Trending December 2023 # C – It Role To Hands # Suggested January 2024 # Top 13 Popular

You are reading the article C – It Role To Hands updated in December 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 C – It Role To Hands


Congratulations on choosing data science as your future career! It’s a great decision.

Data science is a thriving field with a remarkable number of job openings around the globe. The demand is outstripping the supply! That means there are more vacancies than qualified data science professionals.

So this journey you have taken to become a hands-on data science professional? You can already visualize why it’s the path to future success. There are a variety of problems you can solve, a whole host of tools you can master, and a broad range of techniques you can learn and then play around with.

Although being from an IT background, we would highly suggest you take data engineering into consideration for your career transition as it matches almost all your strong points (plus it is a potential future job role!).

The canvas is in front of you – now it’s your turn to pick up the data science brush and start painting your way to a successful data science transition.

What can you expect in a hands-on data science role?

A hands-on data science role is little bit of programming, little bit of statistics, a pinch of business domain knowledge and a whole lot of forming and understanding the problem statement

Data science may be the sexiest job of the 21st century but like all jobs, even this one requires hard work. A day-to-day hands-on role in data science requires working on the same problem for long hours performing continuous in-depth research. This role requires you to be well-versed with probability and statistics, programming, machine learning.

A data science role requires you to be in continuous communication with the stakeholders as well as other teams. On the soft skills side, you’d want to keep up on your communication skills, storytelling skills and structured thinking ability. We’ll talk about these skills in a moment.

A typical data science project lifecycle looks like this:

Converting the business problem into a data problem

Hypothesis generation

Data collection or extraction

Data exploration and validating hypotheses

Data modeling

Model deployment

Presenting your work to the final user/client/stakeholder

Depending on your role, your project, and your organization, you’ll be working on different stages. Some projects require a data scientist to do the end-to-end work. Most projects will expect you to be involved from the start but will leave the data collection and model deployment stages to data engineers. It all comes down to specific use cases.

What can you expect in a data engineering role? Given your background, you have an extra edge to look at the engineering side of data-related roles as well.

A data science role seems to be very attractive but the industry requires data engineers more than they do data scientists.

A data engineer is someone who develops and maintains architectures for smooth data flows within large-scale data processing systems. He deals in raw, unstructured, and dirty data which is often inconsistent and invalidated.

It is the job of a data engineer to create architectures and systems to churn out data that is efficient, reliable, and of high quality. They work in sync with the team of data scientists as well as the stakeholders.

Given your engineering background, you will find it much easier to understand the following concepts –

Software engineering,

Database systems

Cloud technology

Efficient programming skill

This will definitely provide an edge over other candidates!

The role of a data scientist is really crucial to the whole organization and the economy as a whole. But the problem is – there is a shortage of “Skilled” data scientists globally. The AI and ML Blackbelt accelerate program aims to make you an industry-ready certified data science professional with 14+ courses, 39+ real-life projects, and 1:1 mentorship sessions so that you are never off-track.

What are the key skills required to excel in a hands-on data science and data engineering role?

Data science and data engineering are multi-faceted roles. There is no one-size-fits-all approach to learning these subjects. Having said that, there are a few core skills you will need to pick up to make a successful career transition to data science.

Here are the key skills you would need:

Programming knowledge

Software engineering

Ability to work with Databases

Big Data

Machine Learning concepts

Model Deployment

Apart from these core skills, there are other skills you should be aware of, such as:


Structured Thinking


Deep Learning concepts

Some of the commonly used tools in big data are Apache Spark, Hadoop, AWS, and on the database side, both SQL and NoSQL databases have equal importance. MySQL, CassandraDB, MongoDB are a few common ones.

How can you excel in each of these required skills?

Ah, the key question! Now that you know what you need to learn, the attention turns to how you can learn those skills. Let’s look at a few options and suggestions on how to pick up and hone the key skills we mentioned above.

Programming Language

Machine Learning has seen a great jump only because of the boost in computing power. Programming provides us a way to communicate with machines. In the case of data science, you must be comfortable with programming but in data engineering, you need to be good at programming concepts.

