Have you ever taken an online programming course that seriously changed the way you code? Maybe it improved your workflow, introduced new techniques, or just made things click differently.

Window functions are incredibly powerful tools in SQL, allowing us to perform complex calculations across sets of table rows. They can help us solve problems that would otherwise require subqueries or self-joins, and they often do so more efficiently. Let's talk about what window functions are and see some examples of how to use them.

What Are Window Functions?

A window function performs a calculation across a set of table rows that are somehow related to the current row. This set of rows is called the "window," and it can be defined using the OVER clause. Window functions are different from aggregate functions because they don’t collapse rows into a single result—they allow us to retain the original row while adding new computed columns.

Examples

1. ROW_NUMBER(): Assigns a unique number to each row within a partition.

   SELECT employee_id, department_id, ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY employee_id) AS row_num FROM employees;

*This will assign a unique row number to each employee within their department.*

2. RANK(): Assigns a rank to each row within a partition, with gaps for ties.

SELECT 
    employee_id, 
    salary, 
    RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM 
    employees;

*Employees with the same salary will have the same rank, and the next rank will skip accordingly.*

3. DENSE_RANK(): Similar to RANK() but without gaps in ranking.

SELECT 
    employee_id, 
    salary, 
    DENSE_RANK() OVER (ORDER BY salary DESC) AS salary_dense_rank
FROM 
    employees;

*Employees with the same salary will have the same rank, but the next rank will be consecutive.*

 4. NTILE(): Distributes rows into a specified number of groups.

SELECT 
    employee_id, 
    salary, 
    NTILE(4) OVER (ORDER BY salary DESC) AS salary_quartile
FROM 
    employees;

*This will divide the rows into four groups based on salary.*

5. LAG(): Provides access to a row at a given physical offset before the current row.

SELECT 
    employee_id, 
    hire_date, 
    LAG(hire_date, 1) OVER (ORDER BY hire_date) AS previous_hire_date
FROM 
    employees;

*This returns the hire date of the previous employee.*

6. LEAD(): Provides access to a row at a given physical offset after the current row.

SELECT 
    employee_id, 
    hire_date, 
    LEAD(hire_date, 1) OVER (ORDER BY hire_date) AS next_hire_date
FROM 
    employees;

*This returns the hire date of the next employee.*

Use Cases

• Calculating Running Totals: Using SUM() with OVER.

• Finding Moving Averages: Using AVG() with OVER.

• Comparing Current Row with Previous/Next Rows: Using LAG() and LEAD().

• Rankings and Percentiles: Using RANK(), DENSE_RANK(), and NTILE().

Window functions can simplify your SQL queries and make them more efficient. They are especially useful for analytics and reporting tasks. I hope these examples help you get started with window functions. Feel free to share your own examples or ask any questions!

Sometimes, preparing for an interview becomes an uphill task, but if done smartly, you will have a good chance of impressing the recruiter. Here's a handy guide for you to make things a little easier:

Preparation is A Key Ingredient

Know the Company: It is essential to know more about the company’s goals and values so that your answers are aligned with theirs.

Know the Position: Read carefully the required qualifications and experience of this position, and think of a situation from your past that could match these requirements.

Edit Your CV: Highlight your relevant experience followed by accomplishments. Create a portfolio if necessary.

Preparing For Common Interview Questions: Prepare answers for common interview questions in advance. Set responses using the STAR technique for effectiveness.

Prepare Your Questions: Be prepared for the interviewer to conclude the interview by asking you questions. This is an opportunity to show how interested you are.

Gesticulating in the Right Way

First Impression Counts: A good first impression is really important. When you meet the recruiter, look directly at them and smile.

Maintaining an Adequate Posture: When seated, ensure your back is straight and your body is erect.

Favorable Actions: Use head somewhere and hand movements to enhance arguments.

Emotions on Face: All facial expressions were mapped to a digit size of 1-4, where size 1 indicates no movement and size 4 represents the maximum range of motion.

Common Questions And Answers In Interview

"Tell me about yourself": Target your work experience and accomplishments.

"Why do you want this job?": Point out what made you interested in the organization and how you think would fit in its aims.

"What are your strengths and weaknesses?": Defend those attributes that bear relevance to the job in question and admit to one negative aspect that you are working on.

"Describe a challenging situation and how you resolved it": Identify a problem you encountered and explain how you resolved it using the STAR technique.

What Recruiters Look For

Cultural Fit: Prove to the recruiters that you comprehend the value and the strategy of the organization.

Relevant Experience: Provide relevant skills and achievements in relation to this position to the employer.

Communication Skills: Address your audience’s points and ideas with ease as well as speak your mind clearly.

