenging aspects of my coursework has been database management. Understanding the intricacies of SQL, ER diagrams, normalization, and other database concepts often left me overwhelmed. That was until I discovered Database Homework Help from DatabaseHomeworkHelp.com. This service has been a lifesaver, providing me with the support and guidance I needed to excel in my studies.

The Initial Struggle

When I first started my database course, I underestimated the complexity of the subject. I thought it would be as straightforward as other programming courses I had taken. However, as the semester progressed, I found myself struggling with assignments and projects. My grades were slipping, and my confidence was waning. I knew I needed help, but I wasn't sure where to turn.

I tried getting assistance from my professors during office hours, but with so many students needing help, the time available was limited. Study groups with classmates were somewhat helpful, but they often turned into social gatherings rather than focused study sessions. I needed a more reliable and structured form of support.

Discovering DatabaseHomeworkHelp.com

One evening, while frantically searching for online resources to understand an especially tricky ER diagram assignment, I stumbled upon DatabaseHomeworkHelp.com. The website promised expert help on a wide range of database topics, from basic queries to advanced database design and implementation. Skeptical but hopeful, I decided to give it a try. It turned out to be one of the best decisions I’ve made in my academic career.

First Impressions

The first thing that struck me about DatabaseHomeworkHelp.com was the user-friendly interface. The website was easy to navigate, and I quickly found the section where I could submit my assignment. The process was straightforward: I filled out a form detailing my assignment requirements, attached the relevant files, and specified the deadline.

Within a few hours, I received a response from one of their database experts. The communication was professional and reassuring. They asked a few clarifying questions to ensure they fully understood my needs, which gave me confidence that I was in good hands.

The Quality of Help

What impressed me the most was the quality of the assistance I received. The expert assigned to my task not only completed the assignment perfectly but also provided a detailed explanation of the solutions. This was incredibly helpful because it allowed me to understand the concepts rather than just submitting the work.

For example, in one of my assignments, I had to design a complex database schema. The expert not only provided a well-structured schema but also explained the reasoning behind each table and relationship. This level of detail helped me grasp the fundamental principles of database design, something I had been struggling with for weeks.

Learning and Improvement

With each assignment I submitted, I noticed a significant improvement in my understanding of database concepts. The experts at DatabaseHomeworkHelp.com were not just solving problems for me; they were teaching me how to solve them myself. They broke down complex topics into manageable parts and provided clear, concise explanations.

I particularly appreciated their help with SQL queries. Writing efficient and effective SQL queries was one of the areas I found most challenging. The expert guidance I received helped me understand how to approach query writing logically. They showed me how to optimize queries for better performance and how to avoid common pitfalls.

Timely Delivery

Another aspect that stood out was their commitment to deadlines. As a student, timely submission of assignments is crucial. DatabaseHomeworkHelp.com always delivered my assignments well before the deadline, giving me ample time to review the work and ask any follow-up questions. This reliability was a significant relief, especially during times when I had multiple assignments due simultaneously.

Customer Support

The customer support team at DatabaseHomeworkHelp.com deserves a special mention. They were available 24/7, and I never had to wait long for a response. Whether I had a question about the pricing, needed to clarify the assignment details, or required an update on the progress, the support team was always there to assist me promptly and courteously.

Affordable and Worth Every Penny

As a student, budget is always a concern. I was worried that professional homework help would be prohibitively expensive. However, I found the pricing at DatabaseHomeworkHelp.com to be reasonable and affordable. They offer different pricing plans based on the complexity and urgency of the assignment, making it accessible for students with varying budgets.

Moreover, considering the quality of help I received and the improvement in my grades, I can confidently say that their service is worth every penny. The value I got from their expert assistance far outweighed the cost.

A Lasting Impact

Thanks to DatabaseHomeworkHelp.com, my grades in the database course improved significantly. But beyond the grades, the most valuable takeaway has been the knowledge and confidence I gained. I now approach database assignments with a clearer understanding and a more structured method. This confidence has also positively impacted other areas of my studies, as I am less stressed and more organized.

Final Thoughts

If you're a student struggling with database management assignments, I highly recommend Database Homework Help from DatabaseHomeworkHelp.com. Their expert guidance, timely delivery, and excellent customer support can make a significant difference in your academic journey. They don’t just provide answers; they help you understand the material, which is crucial for long-term success.

In conclusion, my experience with DatabaseHomeworkHelp.com has been overwhelmingly positive. The support I received has not only helped me improve my grades but also enhanced my overall understanding of database concepts. I am grateful for their assistance and will undoubtedly continue to use their services as I progress through my computer science degree.

[uegub]

Why Tableau is Essential in Data Science: Transforming Raw Data into Insights

Data science is all about turning raw data into valuable insights. But numbers and statistics alone don’t tell the full story—they need to be visualized to make sense. That’s where Tableau comes in.

Tableau is a powerful tool that helps data scientists, analysts, and businesses see and understand data better. It simplifies complex datasets, making them interactive and easy to interpret. But with so many tools available, why is Tableau a must-have for data science? Let’s explore.

1. The Importance of Data Visualization in Data Science

Imagine you’re working with millions of data points from customer purchases, social media interactions, or financial transactions. Analyzing raw numbers manually would be overwhelming.

That’s why visualization is crucial in data science:

Identifies trends and patterns – Instead of sifting through spreadsheets, you can quickly spot trends in a visual format.

Makes complex data understandable – Graphs, heatmaps, and dashboards simplify the interpretation of large datasets.

Enhances decision-making – Stakeholders can easily grasp insights and make data-driven decisions faster.

Saves time and effort – Instead of writing lengthy reports, an interactive dashboard tells the story in seconds.

Without tools like Tableau, data science would be limited to experts who can code and run statistical models. With Tableau, insights become accessible to everyone—from data scientists to business executives.

2. Why Tableau Stands Out in Data Science

A. User-Friendly and Requires No Coding

One of the biggest advantages of Tableau is its drag-and-drop interface. Unlike Python or R, which require programming skills, Tableau allows users to create visualizations without writing a single line of code.

Even if you’re a beginner, you can:

✅ Upload data from multiple sources

✅ Create interactive dashboards in minutes

✅ Share insights with teams easily

This no-code approach makes Tableau ideal for both technical and non-technical professionals in data science.

B. Handles Large Datasets Efficiently

Data scientists often work with massive datasets—whether it’s financial transactions, customer behavior, or healthcare records. Traditional tools like Excel struggle with large volumes of data.

Tableau, on the other hand:

Can process millions of rows without slowing down

Optimizes performance using advanced data engine technology

Supports real-time data streaming for up-to-date analysis

This makes it a go-to tool for businesses that need fast, data-driven insights.

C. Connects with Multiple Data Sources

A major challenge in data science is bringing together data from different platforms. Tableau seamlessly integrates with a variety of sources, including:

Databases: MySQL, PostgreSQL, Microsoft SQL Server

Cloud platforms: AWS, Google BigQuery, Snowflake

Spreadsheets and APIs: Excel, Google Sheets, web-based data sources

This flexibility allows data scientists to combine datasets from multiple sources without needing complex SQL queries or scripts.

D. Real-Time Data Analysis

Industries like finance, healthcare, and e-commerce rely on real-time data to make quick decisions. Tableau’s live data connection allows users to:

Track stock market trends as they happen

Monitor website traffic and customer interactions in real time

Detect fraudulent transactions instantly

Instead of waiting for reports to be generated manually, Tableau delivers insights as events unfold.

E. Advanced Analytics Without Complexity

While Tableau is known for its visualizations, it also supports advanced analytics. You can:

Forecast trends based on historical data

Perform clustering and segmentation to identify patterns

Integrate with Python and R for machine learning and predictive modeling

This means data scientists can combine deep analytics with intuitive visualization, making Tableau a versatile tool.

3. How Tableau Helps Data Scientists in Real Life

Tableau has been adopted by the majority of industries to make data science more impactful and accessible. This is applied in the following real-life scenarios:

A. Analytics for Health Care

Tableau is deployed by hospitals and research institutions for the following purposes:

Monitor patient recovery rates and predict outbreaks of diseases

Analyze hospital occupancy and resource allocation

Identify trends in patient demographics and treatment results

B. Finance and Banking

Banks and investment firms rely on Tableau for the following purposes:

✅ Detect fraud by analyzing transaction patterns

✅ Track stock market fluctuations and make informed investment decisions

✅ Assess credit risk and loan performance

C. Marketing and Customer Insights

Companies use Tableau to:

✅ Track customer buying behavior and personalize recommendations

✅ Analyze social media engagement and campaign effectiveness

✅ Optimize ad spend by identifying high-performing channels

D. Retail and Supply Chain Management

Retailers leverage Tableau to:

✅ Forecast product demand and adjust inventory levels

✅ Identify regional sales trends and adjust marketing strategies

✅ Optimize supply chain logistics and reduce delivery delays

These applications show why Tableau is a must-have for data-driven decision-making.

4. Tableau vs. Other Data Visualization Tools

There are many visualization tools available, but Tableau consistently ranks as one of the best. Here’s why:

Tableau vs. Excel – Excel struggles with big data and lacks interactivity; Tableau handles large datasets effortlessly.

Tableau vs. Power BI – Power BI is great for Microsoft users, but Tableau offers more flexibility across different data sources.