First of all, choose the programming language of your choice. Python, R, or Julia are to name a few and each has its own set of Pros and Cons. Python is a general-purpose programming language having multiple data science libraries along with rapid prototyping whereas R is a language for statistical analysis and visualization. Julia offers the best of both worlds and is faster. If you are confused about which language to choose, we have compiled a resourceful article for you:

Python is the market leader right now and continues to be widely used in the industry. It’s a lot easier to perform machine learning tasks using Python, due to the availability of libraries and high support for deep learning. For data engineers, Java is the go-to language and the majority of big data frameworks are written in Java. Another appealing language is Scala!

Software Engineering

To write a high and good quality code that won’t cause havoc during the production stage, it is necessary to know the basics of some of the software engineering subjects like – basic lifecycle of software development projects, data types, compilers, time-space complexity, etc.

Writing efficient and clean code will help you in the long run and help you collaborate with your team members. Again, you don’t need to be a software engineer but being clear with the basics will help you.

Ability to work with Databases

As a hands-on data science professional, you’ll be working a LOT with databases. You will need them to extract your data, extract subsets, and extract samples.

Hence, having hands-on knowledge of databases is essential. The most common database language you should pick up is SQL.

SQL is a must-have skill for every data science professional. You should start from the basics of databases and structured query language (SQL) and learn about everything you would need in any data science profession, including Writing and executing efficient Queries, Joining multiple tables, and appending and manipulating tables.

Whereas, if you are inclined towards data engineering, you will be requiring to go deeper into this field and understanding in-depth NoSQL as well. Knowledge of AWS and other cloud services is also essential.

Here are a few resources to help you get started with Databases:

Big Data

We are generating data at a rate of 2.5 Quintillions per day! Due to the rise of the internet, social media networks, IoT there has been a sudden boom in the rate of data we are generating. This data is high in volume, velocity, and veracity which form the 3V’s of Big Data.

Organizations have been overwhelmed with such a large amount of data and they are trying to tackle this data by rapidly adopting Big Data Technology so that this data can be stored properly and efficiently and used when needed.

Hadoop, Spark, Apache Storm, and Flink, Hive are some of the Frameworks/ Tools you must master.

Here are a few resources to help you get started with Big Data:


Statistics is the grammar of data science.

The knowledge of the concept of descriptive statistics like mean, median, mode, variance, the standard deviation is a must. Then come the various probability distributions, sample and population, CLT,  skewness and kurtosis, inferential statistics – hypothesis testing, confidence intervals, and so on.

Statistics is a MUST concept to become a data scientist. You can deep dive into some of these concepts with these clear articles and their examples:

Machine Learning Concepts

For a data scientist, machine learning is the core skill to have. Machine learning is used to build predictive models. For example, you want to predict the number of customers you will have in the next month by looking at the past month’s data, you will need to use machine learning algorithms.

If you are looking for specialization, Natural Language Processing (NLP) and Computer Vision are two fields that are absolutely thriving right now. Each requires you to dive deep into those specific fields so make sure you’re aware of what you’re getting into.

This is as good a place to start as any:

Model Deployment

Once you have made the complete data science project, it is time for the intended user/ stakeholder to reap the benefits of the predictive power of your machine learning model. In simple words, this is model deployment. This is one of the most important steps from a business point of view but also the least taught one.

Let us take an example here. An insurance company has initiated a data science project which uses Vehicle images from accidents to assess the extent of the damage. The data science team works day and night to develop a model that has a near-perfect F1 score. After months of hard work, they have the model ready and the stakeholders love its performance but what after that?

Remember that the end-user, in this case, are the insurance agents and this model needs to be used by multiple people at the same time who are NOT data scientists. Therefore they’ll not be running a Jupyter or Colab notebook on GPUs. This is where you need a complete process of model deployment.

This task is usually done by machine learning engineers but it varies according to the organization you are working in. Even if it is not the job requirement of your company, it is very important to know the basics of model deployment and why it is necessary.