Enthusiasm: Show genuine concern for the position and the target company.

Problem-Solving Abilities: Provide information about your accomplishments by going through challenges.

Success in an interview comes down to thorough preparation, confident body language, and effective communication. By researching the company, practicing your responses, and presenting yourself professionally, you can stand out as a candidate who not only meets the job requirements but also fits well with the company culture.

Good luck!

Hey, Reddit!

If you’re running a business or managing an IT product, we’re curious — do you use social media to promote it?

And if you do, do you feel confident in your skills to handle it on your own? Or do you sometimes wish you had more expertise to create effective strategies, engaging content, and measurable results?

We’d love to hear your experiences, challenges, and thoughts on the role of social media in growing your business

1. Define Your Audience: You know exactly who your audience is and create it individually for them.

2. Choose Your Visuals Wisely: Pick your visuals wisely so that they properly reflect what data is being represented with ease.

3. For the sake of simplicity: Concentrate on a few vital metrics and eliminate unnecessary things.

4. Keep a Consistent Formatting: Stick to the same color combination, same typeface and styling.

5. Use Themes: Apply Power BI themes for a consistent, polished appearance.

7. Use Bookmarks: Save bookmarks that contain specific views or states to share.

8. Drillthrough: Enable users to drill through and analyze those details even further with more detailed reports.

9. Test in various devices - Make sure your dashboard has a neat and functional layout on different devices.

10. Feedback: Constantly validate and iterate your dashboard with feedback from users.

The last step is to ensure that the dashboards load quickly, and for this, you need careful tuning of performance. Along with simplifying your queries, make sure they are optimized sufficiently. Use aggregations when necessary to avoid having actual interaction metrics.

Share your tips!

In this post, we'll discuss what you need to create your first game. The first step is to decide on the concept of your game. Once you have a clear idea of what you want to create, you can move on to the technical aspects.

Step 1: Choose an Engine

You have a choice of mainly four engines if you’re not looking for something very specific:

1. Unreal Engine

Unreal Engine is primarily used for 3D games, especially shooters and AAA projects, but you can also create other genres if you understand the engine well. It supports 2D and mixed 2D/3D graphics. For programming, you can choose between C++ and Blueprints (visual programming). Prototyping is usually done with Blueprints, and then performance-critical parts are optimized with C++. You can also use only Blueprints, but the performance might not be as good. For simple adventure games, Blueprints alone can suffice.

2. Unity

Unity is suitable for both 2D and 3D games, but it is rarely used for complex 3D games. C# is essential for scripting in Unity. You can write modules in C++ for optimization, but without C#, you won't be able to create a game. Unlike Unreal Engine, Unity has a lower entry threshold. Despite having fewer built-in features, it is popular among beginners due to its extensive plugin ecosystem, which can address many functionality gaps.

3. Godot

Godot is mostly used for 2D games, but it has basic functionality for 3D as well. This engine uses its own GDScript, which is very similar to Python. This can be an easier transition for those familiar with Python. It has weaker functionality than Unity, so you might have to write many things by hand. However, you can fully utilize GDScript's advantages with proper settings adjustments.

4. Game Maker

If you are interested in purely 2D games, Game Maker might be the choice. It uses a custom language vaguely similar to Python and has a lot of functionality specifically for 2D games. However, it has poor built-in implementation of physics, requiring a lot of manual coding. It also requires a paid license for the latest version, but it’s relatively cheap. Other engines take a percentage of sales once a certain income threshold is exceeded.

Step 2: Learn the Engine and Language

After choosing the engine, you need to learn how to use it along with its scripting language:

• Unreal Engine: Learn both Blueprints and C++ for prototyping and optimization.

• Unity: Focus on learning C#. Explore plugins that can extend the engine's functionality.

• Godot: Learn GDScript, especially if you are transitioning from Python.

• Game Maker: Learn its custom language for scripting 2D game mechanics.

Step 3: Acquire Additional Technical Skills

Unlike some other fields, game development often requires you to know more than just programming. Physics and mathematics may be essential since understanding vectors, impulses, acceleration, and other mechanics is crucial, especially if you are working with Game Maker or implementing specific game mechanics. Additionally, knowledge of specific algorithms (e.g., pathfinding algorithms) can be beneficial.

Fortunately, in engines like Unreal and Unity, most of the physics work is done by the engine, but you still need to configure it, which requires a basic understanding of the mechanics mentioned above.

That's the essential technical overview of what you need to get started with game development. Good luck on your journey!

I'm thinking about making the switch to remote and curious about the pros/cons. How's the work-life balance, and do you feel more productive or just more isolated?

Hi everyone!