Tableau vs. Python (Matplotlib, Seaborn) – Python libraries require coding skills, while Tableau simplifies visualization for all users.

This makes Tableau the go-to tool for both beginners and experienced professionals in data science.

5. Conclusion

Tableau has become an essential tool in data science because it simplifies data visualization, handles large datasets, and integrates seamlessly with various data sources. It enables professionals to analyze, interpret, and present data interactively, making insights accessible to everyone—from data scientists to business leaders.

If you’re looking to build a strong foundation in data science, learning Tableau is a smart career move. Many data science courses now include Tableau as a key skill, as companies increasingly demand professionals who can transform raw data into meaningful insights.

In a world where data is the driving force behind decision-making, Tableau ensures that the insights you uncover are not just accurate—but also clear, impactful, and easy to act upon.

[adhdnursegoat]

Episode 2

Word Count: 9.2k

Content Warning: none right now

Pairing: Edward Nashton X OC Romy Winslow

Setting: Pre-Arkham Origins; 2013

─── [ sequence: loading ] ───

Tuesday, December 18th, 2012

Something isn’t right.

Edward narrowed his eyes at the screen, the onyx and emerald glow casting hard shadows across his face, deepening the lines of ever-present ire. The dataset sprawled before him, tangled, disorganized, and inefficient—a perfect mirror of the Gotham City Police Department itself. 

For years, the GCPD’s reputation for sloppy documentation had been almost impressive in its own way, as if this endless mess were some grand tradition they upheld out of sheer spite for change. Crime logs scrawled hastily, half-formed incident reports lost in the shuffle of physical files, a scattering of disjointed data without a semblance of order or care. And now, all of it had fallen to him.

The so-called “cybercrime division” was practically a joke before he arrived, a name slapped on an old, cluttered storage room. Its single, flickering fluorescent light buzzed overhead like a dying insect; its lone, wheezing computer, so ancient it sounded like it was about to take off the first time he powered it on. It had taken him months to convince the precinct to let him install even basic equipment, months of tolerating the grinding fan and a monitor that crackled whenever he turned it on. He had even bought and collected his own equipment to help do their job for them.

But now, he had slowly, painstakingly transformed the place, pulling it from the brink of irrelevance.

He was the GCPD’s cybercrime division. And, if he were honest, he’d rather it be this way.

The first task had been nothing short of brutal, a punishment only someone as patient—or as obsessively thorough—as him could withstand. He had spent weeks, months even, combing through stacks of paper files that had yellowed with age, pulling arrest records, crime logs, and incident reports from years past, each entry a piece of Gotham’s history filed with indifference and half-hearted effort.

But that was just the beginning.

Once the data had been extracted and uploaded into a digital system, Edward moved to the next step: cleaning it. He combed through each entry, scrubbing it clean of mistakes, standardizing formats, deleting duplicates, and filling in the blanks left by years of neglect. It was an endless process, every correction a small battle against the chaos that had festered there long before his arrival. The work had been like sculpting—he chipped away at it, day by day, until the rough edges began to take shape.

With the groundwork set, he had turned his attention to the architecture itself. The system he was building would become Gotham’s digital skeleton, a structure capable of supporting and, eventually, predicting the city’s crimes. He designed SQL databases from the ground up, creating logical tables for every critical piece of data: incident types, time of day, locations, affiliations, every detail that could build a comprehensive picture of Gotham’s criminal underworld. Each table was linked, connected, and cross-referenced in ways that only he fully understood.

He wrote queries that could pull up crime histories, correlate locations, and flag patterns—all in the blink of an eye. Every inch of it had been optimized, refined, and customized, honed to be faster, sharper, and more intuitive than anything the department had ever seen. It was a framework only he knew how to navigate, the kind of code that would baffle even the most tech-savvy officer.

But this was Gotham.

Data alone wasn’t enough; the system needed security—a wall strong enough to withstand the city’s relentless forces. He had spent countless nights implementing layer upon layer of protection, configuring firewalls, building encryption protocols so complex that even he would struggle to undo them. Each file, each report, each encrypted string had become a piece of his fortress. He was transforming this forgotten room into a stronghold, its walls fortified against any threat that dared to infiltrate. Only he held the keys, and only he knew which locks he’d installed.

Then the real work had begun.

Once he had established a patent data flow in the system, he had started layering in more complex tools—predictive algorithms and crime prediction models that mapped Gotham’s streets like veins, arteries pulsing with the city’s crime. He had used regression analysis to find trends, drawing connections between crimes that no one else had even considered. He mapped crime incidents to temporal and spatial data, forming a pattern that gave him a lens into Gotham’s soul. 

But the GCPD couldn’t understand raw numbers—not the way he did. They needed visuals, pretty pictures, something digestible for their mushy minds. So he had built dashboards and reports, simple yet elegant, that displayed his work in colorful heat maps, time-series analyses, and relational charts. Even Gotham’s least tech-savvy officers could click through the data now, though they hardly knew what they were looking at. But Edward did. He could track hotspots, watch the swell of crime ebbing and flowing unlike anyone else.

Each day, as the system grew, he had refined it further. He ran diagnostics, tweaked scripts, and checked logs to ensure there were no breaches, no unexpected bugs. Every piece of data was backed up, replicated on secure servers, ready to be restored at a moment’s notice if Gotham’s chaos took a swipe at his work. And if it did, he would be prepared. Because this was more than a job; this was his creation, his legacy.

With every keystroke, every security protocol, every predictive model, he built a machine that made Gotham’s chaos readable, its patterns decipherable, and its secrets… well, not so secret.

Until a few days ago, his work had seemed routine—a necessary but unglamorous role. But then something unusual had caught his attention: a pattern in the officer response logs.

Every month, he reviewed the logs. It was a habit, part of his meticulous nature. Until recently, there had been nothing unexpected. But now, a repeated anomaly had begun to emerge. Certain neighborhoods showed response times that were curiously high, particularly in cases involving specific types of violent crimes—kidnappings, assaults, even homicides. In other areas, responses to similar crimes were fast, efficient, predictable. Yet, in these particular zones, it was as if time slowed.

He had noticed response times of fifteen, even twenty minutes, where they would typically average around five.

It was subtle, barely noticeable at first. Most people would have brushed it off as a glitch or user error. But Edward Nashton was not most people—and “user error” was not in his personal vocabulary.

“What if…” he muttered, pulling up a fresh SQL query and setting filters for crimes tagged as high-priority in those specific neighborhoods. His fingers flew across the keyboard as he added parameters, refining the search.

SELECT Neighborhood, AVG(Response_Time) AS Avg_Response 

FROM Incident_Reports 

WHERE Crime_Type = 'High-Priority' 

GROUP BY Neighborhood;

The query ran, and Edward leaned forward, his glasses catching the glow of the screen as rows of data populated in rapid succession. A comparison of average response times across all The data stared back at him, validating his suspicions. The averages for these neighborhoods were well outside the norm. Frowning, he created a quick bar chart to visualize the data, and there it was—a spike in response times, glaringly obvious, almost like a neon sign begging for someone to notice.

What’s more, the pattern seemed to correlate with the involvement of certain officers. He drilled down further, narrowing the logs to responses where these outlier times were recorded, and sure enough, the same handful of officers’ IDs kept appearing. At least three officers, in particular, showed up again and again, logged as the responding parties in incidents with suspiciously delayed responses:

Edison, James

Hartley, Jack

Murphy, Curtis

Edward leaned back, his lips twitching to the side in a faint sneer. Gotham’s filth didn’t just rest on its streets—it was deeply embedded within the very department meant to protect it. This pattern wasn’t accidental. The slow responses weren’t random errors; they were deliberate, selectively applied.

For the first time in months, Edward felt the rush of excitement he’d been craving since joining the GCPD. This wasn’t just data compilation or trend analysis anymore. He had uncovered something substantial, something buried, waiting to be unearthed. It wasn’t just about numbers; this was a deeper, darker game involving the very people entrusted with Gotham’s safety.

This wasn’t merely an inconsistency. It was corruption, plain and simple, hiding in the numbers. And if there was one thing Edward Nashton excelled at, it was peeling back layers to expose the truth lurking beneath.

The screen flickered faintly, his cursor hovering over rows of data as his mind picked apart the patterns, noticing every inconsistency, every shred of deception. This wasn’t an error or some accidental miscalculation. No, what he saw here was intentional—something deliberate and dark slipping under the radar, a clear thread of corruption woven into the fabric of Gotham’s police force.

If anyone could expose it, could tug at the threads until it unraveled into undeniable truth, it was him. The thought sent a thrill down his spine, a familiar surge of satisfaction that came with knowing he was on the verge of something significant.

Bing!

The sharp notification broke his concentration, dragging his attention to the corner of his monitor where an email preview appeared. Edward’s expression shifted, his lips pressing tight as he read the sender’s name: Commissioner Gillian B. Loeb. A scowl formed before he could stop it, his eyes narrowing behind his glasses. 

“come 2 my office”

The words glared at him. No punctuation, no capitalization—shorthand, as if Loeb couldn’t be bothered with even a semblance of respect. The sheer laziness grated on Edward, adding another layer to his already simmering disdain. Commissioner Loeb might as well have stomped down to his desk and demanded his presence with the same lack of decorum, and Edward doubted he would have been as irked. His lip curled, the faintest twitch of irritation betraying his thoughts.