Here are a few resources to help you get started with structured thinking:

Structured Thinking

Structured thinking is a process of putting a framework to an unstructured problem. Having a structure not only helps an analyst understand the problem at a macro level, but it also helps by identifying areas that require deeper understanding.

Without structure, an analyst is like a tourist without a map. He might understand where he wants to go (or what he wants to solve), but he doesn’t know how to get there. He would not be able to judge which tools and vehicles he would need to reach the desired place.

How many times have you come across a situation when the entire work had to be re-done because a particular segment was not excluded from data? Or a segment was not included? Or just when you were about to finish the analysis, you come across a factor you did not think of before? All these are results of poorly structured thinking.

Here are a few resources to help you get started with structured thinking:

The AI and ML Blackbelt Accelerate program not only covers all the hard skills like Python, machine learning, statistics but also other essential soft skills like structured thinking and storytelling skills. Not just that you also get a resume and interview assistance!

Focus on Gaining Hands-On and Practical Experience in Data Science

Whatever we have covered so far has a lot to do with understanding different data science concepts. We’ve covered both the technical side (programming, machine learning, statistics, etc.) and the soft skills aspect (structured thinking).

So, what’s the next step for you in your transition journey?

It’s time to apply your knowledge in a practical scenario! Yes, you need to marry your theoretical knowledge with hands-on practical experience to truly stand out as a data science transitioner. Given your background, there are broadly three ways you can do this:

Participate in hackathons: This is perhaps the most popular option to gain practical knowledge. Data science competitions and hackathons are awesome! You’ll love the variety of business problems we get to solve and when we add in the pressure of finding a solution under a tight deadline – it’s a great learning experience. Data Science hackathons area great way to:

Test your data science knowledge

Compete against top data science experts from around the world and gauge where you stand

Get hands-on practice of a data science problem working in a deadline environment

Improve your existing data science skillset

Enhance your existing data science resume

Get started with hackathons here

Pick up open source data science projects: One key thing that has helped transitioners immensely is picking an open-source data science project and running with it. This not only helps you understand the key areas you need to improve on but also shows you the way forward. And these projects aren’t your run-of-the-mill data science projects. These are specific projects that tackle a certain data science sub-field, such as computer vision, web analytics, and so on. The project could be a dataset, a state-of-the-art library that has brought the data science field forward, or even an open-source analytics tool. So, pick a project that intrigues you and start working on it today! Check out more open source projects here!

Apply for data science internships: This is the most popular path to breaking into the data science industry. Even for experienced people – internships are a very effective way to break into data science. We have now seen so many successful transitions enabled by internships. Not only do you gain hands-on experience in data science, but you also get to learn how the industry works and how a typical data science project functions. It’s an invaluable experience!

Data Science projects are a must if you want to make a mark in your career. The AI and ML Blackbelt accelerate program offers massive 39+ courses that will make sure you get exposure to a variety of projects. Are you ready for all types of tasks that will come ahead in your journey? 

Stay up to date with current developments in the domain

This is another essential aspect of working in data science. We’ve seen the majority of transitioners skip this step and focus exclusively on picking up machine learning concepts – don’t do that!

Data science is still a very nascent field. We see major breakthroughs happening on a regular basis (sometimes a weekly basis!) and it can become difficult to keep up with all that’s happening. But if you can find time to catch up on the latest developments, you’ll already have an edge on your competition.

Let us give you an example. The Natural Language Processing (NLP) field has come a long way in the last 3 years (since 2023). We see a new language model seemingly every week that builds on the last major breakthrough. If you can keep up with this pace, if you can spend a bit of time understanding what’s going on, you’ll gain invaluable knowledge that your peers won’t have.

So what are the different ways in which you can stay up to date in the vast space of data science? Here are three suggestions based on our experience:

Follow Newsletters and blogs: This is the easiest way to stay abreast of developments. There are plenty of good newsletters out there (just do a quick Google search) that will send you weekly updates. You can also subscribe to blogs like Analytics Vidhya to check out the latest tools and techniques in data science.

