I wanted to practice a few basic problems on patterns in c++ as I just started the combo course with dsa.

but , every time I run the code there is some weird compilation error with g++ compiler of code studio 

my code works perfectly fine with vs code and other ide's .

someone suggest a solution

Let’s discuss about Facebook Hiring. Either you're a university graduate aiming for any tech-oriented role or an experienced professional who is trying to find better opportunities, you would possibly find my experience useful while interviewing with FAANG. 

How does the Facebook interview process look like? 

The interview process for many technical roles is pretty standard for a general programmer exception being early startups. For Facebook, you have 6 rounds: 

1. Phone screening: Discussion on my work-wise expectations from the role, my current skill set and time complexity of a couple of algorithms. Difficulty: 3/5 

2. DS-Algo: 2 algorithm questions in 45 mins. 2 mins my intro to the interviewer, 2 min interviewer's intro to me. 1 min break. 40 mins for understanding the question, thinking solution then coding. it's this point constraint that made it hard. Question difficulty level: 3/5, Interview difficult level: 4/5

3. DS-Algo: Same as point 2. Question difficulty level: 4/5, Interview difficulty level: 4/5 

4. DS-Algo: Same as point 2. Question difficulty level: 5/5, Questions got very hard during this round. Interview difficulty level: 5/5. 40 mins were just too less :p 

5. Behavioural: this is often against the overall cones a really important and deciding round. So prepare well. I even have given resources to organise this during this blog. Questions around your past contributions, your intern management skills, your excitement to hitch the corporate, work ethics and communication skills were extensively judged. IMO, this round can impact your CTC. No joke! Difficulty level: NA (Candidate drives the interview) 

6. System Design: Give a Google Drive quite system to style which should be scalable enough to serve real-time collaboration. there have been tweaks and it had been not an immediate implementation of Google Drive. Takeaways: clarify the wants, limit the scope of the matter and structure your thoughts. Difficulty level: 5/5 

Note for working professionals: There are levels in big MNC. because the level increase, the amount of DS-Algo rounds are substituted with team management, domain expertise quite rounds. But the method is typically similar. 

How did you steel oneself against interviews? 

This is a really relevant question. FAANG interviews are like binary -- either you qualify mostly all otherwise you qualify none. The preparation depends on: 

1. Your understanding with computing fundamentals - This includes data structures, system design, topics from OS viz. threading, scheduling, memory management; and networking concepts like IP address, DNS and request-response cycle. If you're interviewing for an HFT, the main target is majorly on OS and networking. { HFT process of interviewing is much too different since different skillset is required. 

# Your projects - The projects you are doing gives direct visibility to your understanding of computer fundamentals. To prepare for point 1, practice "regularly" on CodeStudio. If you're not confident in data structures, a course from coding institutions will help surely if you practice. Once you're confident, start practising company-wise. The timeline shouldn't be quite 3 months for confident aspirants, 8 months for brand spanking new to algorithms. If you're taking quite that you simply do injustice to yourself. Sports Programming isn't required for many FAANGs. For Point 2, I strongly advise you to urge involved within the depth of projects at work. this is often relevant to experienced professionals. Depth is required not just for knowledge except for confidence. For undergrads, personal projects help. Don't hesitate to plug your work during interviews. If you do not value your work, nobody does. 

Does resume matter? Yes, definitely. A resume speaks in your absence. confirm you create a resume that at max is 1.5 pages long (1 page is preferable). Remove all irrelevant info like DOB, Father's name, Class 8th olympiad etc. In resume, attempt to specialise in the projects that are relevant to the work description. Sections that ought to be included in your resume: 1. Work Experience/Internship (Mandatory) 