Edward didn’t have friends here—never had. He didn’t linger by the watercooler, didn’t care for small talk, and had no interest in the routine camaraderie his coworkers indulged in. Loeb, however, wasn’t just a minor irritant like the rest. No, Loeb sat proudly at the top of a list of people Edward preferred to avoid—a list with its own special level of contempt reserved just for him. Loeb’s greed, his smug superiority, the way he flaunted his power as though it were untouchable—it all disgusted Edward. But he wasn’t foolish enough to ignore him.

He drew in a slow breath, pushing back the annoyance as he removed his glasses, his thumb and forefinger pressing firmly against the bridge of his nose. The tightness settling behind his eyes was familiar, a strain born from hours spent at the monitor. He rubbed at it, hoping to ease the creeping fatigue. Forcing himself to release a sigh, he closed his eyes briefly, letting the weight of the task at hand wash over him, clearing his thoughts.

Edward’s eyes flicked back to the fresh data on his screen, teeming with unspoken implications. He could go now, take this to Loeb, drop the details in his lap, and watch the Commissioner squirm. But… no. Not yet. If there was anything he’d learned, it was that timing was everything, and he wanted this case to be “pretty” and clean—undeniable.

With a quiet sigh, he finally pushed back from the desk, his legs and back groaning in protest. The human body wasn’t built for this kind of work, not the endless hours hunched over monitors and squinting at screens. He stretched, lifting his arms until he felt the crack in his shoulders, then rolled his neck, savoring the sharp pop that released some of the tension.

After a final look around his cramped, shadow-filled corner of the storage room, he made his way to the door. The space was dark and dank, with stacks of old case files and barely-functioning equipment shoved into every corner. He’d been asking for more space since the day he arrived, but as long as he remained the sole member of the “cybercrime division,” there was no point—not according to the people holding the budget. He could already imagine their dismissive words, the laughter as they shrugged him off. Why upgrade the closet for one man?

When he opened the door, a different kind of darkness hit him. GCPD’s main floor was lit by the harsh hue of fluorescent lights, casting an unnatural pallor over everything. The grime felt omnipresent, tinging every surface with a layer of wear that no amount of scrubbing could erase. The entire precinct pulsed like a spastic nerve, alive with chaotic energy.

He stepped out, crossing to the bustling bullpen. The layout was predictable—three levels stacked atop one another like a fortress of bureaucracy. A sublevel housed the detained. The main level, where he stood now, held the bullpen at its center, filled with two rows of desks paired off in clusters. Corridors stretched out on the east and west sides of the building, leading to file and evidence rooms, interrogation suites, and break areas.

Officers strolled by with coffee in hand, their conversations blending into the background noise. Detectives leaned against desks, swapping stories and laughing loud enough to be heard across the room. Secretaries rushed from one end of the bullpen to the other, arms stacked with paperwork or balancing phones against their shoulders. Above, the second and third levels housed offices for secretaries and various divisions, their windows glowing faintly in the overhead light.

And above it all, perched on the second-level landing like a throne, was the Commissioner’s office. It loomed over the precinct, a constant reminder of who held power there.

Edward shoved his hands into his pockets, his stride unfaltering, gaze fixed straight ahead. As he wove through the bustling bullpen, the familiar hum of GCPD’s endless chatter faded into a low buzz, a background noise he had long since learned to ignore. He didn’t belong here—not with these people, not with their idle gossip and endless banter. He was here to work, nothing more. And most of the time, they respected that, leaving him alone, unnoticed in the corners of the precinct.

“Dracula has risen!”

Most of the time.

Edward gritted his teeth, his jaw tightening as he caught the grating laughter ringing from behind him. He didn’t break stride, didn’t turn—just kept moving, his hands shoved deep into his pockets, shoulders hunched slightly as if to shield himself from the attention. Just keep moving. He had mastered the art of appearing unbothered, of letting these low-effort taunts roll off him. But Hartley’s voice, dripping with smug familiarity, broke through, just loud enough to draw the attention of a few nearby officers who exchanged knowing looks.

“Naaaashton!” the voice called, drawing out the syllables with exaggerated cheer, as if addressing an old friend. Edward could practically feel the man’s self-satisfied smirk boring into the back of his head. “I’m always surprised to see you out in the sun. More surprised when you don’t burn.”

It was the kind of comment he had grown used to, the small digs Hartley loved to throw his way whenever he passed by. Hartley, with his false bravado and ignorance parading as wit, never missed a chance to turn Edward into the precinct’s punchline.

Officer Jack Hartley—the poster boy of stereotypical “All-American” masculinity, with cobalt eyes and sandy hair, tall and built like he was carved out of an idealized gym catalog, complete with a bulky torso that fanned out into broad shoulders and arms that tapered down in a ‘V’ like an oversized Dorito. A man who would be lost without his badge to wave around and his flexed biceps, displaying that questionable tribal tattoo spiraling down one arm.

Edward kept moving, eyes trained straight ahead, but he allowed himself a sidelong glance, just enough to see Hartley’s smirk and the dumb faces around him. He could feel the heat of their attention, their eyes eagerly watching for his reaction. This time, he didn’t stay silent.

“Hartley,” he replied, his voice sharp and controlled. “I’m always surprised to see you haven’t been fired for your incompetence.”

There was a beat of silence. Edward didn’t stop to savor it, but he caught the reaction—the flicker of embarrassment in Hartley’s expression, the slight widening of his eyes before the scowl settled in. A few snickers rippled through the nearby officers, a sound that only deepened Hartley’s frown. His cheeks flushed slightly, the kind of reaction that Hartley, a man who considered himself untouchable, never expected to feel.

“Oh, you’re a real comedian, aren’t you, Nashton?” Hartley muttered, his voice barely audible now, laced with a gruff edge, the forced comeback of someone unprepared for a response.

Edward didn’t dignify it with another verbal reply. But, to answer the question— no. He wasn’t a comedian. He hated jokes. He only spoke truth. The words, the tiny prick of retaliation, had already done their work, striking just the right note to unsettle Hartley without so much as breaking his stride. He allowed himself to savor it for only a second, a brief and private victory that curled ever so slightly at the corner of his mouth. He knew it was minor, a passing exchange that no one would remember by the end of the day—but that small reminder, that assertion of his own superiority, was more than enough. For Edward, it wasn’t about showing off; it was about reminding himself, and everyone around him, that he was sharper, quicker, and not someone who could be so easily dismissed.

As he steadied his pace toward Loeb’s office, his thoughts drifted to the people around him, each one of them blending into the other like dumb lumps of flesh. Idiots—all of them. The entire precinct was an echo chamber of mediocrity, swollen with officers who took pride in their badges but lacked even a shred of real intellect. They sat at their desks, shuffling papers, swapping jokes, indulging in the hollow camaraderie of shared ignorance. They had no ambition, no hunger for knowledge, no desire to see past the routines they repeated day after day. They were just bodies filling space, a backdrop against which his mind and his skills blazed brighter by contrast.

Each step up the stairs only solidified his distaste. Every click of his shoes against the metal felt like a declaration, a rhythm that reminded him he was alone in a sea of self-satisfied drones. None of them measured up. None of them could measure up. Hartley’s lazy jeers, the way he flexed as if it made him someone important, the way he reveled in the pointless antics of the bullpen—these were the people tasked with keeping Gotham safe. It would have been laughable if it weren’t so tragic.

His eyes stayed fixed ahead, not sparing a single glance back at the bullpen. He had no reason to look, no interest in indulging the officers’ empty stares or their shared smirks. They were beneath him, irrelevant to his purpose, and the thought only strengthened his resolve as he approached Loeb’s office.

When he reached the landing, Edward straightened, pulling himself up to his full height, his fingers brushing over the door handle. He spared no glances to the bullpen below as he entered the Commissioner’s office and shut the door behind him with a soft click. 

The room was a display of power—ornate but garish, every detail chosen for intimidation rather than taste. Heavy mahogany furniture dominated the space, the Commissioner’s oversized desk an imposing centerpiece cluttered with papers and a gleaming nameplate. The walls were lined with plaques and framed commendations, their polished surfaces reflecting the faint light from a brass floor lamp in the corner. A thick, dark green carpet muffled Edward’s steps as he moved further inside, the smell of old leather and cigar smoke lingering in the air like a stain. Behind Loeb, floor-to-ceiling windows framed the grimy skyline of Gotham, their blinds half-drawn, letting in just enough gray light to make the space feel oppressive rather than bright. The office was a monument to its occupant’s ego—a fortress designed to remind anyone who entered exactly who held the power here.

The old man, standing at the windows, barely glanced over his shoulder to see Edward enter. “Sit.”

Edward frowned but did as he was told. Then he waited. And waited. And waited some more. Loeb’s stance, hands clasped firmly behind his back, suggested authority—or, more precisely, a performance of it. Edward couldn’t tell if the Commissioner was actually observing anything down on the street or merely pretending to do so, basking in his own bloated sense of importance. The stance, the imperious tone, the refusal to even acknowledge him face-to-face—every detail screamed a carefully curated aura of authority. Loeb stood as if by habit, a fossil of bureaucratic pomposity, clinging to a legacy of hollow power.