Data Elixir

The Data Science Roundup

O’Reilly Newsletter


The Week in Data

Attend MeetUps: This one requires a bit of effort but the eventual payout can be HUGE. Meetups offer you an unparalleled opportunity to meet your fellow transitioners and connect with them, learn from them, and build a rapport that might benefit both parties. Over time, once you are comfortable with core machine learning concepts, you can even try and speak at these meetups to build your profile

The big salary question – what can you expect from this transition?

Making a career switch to data science for getting a salary bump is entirely justified. However, it isn’t as straightforward as you might think. There are certain things, such as work experience and your current domain, that will play a MASSIVE role in deciding your salary post-transition.

Taking figures from the popular and relatively accurate website called Glassdoor, this is what the salary situation looks like for a data scientist:

As you can see, in India, the average salary of data scientists is approximately INR 10,00,000 per year whereas the average salary of data science professionals in the United States is $113,000 per year.

Similarly, the average salary for data engineers in India is approximately INR 7,00,000 per year, the figure is around $103,000 per year in the United States.

If you bring a bit more experience to the table and you have relevant domain experience, you might look at a more senior role (though this is a bit rare if you have no prior data science experience):

Similarly, the average salary of a senior data engineer is around the lines of INR 14,00,000 in India. A senior data engineer makes earns an average pay of $136,000 per year.

As we said, it comes down to how relevant your previous experience is. More often than not, as a person transitioning from an IT role to data science, you’ll be looking at the first graph.

What are the challenges to get the “Sexiest Job of the 21st Century”?

There has never been a better time to become a data scientist. Data Science is a booming industry but it also comes with its own set of challenges. Keeping in mind that you come from an IT background, it should help you overcome the majority of challenges, however, we’ll list a few that need your special attention. If you have reached till here, we know you can work out through obstacles. Let’s take them up one by one –

Working on Quant Skills – The basis of data science is derived through its quantitative nature. An absence of a quantitative degree may create a challenge in understanding the basics of this field. Therefore, you may need to spend a chunk of your time working on your quant skills at the beginning of your journey to create a strong foundation.

Mastering Statistics – Statistics forms the core of data science algorithms. You’ll need some effort to keep down your keyboard and learn using the old pen and paper method and then move on to embrace your coding skills into statistics. Statistics will help you step ahead in your data science and data engineering career so make sure to work on it in your initial days.

Absence of practical knowledge – No matter at what stage of the data science career cycle you are, the key thing is to have experience with real-life projects. Gone are the days, where the definition and code of a simple random forest algorithm would have landed you the job. You must be clear with the ins-and-outs of the subject. You can work through the challenge by focusing on the points mentioned above. 

Focussing on the tool rather than the concept – A tool is merely a means to getting your data science task done in an efficient manner, it is in no way an indicator of a strong grasp of data science tasks. A great example is SAS, it’s a paid data science tool that was used majorly in the analytics industry but after the arrival of open-source tools like Python and R, it saw a decline. Therefore, it’s imperative that you focus on the concept rather than the tool.

Structured Thinking – Ah, the most crucial skill yet the most overlooked one. Structured Thinking as discussed above is the art of breaking down the large unstructured problems into smaller and manageable problems. A data science project is valid as long as the problem statement is correct, otherwise, the whole project goes down the drain. Being a data science professional, you must ensure that you are working on the right problem statement.

Afraid of all the challenges that are supposed to come in your way? Well not anymore, how about an expert mentor that will provide you with a personalized learning path that is in sync with your goals and keeps track of your progress? It is possible with the AI and ML Blackbelt Accelerate program which comes along with 75+ mentorship sessions.

Final Thoughts

Now that you are aware of the various components you’ll need to put together to make this career transition, are you prepared to buckle up and take this thrilling journey? The payoff is immense but as you might have gathered, you’ll face plenty of obstacles along the way. Your eventual success will come down to how well you can get past these hurdles.