2. Education (Mandatory but include only highest degree. 12th etc isn't relevant for knowledgeable candidates) 

3. Technical Skills (Mandatory) 

4. Projects (Optional if point 1 is healthy) 

5. Achievements (Optional) Resume writing is itself an in depth discussion which i will be able to write soon. Does college matter? When does college not matter? Yes, college matters with no doubt. It gives you an ecosystem to sharpen your technical, emotional, conversational and soft skills. It allows you to make a robust professional network. In India, such an ecosystem is more seen in tier 1 colleges. College doesn't matter if you're not in Tier 1 college. Period. for instance it once more. College doesn't matter if i'm not from Tier 1 college. rather than focussing on college, shift your specialise in developing skills, and build a network both inside and out of doors your college. For students/professionals from tier 2/3/4/5... networking may be a big issue. Many opportunities are never leveraged because you do not know that they exist. Network solves this problem. I desire i can not achieve life. What should I do? Question yourself whether you've got seen enough failures to desire that. If yes, tell yourself, "I will make mistakes; the sole thing I can do is to form them early". Success comes by following a process. 

Don't specialise in the web. Will I see a big change in my life if I crack FAANG? Should i select FAANG over startup? Definitely yes. you'll be recognised on a worldwide scale. you'll have the chance to figure on projects impacting billions of lives. this is often true only you're within the right team at the proper place though. you'll have better connections, better paychecks. But but but...never underestimate the training you get in startups. Startups shape you from within. They push you to release things faster. they provide you adequate chance to form mistakes and improve. So if are you trying to find diverse skillset (DIY) choose a startup; if you're trying to find in-depth process-oriented skillset choose FAANG.

Want to learn all about the process of getting yourself in Facebook? Explore everything here!!!!

Hey, I have shared my campus interview experience with Zepto for SDE - 1.

Hope this helps!

Interview experience link with approach

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Data Science vs. Machine Learning

Machine learning and Data science are the most important fields in today's world. All the science fiction you see being born in the world is a contribution from the fields of computer science, artificial intelligence (AI) and machine learning. In  this Data Science vs Machine Learning blog, we discuss the importance and differences between machine learning and data science. 

 In this Data Science vs. Machine Learning blog, we cover the following topics: 

 * What is Data Science? 

 * What is machine learning? 

 * Areas of Data Science 

 * Use Case Studies

 What is Data Science? 

 Before we get into the details of data science, let's consider how data science came about. Remember when most  data was stored in Excel spreadsheets? Those were simpler times because we produced less data and the data was structured. Then simple Business Intelligence (BI) tools were used to analyse and process  data. 

 But times have changed. More than 2.5 quintillion bytes of data are created every  day, and that number is only growing. By 2020, about 1.7 Mb of data will be generated per second for every person on Earth. Can you imagine how much data that is? How do we handle so much data? 

In addition to the fact that the data produced today is mostly unstructured or semi-structured, simple BI tools can no longer do their job. We need more complex and powerful algorithms to process data and extract useful insights. This is where data science comes into play. 

For example,  you must have watched something on Netflix. Netflix data studies the movie-watching habits of its users to understand what interests users and uses that data to make decisions about which Netflix series to produce. 

Similarly, Target recognises each customer's buying behaviour by drawing  patterns from their database, helping them make better marketing decisions. 

Now that you know why data science is important, let's move on and discuss what machine learning is. 

What is Machine Learning? 

The idea behind machine learning is that you teach machines by feeding them information and letting them learn on their own without  human intervention. To understand machine learning, let's consider a small scenario. 

Let's say you joined skating  and  have no previous skating experience. At first you would be pretty bad at it because you have no idea  how to skate. But as you observe and gain more knowledge, you will improve. Observation is only one way of collecting data. 

Just as we humans learn from our observations and experiences, machines can learn by themselves when given a lot of data. This is exactly how machine learning works. Machine Learning starts with reading and observing the training data to find useful insights and patterns  to build a model that predicts the correct result. After that, the performance of the model is  evaluated  using the test dataset. This process is carried out until the machine automatically learns and maps the input to the correct output without  human intervention.

Hopefully you have an idea of ​​what machine learning is. If you want to learn more about machine learning, watch this video from our machine learning experts. 