The man himself was almost a caricature, the embodiment of the department’s rot. His body strained against his uniform, seams puckered and pulled tight around his frame. The cap on his head dug visibly into his pallid skin, leaving an indentation along his brow, a mark of fluid retention only emphasized by the puffiness of his jowls. Loeb was thick-necked, with sagging skin that folded around his face in a way that resembled a bulldog’s. The clubbed fingers clasped at his back gave away years of heart strain, his slow circulation, and unchecked lifestyle, further evident in the labored rise and fall of his shoulders. He was an uncomfortable-looking man, like a worn-out relic forced into a role it no longer fit.

Edward glanced at his watch.

At last, the coot deigned to speak.

“Nashton,” the Commissioner quipped, “you’ll be getting a student.” His tone brooked no argument.

Gillian Loeb finally turned from the window, taking heavy, unhurried steps toward the desk, his movements sluggish, a body too tired to fully lift its feet from the floor. The scuffing of his shoes against the linoleum was maddeningly loud in the otherwise silent office, each step punctuated by his labored breath—a rasping sound that filled the room, making his presence that much harder to ignore. He reached his desk, his eyes narrowing just enough to convey irritation, perhaps at the exertion of moving across the room. With a relieved huff, he lowered himself into the worn red leather chair behind his desk, and it groaned under his weight, the sound of old leather and strained springs filling the air.

Edward resented being voluntold for anything, especially by a man who likely couldn’t navigate a basic search engine. But what choice did he have? Loeb’s words, dripping with condescension, only served to deepen Edward’s frown. He shifted in the stiff wooden chair opposite the Commissioner’s desk. He crossed his arms, fingers digging into his elbows as he suppressed the urge to roll his eyes. The impatience was barely masked—an edge to his expression that spoke volumes to anyone perceptive enough to notice. Loeb, of course, was not.

Then, the Commissioner began his speech, one that had likely been rehearsed, perhaps at his morning mirror. His voice rolled through the room, slow and full, each word dragging as he introduced the “exciting new work-study program.” Edward’s eyes flickered, resisting the urge to visibly wince as Loeb stressed the importance of “investing in someone’s future with the GCPD.” It was predictable, even painfully so, and Edward could practically see through Loeb’s words to the core of it: this so-called initiative was just a thinly veiled scheme, some tax break or budget cut disguised as a benefit to the community.

He was not naïve. He didn’t need the specifics to understand how the department operated. The GCPD’s funding, already stretched thin, had likely prompted this decision. The idea of a “program” that would cost them next to nothing while earning them goodwill with Gotham’s public was probably irresistible to the old bureaucrat. With students desperate for experience, the department could add another set of hands—hands they wouldn’t even have to pay. To Loeb, it was a flawless plan.

Edward’s leg bounced lightly as Loeb continued, the man oblivious to his impatience. Loeb droned on about the value of “real-world experience,” his words as empty as the promises they contained. Edward had read enough department memos and budget drafts to know the truth. This wasn’t about nurturing young talent or providing mentorship. It was about creating a self-serving “opportunity” that the GCPD could tout in press releases.

Loeb, meanwhile, was fully immersed in his monologue, clasping his hands as he expounded upon the program’s “benefits.” There was a look of smug satisfaction on his face, as if he were certain Edward should be grateful for the “honor” of mentoring this student. Edward could feel his jaw clenching, the tension in his arms building as he listened to the Commissioner pontificate about the duty of guiding someone who “could be the future of Gotham’s finest.”

Finally, Loeb paused, and Edward seized the chance to speak., his voice level, measured. “And this ‘student’ is supposed to assist me?”

“Yes, precisely.”

“I highly doubt they would be of any assistance, Commissioner.” Edward had a difficult time barring the condescension in his voice.

“You should be thankful.” Loeb narrowed his beady brown eyes at him. “Think of it as… additional help. Someone who can shoulder some of the workload.”

The Commissioner said it as if he were doing him a favor. Pfft. Edward knew better. He wasn’t being given a protégé; he was being saddled with an amateur who would inevitably fumble through tasks, leaving him to clean up the mess. More work—that’s what this was. The idea of a student trying to “help” in his field felt like a bad joke. He had spent a year refining his division—every system, every dataset was his creation. The thought of letting some kid handle even a fraction of it filled him with a quiet dread, like watching someone try to operate a complex machine without understanding a single gear.

Loeb shifted in his chair, taking Edward’s silence as agreement. “The youth these days, Nashton. They’re the future, and we have a duty to mold them. The department sees this as an investment. Someone to eventually join your endeavors full time.”

Edward’s jaw tightened. Investment? He couldn’t help but smirk slightly at the absurdity. Loeb had no real idea what Edward did, no real grasp of the complexity his work required. In Loeb’s mind, a student could simply step in and soak up skills like a sponge. But Edward knew better. To him, this wasn’t an investment; it was a hindrance, a risk of inefficiency, and the last thing he needed.

But with Loeb’s expectant gaze bearing down on him, he understood the futility of voicing his concerns. The decision had been made, probably long before he was even called into this office. He wasn’t being given a choice—he was being told to fall in line.

“We’ve got some candidates lined up. You narrow it down, and we’ll finalize it.”

Loeb pushed a stack of russet-colored folders toward him, and Edward suppressed a sigh as he unfurled his arms, grabbed the stack, and flipped open the first file. The pages were full of redacted lines—names, ages, and even genders all neatly blacked out. He rolled his eyes. There were pages of transcripts, an accompanying essay (which he was not going to read), academic achievements, extracurriculars, and sanitized letters of recommendation, none of which told him anything interesting.

Edward felt the familiar dull boredom creep in.

He eyed the first profile, scanning each line with a growing sense of irritation. Harvard, it read in bold letters, as if the word alone signified worth. Straight As, a laundry list of commendations from professors who probably barely knew this student beyond the name printed on their assignments. It was the kind of profile built from legacy admissions, expensive prep schools, and connections more valuable than skill. Every accolade, every honor felt manufactured, the result of privilege rather than grit or true intelligence. This was the sort of person whose future had been paid for, gift-wrapped, and delivered to them on a silver platter. A pawn that had been moved through life’s chessboard with no actual understanding of the game.

Edward flipped to the next file, another profile reeking of the same glossy, untarnished perfection: a prestigious background, impeccable grades, extracurriculars that spoke more to showmanship than substance. His lip curled, an almost imperceptible twist of disdain. What use was someone like this to him? He didn’t need another pre-packaged prodigy, the type who had been endlessly praised but never challenged, the kind who breezed through academia without ever truly understanding what it meant to think, to analyze, to push limits. He needed someone who had actually had to work for something, who had seen struggle, who understood what it meant to build something from scratch—someone with the kind of determination that couldn’t be bought.

These files in front of him represented everything he despised about the world: the hollow merit of titles, the pretense of excellence. It was the kind of privilege that relied on appearances rather than substance, and it left a sour taste in his mouth. He flipped through each one with growing impatience, each page a carbon copy of the last, all polished to an empty sheen that hid any real substance.

His gaze sharpened as he closed another file. What he wanted, if he was to have an assistant, was someone with actual mettle. Someone with grit, someone who hadn’t had everything handed to them. The kind of candidate who could be taught something beyond the regurgitated lessons of privilege. Edward’s jaw tightened as he tossed the files back onto the desk before grabbing another file near the bottom of the stack.

When he opened this one, he cocked a brow. Something caught his eye.

There was an entry—a two-month juvenile record attached to a high school transcript from their junior year. Edward’s interest piqued immediately. He leaned back in the chair, letting the file rest in his fingers as he read the details. The record noted a hacking incident: unauthorized access to school servers to alter grades. He almost chuckled, finding this much more intriguing than the immaculate résumés of Ivy League candidates.

The report stated they had felt their grades were given unfairly and decided to take matters into their own hands. It was an act of rebellion, yes, but also one of precision and calculation. They hadn’t sabotaged the system—they had simply revised their grades without damaging any other records or erasing traces of the hack. There was a comment from a principal decrying the act as undermining the school’s “integrity” and a record of a lengthy expulsion hearing. Yet, despite this incident, there were a handful of letters from teachers who seemed reluctant to give up on them.

He read further, finding notes on their turnaround at their senior year and at Gotham City Community College. After high school, it seemed no other institution had wanted to take a chance on them, except for this one. But instead of coasting through, they had thrived—joining the debate team, earning honors, and eventually transferring to Gotham University. Now they were a college senior majoring in computer science with a minor in criminal justice.

As he skimmed through the final notes, Edward smirked. This work-study tied directly into their capstone project—a predictive AI programmed to determine when and where crimes were more likely to occur. It was a smart move, one that showed ambition and resilience. They were not another cookie-cutter success story from an Ivy League—they were someone who had clawed their way out of a mess, took risks, and kept climbing. Whoever they were, they were far more intriguing than the other candidates. He didn’t need some entitled, bougie fraternity brat who would think they were smarter than him.

He closed the file with a soft pat, already deciding. He flicked it onto the desk with an air of indifference and slid to a stop in front of Loeb. “This one,” he said flatly.

The Commissioner picked up the folder, his thick fingers fumbling with the dry edges as he peeled it open. His brow furrowed deeper as he read, and he shot Edward a wary look over the papers. “This one? The one with the juvie record? Are you sure?”

Edward’s expression remained cool, detached. “It’s either this one or none at all,” he replied without missing a beat.