You're reading C – It Role To Hands

Top 10 C And C++ Projects For Beginners To Improve Their Skills

Here we will see some top C and C++ Projects for beginners to upskill themselves 

One of the best ways to master the concepts of any programming language is to create a project in that particular language. Making a project will help you gain a good hold of the language as well as its fundamentals. C and C++ projects are more trends nowadays. C and C++ gave programmers high control over memory and system resources. C and C++ projects for beginners are the best way to start a career in this field. If you know any other programming language, C and C++ projects for beginners will be easy to learn. Even otherwise, C and C++ are friendly languages, and it will be easy to learn through some hands-on top C and C++ projects and practice. In this article, we will enlist the top 10 C and C++ projects for beginners to improve their skills.

Login and Registration System

This is one of the simplest projects to start with to learn about file systems in C++. The project involves a user registration process by asking username and password. Upon successful registration, a user file is created with the credentials. If the user does not exist, upon login, an error will be shown. You will also learn how to use Visual Studio to create a simple project.

Car Rental System

This is a trendy project and very useful for learning about keyboard events, date-time functions, and implementing a C++ login system. The program has separate menus for the admin and other users. There are also methods to calculate fares based on time and distance, including displaying car details, availability, etc.

Bookshop inventory system

This is a simple project where the system maintains the inventory of books in a bookshop. If a customer purchases a book, the book’s count will decrease; if a book is added, the same is updated. Notice the use of pointers. You can modify the code to add a book ID and make the search based on the book ID or make the search using just one parameter giving multiple results, and so on.

Student Report Management System

Through this project, we can learn a lot about input/output streams and the file management system of C++. Our program collects student details like name, roll number, and marks in each subject, and calculates their grade. This is a simple console app. Note that we focus only on the correct inputs in this project, and you can enhance it to handle wrong inputs.

Casino Number Guessing Game

This is an exciting project, where we will learn about the library used for random numbers: cstdlib. The program asks for a betting amount and then asks the user to guess a number on rolling. If the random number generated matches the user input, he wins, else money is deducted. The user can keep playing until he loses all the amount he put in initially.

Sudoku Game 

We all know about the popular Sudoku game, wherein we need to arrange numbers from 1-9 such that they appear only once in a row and column of a 9×9 grid. The program uses the concept of backtracking. In this program, we have hard-coded the initial values, but you can also get the same input from the user.

Credit Card Validator

This is a simple project that uses Luhn’s algorithm to validate a user’s credit card. The program works for all popular cards like Visa, Amex, MasterCard, etc. Luhn’s algorithm checks for basic validations; for example, a Visa card should start with 4 and then moves on to complex digit-wise calculations. It is a good program to learn because most e-commerce transactions require credit card validation.

Helicopter Game

For all the 90s kids, this was one of the most favorite games and very easy to implement! In this project, we will use SDL graphics. The game is to move the helicopter forward without touching the obstacles. The player should control the game through keys, and holding the key moves the helicopter, and releasing it will bring the helicopter down.

Tic-Tac-Toe Game

We all know that tic tac toe is a game, which is played between two players to mark Xs and 0s alternately and each player tries to complete a row before the other player. To create its structure, we have to display it using the count function, and with the help of a multidimensional array, the spaces can be filled with 0s and Xs. This tic-tac-toe game can be created with the use of if-else statements, switch-case statements and other basic concepts of C++ are enough to make this project a Tic Tac Toe game.

Hotel Management System

Identity Cohesion Vs. Role Confusion

Everyone knows the term “identity crisis,” especially when they are teenagers, and it is a dramatic phase and carries a negative connotation. Some psychologists, however, believe it is a normal part of social development. Erik Erikson formulated a theory called the eight psychosocial stages of development which he describes this phase in his fifth stage, identity cohesion vs. role confusion.

Identity Cohesion vs. Role Confusion Erik Erikson’s Theory- Identity Crisis

The German-American psychologist Erik Erikson coined the term “identity crisis.” Some psychologists say that Erikson did so because of his turbulent childhood. He was born in Germany in 1902 and adopted by a Jewish stepfather. He often felt like an outsider and was teased for being Hewish. As he experienced identity crises between the ages of 12–18, this may be why, he speculated, his struggle was present in other children as well.

What does Happen Before Identity Cohesion vs. Role Confusion? The 5th Stage: Identity Cohesion vs. Role Confusion

Now, during adolescence, the individual questions these prior identifications. Then the ego reconstructs them while integrating these with strong, emerging sexual feelings and social roles.