Areas of Information Science 

Information Science encompasses many areas, including artificial intelligence (AI), machine learning and deep learning. Data Science uses various methods of artificial intelligence, machine learning and deep learning to analyse data and extract useful knowledge. To make things clear, let me define the following terms for you: 

 * Artificial Intelligence: Artificial Intelligence is a subset of computer science that allows machines to simulate human behaviour. 

 * Machine Learning: Machine learning is a subfield of artificial intelligence that offers machines the ability to automatically learn  and evolve based on experience without special programming. 

 * Deep Learning: Deep learning is a branch of machine learning that uses various computer procedures and algorithms called artificial neural networks inspired by the structure and function of the brain. 

In summary, data science involves extracting information from information. To  do this, it uses various methods from different disciplines such as machine learning, artificial intelligence and deep learning. It should be noted here  that data science is a broader field and does not only depend on these techniques. 

Now that you have clearly distinguished between artificial intelligence, machine learning and deep learning, let's discuss a use case where we see how data science and machine learning are used in  recommendation engines. 

Use Case

Recommendation Engine: 

Before we discuss how machine learning and data science are applied to a recommendation system, let's see what exactly a recommendation engine is. 

What is a recommendation engine? 

You've probably all  used Amazon for online shopping. Have you noticed that when you search for a certain product on Amazon, you get suggestions for similar products? Well, how does Amazon know this? 

The reason why companies like Amazon, Walmart, Netflix, etc. doing so well is because of how they handle user generated information. 

Each user gets a personalised view of the e-commerce site according to their profile, allowing them to choose relevant products. For example, if you're buying a new laptop from Amazon, you might  want to buy a laptop bag. Based on such connections, Amazon will recommend more products to you. 

 The data science workflow has six well-defined steps: 

 * business requirements 

 * data acquisition 

 * data collection 

 * data exploration 

 * data modelling 

 * implementation and optimisation 

 Let's analyse the machine learning process to understand data modelling. You get the data collected for the machine learning process. Data must be in a readable format, such as a CSV file or spreadsheet. Clean your data: Your data can have multiple duplicate, missing, or N/A values. Inconsistencies in these data can lead to incorrect predictions and should be corrected at this stage. model creation. This step splits the data set into 2 sets. One for training and one for testing. Then you need to build a model using the training dataset. Models are built using machine learning algorithms such as logistic regression, linear regression, random forests, and support vector machines. In this step, the machine learning model is trained on the training dataset. Most data sets are used for training, allowing the model to learn how to map inputs of different sets of values to outputs. Model testing: Once the model is trained, it is evaluated using a test data set. In this step, new data points are fed into the model, and the new data points must be run on the previously built machine learning model to predict the outcome. Model improvement: Evaluate the model using the test data and then calculate the accuracy. There are n ways to improve the efficiency of the model. Techniques such as cross-validation are used to improve model accuracy. That was all about the machine learning process. Let's move on to the last step of the data lifecycle. Step 6: Deploy and Optimise The goal of this phase is to deploy the final model into production for end-user adoption. At this stage, the user should check the performance of the model and if there are any problems with the model, they should be corrected at this stage. 

Machine learning helps data science by providing a set of algorithms for data mining, data modelling, decision making, and more. Data science, on the other hand, combines a series of machine learning algorithms to predict outcomes. Before concluding this blog, I would like to conclude that data science and machine learning are interrelated fields, and machine learning is a part of data science, so there are not many comparisons between the two.

The question which get’s asked always about these two is that “What pays more Data Science or Machine Learning?” So, if we compare the Salary Trends of Machine Learning Engineer and Data Scientist we can see that in general, a Machine Learning Engineer Earns a little more than a Data Scientist. 

Professionals involved in software development for e-learning note that professional software development services are very popular today. But what about the future of software development in 2023? Will it remain as promising as it is today? You can find answers to these and other important questions in this article. 

What is software development and what are the main steps? 