Loeb stared at him for a moment, rubbing his jaw, clearly weighing his options. After a long pause, he sighed and tossed the file back on the desk with a resigned grunt. “Fine,” he muttered. “They’ll be here after the holidays.”

─── [ sequence: loading ] ───

In under a month’s time, Edward Nashton found himself caught off guard.

It was not often he was caught off guard, and he did not like it.

He was hunched over his workstation, eyes narrowed as he sifted through lines of encrypted data. It was after lunch, during which he had remained in his space, still working, forgoing eating as he normally did. His office, if one could call it that, was a windowless space in a back corner of the GCPD headquarters, dimly lit and reeking of stale coffee and burnt-out ambition. It was crammed with outdated computers and stacks of scattered papers, the sort of place where Edward thrived in isolation. He was so absorbed in his task that when the door opened and a knock sounded on the doorframe, he muttered, “Yes?” without looking up, already bracing himself for another mundane IT request—misguided souls thinking that the "computer guy" could fix the printer.

But then an unfamiliar voice responded.

“Excuse me? Are you Mr. Edward Nashton?”

It was not the tone he expected—there was no hint of impatience or condescension, which he had grown accustomed to when people sought him out. The voice was feminine, with an even pitch, its calm, smokey cadence infiltrating the monotony of his work. It was an unobtrusive sound, yet so unusual to his ears that he was compelled to see who it belonged to. He looked up. He froze.

A girl was standing at the doorway, her fingers resting lightly on the doorframe as if unsure whether to fully step inside. He had not even heard the door open.

Edward frowned.

His first impression of her was one of dissonance—a sharp, almost unsettling contrast between her and the office she had just entered. The grimy, worn-down precinct felt even darker with her in it, as if the dingy fluorescent lights themselves were suddenly more aware of their inadequacy.

She was beautiful—irritatingly so. Her long, sleek dark hair fell like silk curtains, parted perfectly down the middle, framing her face with an effortless elegance that didn’t belong anywhere near the GCPD. Her eyes, lined meticulously with dark, precise wings, were fixed on him with a hint of amusement. There was a different energy to her, one that felt deliberate, almost as though she knew exactly how out of place she looked and was inviting him to react. He barely realized how long he held her gaze.

With a faint scowl, he forced himself to look away, taking in the rest of her with a detached, analytical eye. Her lavender blazer dress caught what little light there was, gold buttons glinting as they drew a subtle line down her figure. The hem stopped just short of professional modesty, skirting the edge of propriety with a cut that was as tailored as it was daring. She had a designer bag slung over her shoulder, a fuzzy purple notebook and a gray-and-pink plaid winter coat clutched in the same hand, and she was only one chihuahua short of being GCPD’s own Elle Woods.

This office hadn’t seen anything like her, and by the looks of it, she was fully aware of that fact. For a moment, he wondered if she was mocking the precinct in her own way, challenging the drab confines of the facility with something so polished, so perfectly styled. 

His thoughts were cut short by the sound of her clearing her throat, and his eyes snapped back to hers. He realized with sudden embarrassment that she had caught him staring. Worse, she was smirking—her lips shiny and curved in an almost mocking acknowledgment of his mistake.

“Yes,” he said stiffly, clearing his own throat in a failed attempt to reestablish control. “And who might you be?”

“I’m your student, Romy. Romy Winslow.”  Her half-lidded eyes seemed to smolder in the low lighting.

“Student?” Edward repeated, the word coming out more as a question than he intended.

“Yeah,” she nodded. “Like, they told you, right?”

“Of course,” Edward grumbled, scrambling to regain some semblance of authority. He wasn’t used to feeling unprepared, especially not in his own domain.

He did not like when Romy pursed her shiny lips and narrowed her eyes. “You forgot, didn’t you?” she pressed, a teasing lilt to her voice.

Edward’s back straightened, jaw tightening. “You will soon find that I forget nothing, girl,” he quipped. “I’m merely intrigued by your—” he gestured vaguely at her—“appearance. Are you sure your silly little head didn’t get confused? Got lost on your way to a sorority luncheon?”

Romy blinked. She checked her smartwatch, then looked back at him and tilted her head, the innocent confusion in her eyes seeming a little too thoughtful to be genuine. “No… The Greek Meet isn’t until Saturday.”

He frowned.

Oh, she was definitely fucking with him.

Soon, her pink lips pursed in a slight pout, and she glanced down at herself. “Is it too much?”

As she turned to the side, Romy casually modeled her silhouette, the lavender fabric clinging to her form in a way that was both tasteful and tantalizing. The movement drew Edward’s attention, his gaze instinctively tracing her figure. He couldn’t help but follow the curve of her form, from her shoulders that tapered elegantly down to the delicate arch of her spine, and finally to her shapely backside, perfectly showcased by the tailored fit of the dress. He resented that his gaze followed the lines of her legs, made even longer by the gray knee-high, heeled boots she had chosen.  Each line was accentuated with precision.

She caught his eye again, her expression playful yet somehow earnest. “I thought it was just the right amount of business meets pleasure.”

Edward cleared his throat. “Not quite what I was talking about,” he muttered, his gaze darting away in an attempt to collect his thoughts.

“What did you mean then?” Romy asked as she stepped further into the room. She glanced around, her nose wrinkling slightly at the sight of the meticulously stacked boxes of files, outdated monitors, and blinking fluorescent lights. “This is the GCPD Cybercrime Division?” she asked in an offhand manner. “This looks very—” she wriggled her fingers at the general space “—humble.” Though she smiled, it was clear she was struggling to be polite.

“I mean that I did not expect someone so— soft.” He glanced around the area, grimacing at the— as she called it—‘humble’ surroundings. “It is what it is.”

“You mean you didn’t expect a girl?”

“Yes,” he admitted, refusing to dance around it.

“Well,” she said with a shrug, “guess we both had false expectations of the situation, Mr. Nashton.”

Edward felt the frustration building, both at himself and at Romy’s unsettling confidence. “And what exactly did you expect?” he retorted, his eyebrow cocking. “Quantico?”

She smirked, but the movement was subtle, a brief twitch at the corner of her lips. “No.” Her fingers traced over the edge of a dusty computer monitor, her almond-shaped nails—a soft mint green—making the action seem delicate. “But, like,  maybe I expected something a little more contemporary than this, I suppose.”

He bristled at the unintentional insult to his sanctuary of cobbled-together tech that he had spent the better part of a year collecting to upgrade this dump. He found himself oddly off-balance, grappling with the realization that he had expected someone completely different. Someone less refined, more—unpolished. But here she was, her demeanor perfectly maintained in a lavender blazer dress, with the confidence of someone used to catching others off guard.

He did not like it. He did not like how she acted. He did not like how she talked. He did not like what she said. He did not like how she looked. He did not like her.

Edward sat behind his uncluttered desk, arms folded as he leaned back in his creaky chair, eyes narrowing at her. “The GCPD still does not see the full benefit of a cybercrime division,” he said, his voice laced with a bitterness that hinted at more than just professional frustration. He was used to his work being sidelined, his expertise disregarded by those who should know better. Her arrival was yet another inconvenience in a long line of offenses. “These bald apes are content to remain in the twentieth century.”

Trailing closer, she soon sat in a nearby chair, setting her belongings on a table crowded with equipment. “Quite the shame,” she replied, crossing one leg over the other as she settled into the seat he did not offer her to sit in. “I was hoping to gain some valuable expertise before graduating. I wanted to work here in fact.” There’s a glimmer of amusement in her eyes and her voice holds a polite, measured tone.  “My professors said you are brilliant.”

Smug satisfaction settled in his chest. 

“I am.” Edward’s lip curled ever so slightly, and he straightened, giving her a half-lidded look. 

Romy looked at him for a moment before speaking. “They said you were difficult too.”

“Who’s they?’”

“Duncan and Hadley.”

Edward’s eyes narrowed at the mention of his old professors, the faint smugness that had crept into his expression now sharpening into something colder, more cutting. He studied her with a slow, deliberate gaze. This close, he can finally see her eyes—a moss green

“Duncan and Hadley,” he repeated, his tone laced with disdain. “Duncan—let me guess—still regurgitating decades-old theories as if they’re groundbreaking revelations? And Hadley…” He sneered faintly, his lip curling. “Hadley’s what happens when tenure protects the incompetent. Is he still using Windows XP?”

“Unfortunately… They had strong opinions about you as well,” Romy remarked lightly, looking at her nails in an absent minded manner.

“I’m sure they did,” Edward replied smoothly, sitting forward now, his elbows resting on his desk as he leveled her with a pointed look. “Professors like them always do when confronted with someone who doesn’t just color outside their precious lines but redraws the entire picture. Of course, to them, that’s ‘difficult.’”

Her lips quirked at one side and she rested her chin on her hand, watching him with an amused air. “Then it seems I made the right decision to come to you.”

“While it would undoubtedly be an honor for you to work with someone of my genius firsthand,” Edward continued, his voice dripping with confidence as he narrowed his gaze at her, “you won’t stand a chance.”

Romy merely tilted her head, watching him with an expression of calm intrigue, seemingly unbothered by the sharp bite of his words. It unnerved him more than he cared to admit. He wasn’t used to this feeling, least of all in his own space.

“I’m used to people underestimating me, Mr. Nashton.”