What is Identity?

Identity is a multifaceted concept- it refers to the sense of uniqueness one gets from various psychosocial experiences. The ego integrates all the identifications learned as participants in different groups and all self-images. It includes feelings of making good partner choices and our connection with the future when opting for certain careers. This is important in establishing who the individual is in a social setting. Therefore, identity consists of what the individual is, what they want to become, and what they are supposed to be.

Understanding Identity Crisis

Identity cannot be easily achieved; an identity crisis is required. This is the connection between childhood and adulthood. Here, individuals must solve multiple special problems, and improper resolution of these problems forces the individual to find an identity again in later stages. These crises in youths stem from role confusion. Here, they experience a tough period of self-consciousness characterized by awakening sexual drives and body growth. This brings doubts and shame over what they are and may become. The most troublesome part of this stage is deciding one’s occupational identity. Though individuals want to commit to goals that give meaning to their lives, many find it extremely difficult.

The Concept of Totalism

Erikson gave that confused individuals often try to establish their identities by over identifying with their heroes, making them defensive in many ways. Thus, their behavior during this phase is characterized by totalism, which refers to setting absolute boundaries regarding one’s values, beliefs, and interpersonal relationships. Here, simple ideologies may be embraced with little questioning.

Erikson thought these behaviors should be viewed as alternative ways of dealing with experiences. His view on development is optimistic, and he believes that such failure to behave constructively results from political, cultural, and technological changes that lead to establishing values that no longer work. Here, the role of mentors and role models is crucial. Adults need to help individuals with unclear values and provide them with guidance. They need to emulate behaviors that are socially good and acceptable. Remember, both generations need each other to survive.


C++ Program To Sort Dictionary By Keys

In different programming languages, there are some data structures available that are known as dictionaries. The dictionaries are a special kind of faster data structure that stores data based on keys and values. It stores the key-value pair into it so that some elements can easily be searched in almost constant time by the keys. In C++, the dictionary−like data structure is present in C++ STL. The name of this data structure is named as ‘map’. The map creates a pair of keys and values of any type (since we are using C++, the type must be defined before compilation). In this section, we will see how we can sort the entries of the dictionary based on their key parameters in C++.

Let us see how to define map data structure first. This will take two types of templates. The syntax and required library are shown below −

Syntax to define Map Data Structure

Here, we need to import the ‘map’ library to use the map data structure. This takes two data types type1 and type2. Type1 refers to the data type for the key parameter whereas type2 is for the value type. The mapVariable is the object created from the map type class. Now let us see how to sort the map using their key parameters.

Using Vector of Pairs

In this idea, we just create a vector (dynamic array, which is coming as another element from C++ STL) of key-value pairs. Then perform sorting by creating your comparing function. Then store the contents again into a map in a sorted format.


Take map M as input

Define a dynamic array A to store key-value pairs

For each key-value pair p in M, do

insert p into A

End for

sort A based on their keys

create empty map newMap

For each pair p in A −

insert pair p into newMap

End for

return newMap


using namespace std;

// Create a comparator function to perform key-value pair comparison return a.first < b.first; }

//Define sorting function to sort given dictionary or map

for ( auto& it : givenMap ) { pairVec.push_back( it ); }

sort( pairVec.begin(), pairVec.end(), compare);

for ( auto& it : pairVec ) { newMap.insert( { it.first, it.second } ); } return newMap; }

for ( auto& it : givenMap ) { cout << “Key: ” << it.first << “, value: ” << it.second << endl; } }

int main(){

givenMap = { { “Three”, 3 }, { “Two”, 2 }, { “One”, 1 } };

cout << “Before Sorting: ” << endl; display( givenMap );

cout << “After Sorting: ” << endl; givenMap = sorting( givenMap ); display( givenMap ); }

Output Before Sorting: Key: One, value: 1 Key: Three, value: 3 Key: Two, value: 2 After Sorting: Key: One, value: 1 Key: Three, value: 3 Key: Two, value: 2

We have performed the sorting, but we cannot see any difference here. This is because the map data structure itself holds the key-value pairs based on the sorted form of their keys, but it is not always the case. In a few scenarios, it can store data out of their order.