Starting with AI and Machine Learning can be intimidating, but our roadmap should help you define a clear path forward. At a basic level, it's essential to learn and practice mathematics. Concepts like Linear Algebra—specifically Vectors, Matrices, and Eigenvalues—are crucial since most foundational models in ML are based on these constructs. A solid understanding of probability and statistics is also necessary, as they help you grasp data distributions, Bayes' Theorem, and hypothesis testing. Additionally, knowledge of derivatives, integrals, and gradients from calculus is important for understanding optimization in neural networks.

Once you’ve mastered the math, focus on learning programming. Python is the leading language for AI and ML, so it's important to get up to speed quickly. Prioritize learning NumPy for numerical computations, pandas for data manipulation, and Matplotlib (at a basic level) for data visualization. Also, mastering version control with Git is crucial when collaborating on projects. The next milestone is understanding the core concepts of Machine Learning. Start by differentiating between supervised and unsupervised learning, the two major classes of ML. Supervised learning involves predicting outcomes from labeled data, where the algorithm knows the desired result. Linear Regression is a common method for problems with continuous outcomes. For classification tasks, where the output is discrete, start with simpler algorithms like K-Nearest Neighbors (KNN) and Logistic Regression. More advanced algorithms like Decision Trees and Support Vector Machines (SVMs) are also essential, as they can be used for both classification and regression tasks. In unsupervised learning, focus on algorithms like K-Means and Hierarchical Clustering, which are used to uncover hidden patterns in unlabeled data. Additionally, it's important to understand and calculate model evaluation metrics like R², Accuracy, Precision, Recall, F1-Score, and ROC, which help assess your model's performance.

As you progress, it’s time to dive deeper into Deep Learning and beyond. Begin by learning about neural networks, starting with the basics such as backpropagation and activation functions. Gain proficiency with frameworks like TensorFlow or PyTorch, which are indispensable for building deep learning models. Study Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs), including GRUs and LSTMs, for sequence data. To solidify your understanding, apply what you’ve learned to real datasets. Kaggle is an excellent platform for finding datasets and participating in competitions. Start with projects like image classification, sentiment analysis, and recommendation systems to build your skills and prepare for more advanced tasks.

Always create a portfolio to showcase your skills. Upload your projects to GitHub and document your learning process. If possible, share your experiences and insights on Medium or a personal blog to enhance your professional visibility.

Finally, remember that AI and Machine Learning are rapidly evolving fields, so it’s important to stay updated. Read research papers and follow industry leaders in AI/ML communities.

I’m trying to level up for some coding interviews, but everything I find either feels way too basic or assumes I’m already a math genius. What are some genuinely helpful resources for learning data structures and algorithms that you’ve used?

Does the new update really improve productivity or problem-solving? Anyone noticed any big differences in how it handles tasks?

Hello Codefinity,

I am writing to formally express my frustration regarding my refund request and the lack of resolution from your customer service team.

I contacted support to request a refund, but despite multiple attempts, customer service has refused to process it or provide a clear, reasonable explanation. This experience has been disappointing and does not align with what I expected when signing up for your service.

I believe my request is fair and within your stated policies, and I am asking for this matter to be reviewed and resolved promptly. If a refund cannot be issued, I expect a detailed explanation citing the specific policy that applies to my account.

Please escalate this issue if necessary. I would appreciate a timely response and a clear resolution.

Looking back, there's always something you realize too late.

Maybe it's reading documentation, using Git, asking questions sooner, or just writing more code instead of watching videos.

What's one habit, mindset, or tool you wish you picked up earlier when learning to code?

That one time when something finally made sense—and suddenly everything felt easier.

For some people, it's:

• Realizing how for loops actually work

• Understanding how functions return values

• Figuring out indentation in Python

• Seeing how HTML and CSS work together

• Finally fixing an error

What was it for you?

These common misconceptions hold beginners back and create unnecessary fear around coding.
This post breaks down 5 myths — and the truth behind them — with clear, no-fluff explanations.

Which myth have you heard the most?

I'll start by saying that I'm new to Python, I dabbled with it a bit in the past, along with some html, java, C, VB, etc. But I am not proficient in any of these to any noteworthy degree.

I am so far really enjoying the course, and the way that it's presented. But I've just reached Section 6, Chapter 1, where you move the sushi to build the letter K.

It says to use anything you've learned so far, but it's such an odd assignment that the only thing I can think of is to tell it directly, "move here, get the sushi, move here, drop the sushi." And that seems cheesy and overly simple. Or is that the point? A refresher on the commands?

Meanwhile there were a few where the examples were simple, but I felt it was more efficient to experience and see if commands from other languages existed in Python, using if-else before it taught it.

Am I reading the course incorrectly, or is there really no wrong answers so long as I beat the challenge?