“My estimations are always accurate,” he continued, his voice sharper now. He sighed giving her a bored look. “Let’s cut to it, I suppose.” He let one of his hands rest on the desk. “You will only get in my way. I don’t want to waste my time or my breath educating you on something that will likely go in one ear and out the other.” He tapped his fingers against the tabletop in a measured way, his voice cold. “You are to sit, stay, and not move. Don’t touch anything else. You can watch, and maybe, just maybe , you might be graced with a touch of my intellect... One would only be so lucky to have someone of my caliber rub off on them.”

Before Romy responded, there was a slight twitch of her perfectly plucked brow. “... Do you like to rub off on people, Mr. Nashton?”

He blinked, absorbing what she had just said. Rub off, he thought dryly. Clever, very clever. But what really stopped him wasn’t the phrasing; it was the look in her eyes—a knowing, steady gaze that held him longer than it should. There was a flicker of challenge there, of cool confidence, that made him shift in his seat, uncomfortable under the weight of that steady, unflinching stare.

“You know exactly what I mean, girl,” Edward snapped. He fixed Romy with a squint. “I can see you are going to be quite the pain in my ass, aren’t you?”

Romy’s lips twitched as she considered him with sharp eyes. “Oh, no, not at all,” she lilted. “I’m actually trying to make a good impression.”

He watched as she relaxed her slender hands on the arms of the chair, mint green nails clicking once on the wood. Then, when she crossed her legs, it was a slow movement. His attention flicked to her shapely thighs, noting how the lavender hem of her dress raised slightly with the movement. His frown deepened, brows knitting together, and then he looked back at her easy gaze.

“And how do you plan on doing that?” he asked.

Her eyes flicked across his face, and she hummed thoughtfully, obviously thinking about her answer. Then, a slow smirk stretched across her shiny, plush lips, and those young eyes of hers glittered with amusement. She clicked her tongue. “By being quiet, submissive, and obedient…”

Immediately, Edward felt the heat rise, an unbidden flush creeping up his neck and settling under his collar. He resented it, and his jaw tightened in frustration. She leaned back in the chair, her lips curling into that slow, deliberate smirk, and something glittered in her gaze. The subtle bite to her lip—did she even realize she was doing it?—and the way she settled back, so at ease, as if she were testing him, watching to see how he’d react. It was maddening. There was no reason to let a stranger, much less a student, get under his skin.

He kept his tone even, measured. “I have a hard time believing that,” he said with forced calm. “You are already disrupting my workflow by being here. I don’t have the time or interest to indulge anyone’s… antics.”

“Antics?” Romy repeated. “So, like, you assume I’m here to waste your time? That I won’t take this seriously?”

Edward smirked. “Well, if it looks like a duck and talks like a duck,” he chided, not at all masking the disdain in his voice.

Her smile sharpened. “Except when it’s a unicorn,” she simpered, lashes fluttering as she peered at him through half-lidded eyes. “Is that it, Mr. Nashton? Is it because I’m not some acne-riddled, snot-nose, basement incel?” She tilted her head to the side, her long black hair shifting with the movement, and she narrowed her gaze. “Is it because I’m pretty… ?”

The question struck him off balance. He realized he’d been observing every inch of her carefully put-together appearance, struggling to reconcile it with the notion that Commissioner Loeb thought it fit to place her here with him. But Loeb had been unaware of the candidates as well. The disconnect irritated him, the softness of her expression and the sharpness of her words stirring something hot in his chest.

“Listen, little girl,” he sneered, mustering every ounce of cold detachment, “I don’t know what game you’re trying to play, but I’m not the one to challenge.”

Romy’s smile widened, the look in her eyes unmistakably daring. “Oh, I don’t know about that,” she said, letting her voice dip playfully. “You seem like exactly the kind of man to enjoy a good challenge.” She tapped a nail thoughtfully on the wooden chair arm. “Or am I wrong?”

“Challenges are acceptable,” Edward said, his lips twitching as though considering a smile, though his gaze remained guarded. “But only those that actually require intellect. Challenges that flex the mind… not distractions.”

“So, that’s what you see me as? A distraction?” Romy tilted her chin up, looking at him with that gaze that made her look so cool. It only grated on his nerves. “I’ll make sure to cover my shoulders and hide my bra straps then.”

Edward’s eyes narrowed. He opened his mouth to retort, but she was faster, leaning in with a look that was half-sweet, half-mischievous. “Unless, of course…” she purred, “a little distraction is exactly what you need. Maybe it would loosen you up.”

“Loosen up?” he echoed, his voice edged with forced calm. “I don’t need to loosen up. I need focus and productivity, two qualities I have a hard time believing you possess.”

“I have plenty of focus.” She settled back in her chair, unabashedly grinning at his obvious discomfort. “I’m sure we’ll make a… productive team, Mr. Nashton.”

He exhaled slowly, trying to maintain his composure. “You’re insufferably confident, aren’t you?”

“Pot meet kettle,” she replied breezily, gesturing in a casual manner, clearly unbothered by his barbs. “So… are you ready to be impressed, or are we going to keep up the foreplay?”

Edward rolled his eyes then shifted and spun back to his computer. “ Fine,” he said tightly. “You want to prove yourself? Then start by doing exactly what I tell you, without the smart commentary, Ms. Winslow.” He made movements to bring up his work, his fingers tapping away at the keyboard.

She shifted to the side, her eyes gleaming with a playful challenge as she retrieved a sleek laptop from her purse. “Yes, Mr. Nashton, sir.”

His fingers stalled over the keyboard, his usual fluidity momentarily broken. A shiver ran down his spine, slithering low. It made him grit his teeth.

With a deep inhale and an exasperated sigh, he settled into his work, typing with the familiar, precise rhythm he was known for. While he maintained perfect focus, he couldn’t shake the uncomfortable feeling of having someone in his space. He worked alone. He had never had to precept anyone. He was not a teacher. He didn’t have the patience nor the desire for it. Professors had tried setting him up to tutor during his time in college—it hadn’t worked out as they thought it would. It had taken only one time to make someone cry for them to decide teamwork might not be something for him.

He felt it inevitable: Romy would say something completely idiotic; he would correct her; it would hurt her puny little feelings; she would cry; she would quit; and he would never have to hear from her again.

All he had to do was bide his time. He could be patient… when he wanted to be.

But, as much as it stung to admit, Romy surprised him. She was quiet—perfectly quiet, almost too quiet—and she seemed wholly absorbed in what he was doing. It was almost like she didn’t exist.

The minutes stretched, long and quiet, with nothing but the soft hum of computers and the steady beat of typing filling the air. Twenty minutes slipped into thirty, and then an hour, and still, she remained there, intently focused. The steadiness of her gaze as it flickered between her screen, his screen, and his hands—the unwavering attention she devoted to each click, each keystroke—was almost unnerving. There was something in the way she was present, so completely engaged, that felt oddly invasive. And yet, she wasn’t disruptive. She didn’t give any more snarky quips. She didn’t sigh in boredom. She didn’t ask questions or interrupt with idle conversation, simply watching, occasionally typing, the rhythm of her own keystrokes echoing his in a strange, synchronized cadence.

But it was the sound of her nails that really got to him. Each click of the keys under her fingers was punctuated by the sharper snap of those mint-colored acrylics atop them, a sound somehow distinct from the natural clack of a keyboard. It wasn’t irritating—not yet—but he sensed the potential. It was the kind of sound that, over time, could likely chip away at his concentration, like Chinese water torture, each click burrowing into his awareness with grating persistence.

Every now and then, Edward risked a glance at Romy, expecting to catch her on her phone or zoned out, ready to dismiss the task at hand. But she stayed. She was observant, her posture straight, fingers poised and ready, and she took in every word, every glance he spared her, without saying a thing—only a simple nod here and there in respectful acknowledgment. 

The hours slipped by faster than usual, her silence still unbroken. Edward leaned back, cracking his knuckles and flexing his fingers, savoring the temporary reprieve. But as he shifted, his eyes caught movement—Romy, standing right in front of his desk.

He jolted, a sharp intake of breath betraying his surprise. He hadn’t even heard her move.

“ What?” he snapped, his voice tight. “What do you want, girl?”

She blinked, glancing at her watch with maddening calm. “Time to go home.”

It was only then that he noticed the bag slung over her arm and the paper she was holding out. He scowled, snatching it briskly, his lips pulling into a tight, displeased line. A time log. Of course. With a resigned sigh, he grabbed his pen and scribbled his name and initials before shoving it back at her.

She glanced down at the sheet and grimaced. “You have terrible handwriting.”

“Get out,” he gritted, his flat look doing nothing to mask his irritation. He didn’t need her critique on top of everything else.

“Alright. See you tomorrow, Mr. Nashton,” she chuckled, her tone airy, carrying that infuriating undercurrent of amusement, as though his opinion of her couldn’t matter less. Then she spun on her heel and tossed a languid wave over her shoulder, twiddling her mint-colored acrylics.

“Unfortunately.”

Then, the door clicked shut behind her, leaving the office mercifully quiet and empty. Edward leaned back in his chair. Finally, he had his silence. But it wasn’t the victory he’d hoped for.

His gaze flicked toward the empty chair she’d occupied, a faint scowl tugging at the corners of his mouth. This was only the beginning. She’d be back tomorrow, and the day after that, and every Wednesday, Thursday, and Friday after that until the semester ended.