Using a Set of Pairs

Set is another data structure that can be used to sort the key-value pairs from a map data structure. The set data structure stores data in sorted order. So it does not require an additional sorting step after inserting elements into the collection. Let us see the algorithm for a clear understanding.


Take map M as input

Define a set S to store key-value pairs

For each key-value pair p in M, do

insert p into S

End for

create empty map newMap

For each pair p in S −

insert pair p into newMap

End for

return newMap


using namespace std;

// Create comparator function to perform key-value pair comparison struct compare {

bool operator()(const T& a, const T& b) const { if (a.second != b.second) { return a.second < b.second; } return a.first < b.first; } };

//Define sorting function to sort given dictionary or map

for ( auto& it : givenMap ) { pairSet.insert( it ); }

for ( auto& it : pairSet ) { newMap.insert( { it.first, it.second } ); } return newMap; }

for ( auto& it : givenMap ) { cout << “Key: ” << it.first << “, value: ” << it.second << endl; } }

int main(){

givenMap = { { “Three”, 3 }, { “Two”, 2 }, { “One”, 1 }, {“Four”, 4}, {“Five”, 5}, };

cout << “Before Sorting: ” << endl; display( givenMap );

cout << “After Sorting: ” << endl; givenMap = sorting( givenMap ); display( givenMap ); }

Output Before Sorting: Key: Five, value: 5 Key: Four, value: 4 Key: One, value: 1 Key: Three, value: 3 Key: Two, value: 2 After Sorting: Key: Five, value: 5 Key: Four, value: 4 Key: One, value: 1 Key: Three, value: 3 Key: Two, value: 2 Conclusion

In this article, we have seen two different techniques to sort the dictionary data structure (map in C++) and sort them based on their key parameters. The maps are hash−maps and they use hashing techniques to store data for their keys. The keys are unique, and values may be the same for different keys. We have arranged them using set and vector sorting where the vectors and sets are holding pairs. Each pair has two types. The first type is a key type, and the second type is a value type.

Windows Phone 7 Series Hands

Windows Phone 7 Series hands-on

Microsoft have officially launched Windows Phone 7 and with it the Windows Phone Series, promising we’ll see the first devices on the market in time for the holiday 2010 shopping season.  Windows Phone 7 marks a new, more end-user aware phase for the platform, with Zune and Xbox integration, together with stricter controls over the overall end-user experience: third-party UIs, such as HTC Sense, will not be allowed (though OEMs will be able to add into the new WP7 UI), and while they’re not yet revealing the details, Microsoft have a long technical specifications list for handset manufacturers that will better standardize the platform.

There are no handsets debuting today – though HTC, Dell and Qualcomm are among the manufacturers onboard – and all of the demo devices are unbranded, generic models specially built by ASUS.  Still, they’re a decent example of what we can expect: a large, multitouch-friendly capacitive touchscreen with a glass front, three front-panel buttons (back, Start and search), GPS and a rear-mounted camera.  They also have a front-facing camera, though there won’t apparently be support for it natively in Windows Phone 7, and OEMs are limited to what hardware controls they can add; it’s pretty much down to volume buttons, camera shortcut and power.

Windows Phone 7 Series hands-on video:

The on-screen Start button has been retired, and the hardware Windows flag now takes you directly to a blocky homescreen.  Each app on the screen is dynamically represented, pulling in the user’s content and constantly shifting; the gallery icon, for instance, transitions through recent shots from the camera.  Microsoft contrasted it to the iPhone OS, where the extent of user app customization is rearranging the icon layout on their homescreen; Windows Phone 7, meanwhile, pushes up constant reminders of the user’s content.  Swiping to the right, however, gives instant access to the entire app list, again something prompted by user requests for easier access.  Microsoft have created six “hubs” – People, Pictures, Games, Music + Video, Marketplace and Office – which collate similarly themed content.  So, the Music + Video hub resembles the Zune HD UI, and if you install a media plugin, such as Pandora (which Microsoft also announced today), it will integrate in here.  The Games hub links in with a user’s Xbox Profile, and you can modify your profile, view those of others, and play games (though Microsoft haven’t announced a list of titles yet).  The People hub pulls in updates from across the phone and various linked services – though we only saw Windows Live and Facebook mentioned – and you can post your own updates and read those of others.