Software development is a complex process that aims to create and maintain the functionality, quality, and reliability of software using techniques, methodologies, and best practices from disciplines such as computer science, project management, mathematics, engineering, and more. 

The key steps in developing educational software are: 

1. Analysis of requirements

Ideas are explored first. It is important to understand what the client wants to receive and how they view the future product. This allows you to form ideas and evaluate potential customers. Then we study competitors, similar products in the market, target audience, and opportunities. Various analytical studies are beingconducted to understand how to turn ideas into working products. 

2. Design

 At this stage, a workplan is drawn up and a project is created. This will help you understand how the product is implemented. 

3. Development and Programming The designer creates the external part of the program and starts developing the user interface. They create modules, connect components, think over the overall structure and create layouts. 

4. Documentation

At this stage, technical documentation is created. The basic principles and functions of the program are explained in detail.

5. Testing

When the program is ready, it undergoes rigorous testing. A software development methodology defines testing options and methods for evaluating development effectiveness. If errors are found during testing, they will be corrected. 

6. Implementation and Support

If the product passes the test, it goes into operation. Key trends in e-learning software developmentConsidering educational software development companies and the process of global computerization and the ever-growing process of access to specific information, the following prospects for software development can be distinguished: 

* Improved IT services. Information products in the form of software and emergency support services are strategically important. 

* Interaction. The purpose of interaction implies the possibility of combining software and hardware.This allows information to be processed and transmitted based on the scale and speed of the action. 

* Remove intermediate links. The development and improvement of information exchange processes as well as the involvement of network technologies for supplier-consumer interaction will help eliminate intermediate links. 

* Globalisation. E-learning software companies can work fromanywhere and get the information they need. Expansion of the information technology market is aimed at generating profits through the export of services to a wider geographic area. Software development will definitely be in high demand.

* Reconciliation. The differences between toolsand services, technology products and software, home-level applications, business realms, and information and entertainment are leveled out. Internet technologies are used more often. This software is designed to make it easier for users to process, store and share information in any format. Computer technology applies to all areas of human life and activity. Programmers are therefore engaged daily in developing new features and technical means to make the software easier to use. 

What will change the most due to software development in 2023?

 As you can see, increasing computer and network speeds, as well as the need for constantly updated information, are the most important drivers of digital transformation today. Find out below what will change the most assoftware development evolves in 2023. Rapid development in the field of IT The IT area concernsthe activities of entities related to computerized creation, storage, data processing and management processes. The developed products are characterized by integrity and are the result of a combination of software and technical means, hardware, intellectual human resources, information, and databases. The offer on the market of IT products is formed by specialized enterprises of the IT sphere, IT services of non-specialized enterprises, subjects of IT outsourcing, IT consultants, and specialists who independently develop IT products. Demand for IT products is formed by representatives of business, state administration, the non-commercial sector, and the public. Software development is and will definitely be a critical moment for many companies in the coming years. Artificial Intelligence In many areas of science and social life, machines, or so-called robots, endowed with artificial intelligence are used today. They can save a person from everyday activities. Therefore, systems based on artificial intelligence are increasingly used in technology. For example, these are robots involved in production. Artificial intelligence is widely used in software development and it is clear that this trend will continue. It helps e-learning software companies make informed decisions and provides a solid foundation for digital transformation. Progressive Web App A Progressive Web App is a website that can be installed as a standalone app. It can be easily launched from a shortcut and is used separately from the main browser window. From the user's point of view, Progressive Web Apps are just like regular apps, they run much faster, take up less memory, and don't require you to go to the app store to install them. 

As software development advances in 2023, progressive web apps will continue to provide value to users. It is expected that software development will be actively carried out in the future. Therefore, organizations that choose to invest time, money and resources to adapt to changing market conditions will gain a competitive advantage over the long term.

Hey, I have shared my campus interview experience with Microsoft for SDE - Intern.

Hope this helps!

Interview experience link with approach