Edward’s jaw tightened at the thought, the weight of it pressing down on him like a slowly closing trap. She wasn’t just a nuisance; she was a disruption, a thorn in his side he couldn’t pull out, no matter how much he wanted.

Fifteen weeks and two days of this. Of her.

With a sharp exhale, he turned back to his monitors, forcing his attention onto the scrolling lines of data. He didn’t have time to dwell on irritations. He had work to do, and she was gone for the day. That was enough.

It would have to be.

[techfinna]

Top 5 Selling Odoo Modules.

In the dynamic world of business, having the right tools can make all the difference. For Odoo users, certain modules stand out for their ability to enhance data management and operations. To optimize your Odoo implementation and leverage its full potential. 

That's where Odoo ERP can be a life savior for your business. This comprehensive solution integrates various functions into one centralized platform, tailor-made for the digital economy. 

Let’s drive into 5 top selling module that can revolutionize your Odoo experience:

Dashboard Ninja with AI, Odoo Power BI connector, Looker studio connector, Google sheets connector, and Odoo data model.

1. Dashboard Ninja with AI: 

Using this module, Create amazing reports with the powerful and smart Odoo Dashboard ninja app for Odoo. See your business from a 360-degree angle with an interactive, and beautiful dashboard.

Some Key Features:

Real-time streaming Dashboard

Advanced data filter

Create charts from Excel and CSV file

Fluid and flexible layout

Download Dashboards items

This module gives you AI suggestions for improving your operational efficiencies.

2. Odoo Power BI Connector:

This module provides a direct connection between Odoo and Power BI Desktop, a Powerful data visualization tool.

Some Key features:

Secure token-based connection.

Proper schema and data type handling.

Fetch custom tables from Odoo.

Real-time data updates.

With Power BI, you can make informed decisions based on real-time data analysis and visualization.

3. Odoo Data Model: 

The Odoo Data Model is the backbone of the entire system. It defines how your data is stored, structured, and related within the application.

Key Features:

Relations & fields: Developers can easily find relations ( one-to-many, many-to-many and many-to-one) and defining fields (columns) between data tables. 

Object Relational mapping: Odoo ORM allows developers to define models (classes) that map to database tables.

The module allows you to use SQL query extensions and download data in Excel  Sheets.

4. Google Sheet Connector:

This connector bridges the gap between Odoo and Google Sheets.

Some Key features:

Real-time data synchronization and transfer between Odoo and Spreadsheet.

One-time setup, No need to wrestle with API’s.

Transfer multiple tables swiftly.

Helped your team’s workflow by making Odoo data accessible in a sheet format.

5.  Odoo Looker Studio Connector:

Looker studio connector by Techfinna easily integrates Odoo data with Looker, a powerful data analytics and visualization platform.

Some Key Features:

Directly integrate Odoo data to Looker Studio with just a few clicks.

The connector automatically retrieves and maps Odoo table schemas in their native data types.

Manual and scheduled data refresh.

Execute custom SQL queries for selective data fetching.

The Module helped you build detailed reports, and provide deeper business intelligence.

 These  Modules will improve analytics, customization, and reporting. Module setup can significantly enhance your operational efficiency. Let’s embrace these modules and take your Odoo experience to the next level. 

Need Help?

I hope you find the blog helpful. Please share your feedback and suggestions.

For flawless Odoo Connectors, implementation, and services contact us at 

info@techfinna.com Or www.techneith.com  

[madesimplemssql]

Optimizing query performance requires maintaining current SQL Server statistics. Let's Explore:

https://madesimplemssql.com/update-statistics-sql-server/

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[kaaylabs]

Optimizing Business Operations with Advanced Machine Learning Services

Machine learning has gained popularity in recent years thanks to the adoption of the technology. On the other hand, traditional machine learning necessitates managing data pipelines, robust server maintenance, and the creation of a model for machine learning from scratch, among other technical infrastructure management tasks. Many of these processes are automated by machine learning service which enables businesses to use a platform much more quickly.

What do you understand of Machine learning?

Deep learning and neural networks applied to data are examples of machine learning, a branch of artificial intelligence focused on data-driven learning. It begins with a dataset and gains the ability to extract relevant data from it.

Machine learning technologies facilitate computer vision, speech recognition, face identification, predictive analytics, and more. They also make regression more accurate.

For what purpose is it used?

Many use cases, such as churn avoidance and support ticket categorization make use of MLaaS. The vital thing about MLaaS is it makes it possible to delegate machine learning's laborious tasks. This implies that you won't need to install software, configure servers, maintain infrastructure, and other related tasks. All you have to do is choose the column to be predicted, connect the pertinent training data, and let the software do its magic.  

Natural Language Interpretation

By examining social media postings and the tone of consumer reviews, natural language processing aids businesses in better understanding their clientele. the ml services enable them to make more informed choices about selling their goods and services, including providing automated help or highlighting superior substitutes. Machine learning can categorize incoming customer inquiries into distinct groups, enabling businesses to allocate their resources and time.

Predicting

Another use of machine learning is forecasting, which allows businesses to project future occurrences based on existing data. For example, businesses that need to estimate the costs of their goods, services, or clients might utilize MLaaS for cost modelling.

Data Investigation

Investigating variables, examining correlations between variables, and displaying associations are all part of data exploration. Businesses may generate informed suggestions and contextualize vital data using machine learning.

Data Inconsistency

Another crucial component of machine learning is anomaly detection, which finds anomalous occurrences like fraud. This technology is especially helpful for businesses that lack the means or know-how to create their own systems for identifying anomalies.

Examining And Comprehending Datasets

Machine learning provides an alternative to manual dataset searching and comprehension by converting text searches into SQL queries using algorithms trained on millions of samples. Regression analysis use to determine the correlations between variables, such as those affecting sales and customer satisfaction from various product attributes or advertising channels.

Recognition Of Images

One area of machine learning that is very useful for mobile apps, security, and healthcare is image recognition. Businesses utilize recommendation engines to promote music or goods to consumers. While some companies have used picture recognition to create lucrative mobile applications.

Your understanding of AI will drastically shift. They used to believe that AI was only beyond the financial reach of large corporations. However, thanks to services anyone may now use this technology.

[thedbahub]

Identifying Memory Grant Contributors in SQL Server Query Plans

Introduction When optimizing SQL Server performance, it’s crucial to understand how memory grants are allocated to query plan operators. Excessive memory grants can lead to inefficient resource utilization and impact overall system performance. In this article, we’ll explore practical T-SQL code examples and techniques to determine which operators are contributing the most to memory grants in…

[amparol12]

Breaking Homework Barriers: Journey to Database Brilliance

In the fast-paced world of academia, students often find themselves grappling with the intricacies of database management and SQL homework. The challenges posed by these assignments can be daunting, leaving many seeking a guiding light to navigate the complexities of database design, queries, and optimization. If you're one of those students desperately searching for "help with mySQL homework," you've come to the right place. This blog will serve as your roadmap, guiding you through the journey to unlock the secrets of database brilliance.

Unraveling the Mysteries of mySQL Homework

Help with mySQL homework is more than just a search query; it's a plea for assistance in unraveling the mysteries of structured query language and database management systems. As you embark on your academic quest, you'll encounter challenges that test your understanding of data modeling, SQL syntax, and the nuances of optimizing database performance. Fear not, for every hurdle you face is an opportunity to grow and master the art of database design.

Navigating the Database Landscape

To embark on this journey, it's crucial to understand the landscape you're navigating. Databases are the backbone of modern applications, storing and managing vast amounts of information. SQL, or Structured Query Language, serves as the key to interacting with these databases, allowing you to retrieve, insert, update, and delete data seamlessly. However, the road to becoming proficient in SQL can be winding, filled with challenges that demand attention to detail and a deep understanding of database concepts.

The Role of Expert Guidance

In your quest for database brilliance, seeking expert guidance is akin to having a seasoned navigator on your journey. Platforms like DatabaseHomeworkHelp.com are designed to provide comprehensive help with mySQL homework. These services offer a lifeline for students drowning in assignments, providing expert assistance that goes beyond mere completion to ensure understanding and mastery of database principles.

Tailored Solutions for Individual Needs

One size does not fit all, especially when it comes to mastering database concepts. Help with mySQL homework should be tailored to your individual needs and learning style. A reliable service will not only assist with assignment completion but also provide detailed explanations, clarifying doubts and reinforcing your understanding of SQL. This personalized approach is the key to breaking down barriers and fostering true brilliance in database management.

Overcoming Common Challenges

As you delve into the world of databases, you'll likely encounter common challenges that can be stumbling blocks in your academic journey. Whether it's understanding normalization, crafting complex queries, or optimizing database performance, expert assistance can make all the difference. These challenges, when conquered with the right guidance, become stepping stones to a deeper understanding of database management.

Building a Foundation for Future Success

The journey to database brilliance is not just about completing assignments; it's about building a solid foundation for future success. The skills you acquire in navigating SQL and database design will prove invaluable in real-world scenarios. As industries increasingly rely on data-driven decision-making, your proficiency in database management will set you apart in the job market.

Embracing the Learning Process

Every stumble, every challenge, and every "help with mySQL homework" query is an integral part of your learning process. Embrace the journey, knowing that each assignment is an opportunity to enhance your skills. Don't shy away from seeking assistance when needed, as it's a sign of strength to recognize your limitations and actively work towards overcoming them.

Conclusion: Your Path to Database Brilliance

In conclusion, the journey to database brilliance is not a solitary one; it's a collaborative effort that involves seeking guidance, overcoming challenges, and embracing the learning process. When faced with the complexities of SQL homework, remember that help with mySQL homework is readily available. Take advantage of the resources at your disposal, and soon you'll find yourself not just completing assignments but mastering the art of database management. Your path to brilliance starts now.

[arthue05]

From Zero to Hero: Grow Your Data Science Skills

Understanding the Foundations of Data Science

We produce around 2.5 quintillion bytes of data worldwide, which is enough to fill 10 million DVDs! That huge amount of data is more like a goldmine for data scientists, they use different tools and complex algorithms to find valuable insights.  

Here's the deal: data science is all about finding valuable insights from the raw data. It's more like playing a jigsaw puzzle with a thousand parts and figuring out how they all go together. Begin with the basics, Learn how to gather, clean, analyze, and present data in a straightforward and easy-to-understand way.

Here Are The Skill Needed For A Data Scientists

Okay, let’s talk about the skills you’ll need to be a pro in data science. First up: programming. Python is your new best friend, it is powerful and surprisingly easy to learn. By using the libraries like Pandas and NumPy, you can manage the data like a pro.

Statistics is another tool you must have a good knowledge of, as a toolkit that will help you make sense of all the numbers and patterns you deal with. Next is machine learning, and here you train the data model by using a huge amount of data and make predictions out of it.

Once you analyze and have insights from the data, and next is to share this valuable information with others by creating simple and interactive data visualizations by using charts and graphs.

The Programming Language Every Data Scientist Must Know

Python is the language every data scientist must know, but there are some other languages also that are worth your time. R is another language known for its statistical solid power if you are going to deal with more numbers and data, then R might be the best tool for you.

SQL is one of the essential tools, it is the language that is used for managing the database, and if you know how to query the database effectively, then it will make your data capturing and processing very easy.

Exploring Data Science Tools and Technologies

Alright, so you’ve got your programming languages down. Now, let’s talk about tools. Jupyter Notebooks are fantastic for writing and sharing your code. They let you combine code, visualizations, and explanations in one place, making it easier to document your work and collaborate with others.

To create a meaningful dashboard Tableau is the tool most commonly used by data scientists. It is a tool that can create interactive dashboards and visualizations that will help you share valuable insights with people who do not have an excellent technical background.

Building a Strong Mathematical Foundation

Math might not be everyone’s favorite subject, but it’s a crucial part of data science. You’ll need a good grasp of statistics for analyzing data and drawing conclusions. Linear algebra is important for understanding how the algorithms work, specifically in machine learning. Calculus helps optimize algorithms, while probability theory lets you handle uncertainty in your data. You need to create a mathematical model that helps you represent and analyze real-world problems. So it is essential to sharpen your mathematical skills which will give you a solid upper hand in dealing with complex data science challenges.

Do Not Forget the Data Cleaning and Processing Skills

Before you can dive into analysis, you need to clean the data and preprocess the data. This step can feel like a bit of a grind, but it’s essential. You’ll deal with missing data and decide whether to fill in the gaps or remove them. Data transformation normalizing and standardizing the data to maintain consistency in the data sets. Feature engineering is all about creating a new feature from the existing data to improve the models. Knowing this data processing technique will help you perform a successful analysis and gain better insights.

Diving into Machine Learning and AI

Machine learning and AI are where the magic happens. Supervised learning involves training models using labeled data to predict the outcomes. On the other hand, unsupervised learning assists in identifying patterns in data without using predetermined labels. Deep learning comes into play when dealing with complicated patterns and producing correct predictions, which employs neural networks. Learn how to use AI in data science to do tasks more efficiently.

How Data Science Helps To Solve The Real-world Problems

Knowing the theory is great, but applying what you’ve learned to real-world problems is where you see the impact. Participate in data science projects to gain practical exposure and create a good portfolio. Look into case studies to see how others have tackled similar issues. Explore how data science is used in various industries from healthcare to finance—and apply your skills to solve real-world challenges.

Always Follow Data Science Ethics and Privacy

Handling data responsibly is a big part of being a data scientist. Understanding the ethical practices and privacy concerns associated with your work is crucial. Data privacy regulations, such as GDPR, set guidelines for collecting and using data. Responsible AI practices ensure that your models are fair and unbiased. Being transparent about your methods and accountable for your results helps build trust and credibility. These ethical standards will help you maintain integrity in your data science practice.

Building Your Data Science Portfolio and Career

Let’s talk about careers. Building a solid portfolio is important for showcasing your skills and projects. Include a variety of projects that showcase your skills to tackle real-world problems. The data science job market is competitive, so make sure your portfolio is unique. Earning certifications can also boost your profile and show your dedication in this field. Networking with other data professionals through events, forums, and social media can be incredibly valuable. When you are facing job interviews, preparation is critical. Practice commonly asked questions to showcase your expertise effectively.

To Sum-up

Now you have a helpful guideline to begin your journey in data science. Always keep yourself updated in this field to stand out if you are just starting or want to improve. Check this blog to find the best data science course in Kolkata. You are good to go on this excellent career if you build a solid foundation to improve your skills and apply what you have learned in real life.

[web-scraping-tutorial-blog]

5 useful tools for engineers! Introducing recommendations to improve work efficiency

Engineers have to do a huge amount of coding. It’s really tough having to handle other duties and schedule management at the same time. Having the right tools is key to being a successful engineer.

Here are some tools that will help you improve your work efficiency.

1.SourceTree

“SourceTree” is free Git client software provided by Atlassian. It is a tool for source code management and version control for developers and teams using the version control system called Git. When developers and teams use Git to manage projects, it supports efficient development work by providing a visualized interface and rich functionality.

2.Charles

“Charles” is an HTTP proxy tool for web development and debugging, and a debugging proxy tool for capturing HTTP and HTTPS traffic, visualizing and analyzing communication between networks. This allows web developers and system administrators to observe requests and responses for debugging, testing, performance optimization, and more.

3.iTerm2

“iTerm2” is a highly functional terminal emulator for macOS, and is an application that allows terminal operations to be performed more comfortably and efficiently. It offers more features than the standard Terminal application. It has rich features such as tab splitting, window splitting, session management, customizable appearance, and script execution.

4.Navicat

Navicat is an integrated tool for performing database management and development tasks and supports many major database systems (MySQL, PostgreSQL, SQLite, Oracle, SQL Server, etc.). Using Navicat, you can efficiently perform tasks such as database structure design, data editing and management, SQL query execution, data modeling, backup and restore.

5.CodeLF

CodeLF (Code Language Framework) is a tool designed to help find, navigate, and understand code within large source code bases. Key features include finding and querying symbols such as functions, variables, and classes in your codebase, viewing code snippets, and visualizing relationships between code. It can aid in efficient code navigation and understanding, increasing productivity in the development process.

[olibr08]

Unlock Success: MySQL Interview Questions with Olibr

Introduction

Preparing for a MySQL interview requires a deep understanding of database concepts, SQL queries, optimization techniques, and best practices. Olibr’s experts provide insightful answers to common mysql interview questions, helping candidates showcase their expertise and excel in MySQL interviews.

1. What is MySQL, and how does it differ from other database management systems?

Olibr’s Expert Answer: MySQL is an open-source relational database management system (RDBMS) that uses SQL (Structured Query Language) for managing and manipulating databases. It differs from other DBMS platforms in its open-source nature, scalability, performance optimizations, and extensive community support.

2. Explain the difference between InnoDB and MyISAM storage engines in MySQL.

Olibr’s Expert Answer: InnoDB and MyISAM are two commonly used storage engines in MySQL. InnoDB is transactional and ACID-compliant, supporting features like foreign keys, row-level locking, and crash recovery. MyISAM, on the other hand, is non-transactional, faster for read-heavy workloads, but lacks features such as foreign keys and crash recovery.

3. What are indexes in MySQL, and how do they improve query performance?

Olibr’s Expert Answer: Indexes are data structures that improve query performance by allowing faster retrieval of rows based on indexed columns. They reduce the number of rows MySQL must examine when executing queries, speeding up data retrieval operations, and optimizing database performance.

4. Explain the difference between INNER JOIN and LEFT JOIN in MySQL.

Olibr’s Expert Answer: INNER JOIN and LEFT JOIN are SQL join types used to retrieve data from multiple tables. INNER JOIN returns rows where there is a match in both tables based on the join condition. LEFT JOIN returns all rows from the left table and matching rows from the right table, with NULL values for non-matching rows in the right table.

5. What are the advantages of using stored procedures in MySQL?

Olibr’s Expert Answer: Stored procedures in MySQL offer several advantages, including improved performance due to reduced network traffic, enhanced security by encapsulating SQL logic, code reusability across applications, easier maintenance and updates, and centralized database logic execution.

Conclusion

By mastering these MySQL interview questions and understanding Olibr’s expert answers, candidates can demonstrate their proficiency in MySQL database management, query optimization, and best practices during interviews. Olibr’s insights provide valuable guidance for preparing effectively, showcasing skills, and unlocking success in MySQL-related roles.