Microsoft are retiring not only their “Windows Mobile” nomenclature but ActiveSync and any other desktop sync app they’ve used in the past.  Instead, the Zune desktop manager software is being rolled-out worldwide, and that will be used to sync Windows Phone 7 devices.  Both wired and WiFi sync will be possible, which is long-overdue.

Confirmed carrier partners includes AT&T, Deutsche Telekom AG, Orange, SFR, Sprint, Telecom Italia, Telefónica, Telstra, T-Mobile USA, Verizon Wireless and Vodafone, while manufacturers Dell, Garmin-Asus, HTC, HP, LG, Samsung, Sony Ericsson, Toshiba and Qualcomm are on-board.  Those partners won’t be able to change the onscreen QWERTY keyboard – which is one of seven layouts (including numeric) Microsoft have developed – but they will be able to add on a hardware QWERTY.  For the moment, since Microsoft have screen aspect ratio specifications, they’ll have to be landscape rather than portrait QWERTY devices, too.  In the display units Microsoft showed us, the accelerometer wasn’t working properly, but final devices will flip automatically between portrait and landscape on-screen keyboard layouts.

We had a chance to try out some of the prototypes – though not take photos or video yet – earlier on today, and first impressions are reasonably positive.  Microsoft were at pains to point out that it’s still an in-development build, and indeed we saw various bugs and slow-downs.  Often these would take place when opening an app, with data being pulled in but no on-screen indication of that taking place nor its progress.  The touchscreen on the development device seemed responsive, as was the onscreen keyboard, and the animations are smooth.  The browser supports pinch-zoom and will eventually reflow text on a double-tap.

Windows Phone 7 UI Demo:

Press Release:

Microsoft Unveils Windows Phone 7 Series

New phones designed for life in motion to debut at holiday 2010.

BARCELONA, Spain – Feb. 15, 2010 – Today at Mobile World Congress 2010, Microsoft Corp. CEO Steve Ballmer unveiled the next generation of Windows® Phones, Windows Phone 7 Series. With this new platform, Microsoft offers a fresh approach to phone software, distinguished by smart design and truly integrated experiences that bring to the surface the content people care about from the Web and applications. For the first time ever, Microsoft will bring together Xbox LIVE games and the Zune music and video experience on a mobile phone, exclusively on Windows Phone 7 Series. Partners have already started building phones; customers will be able to purchase the first phones in stores by holiday 2010.

“Today, I’m proud to introduce Windows Phone 7 Series, the next generation of Windows Phones,” said Steve Ballmer, chief executive officer at Microsoft. “In a crowded market filled with phones that look the same and do the same things, I challenged the team to deliver a different kind of mobile experience. We believe Windows Phone 7 Series is a phone that truly reflects the speed of people’s lives and their need to connect to other people.”

Designed for Life in Motion

With Windows Phone 7 Series, Microsoft takes a fundamentally different approach to phone software. Smart design begins with a new, holistic design system that informs every aspect of the phone, from its visually appealing layout and motion to its function and hardware integration. On the Start screen, dynamically updated “live tiles” show users real-time content directly, breaking the mold of static icons that serve as an intermediate step on the way to an application. Create a tile of a friend, and the user gains a readable, up-to-date view of a friend’s latest pictures and posts, just by glancing at Start.

Windows Phone 7 Series creates an unrivaled set of integrated experiences on a phone through Windows Phone hubs. Hubs bring together related content from the Web, applications and services into a single view to simplify common tasks. Windows Phone 7 Series includes six hubs built on specific themes reflecting activities that matter most to people:

* People. This hub delivers an engaging social experience by bringing together relevant content based on the person, including his or her live feeds from social networks and photos. It also provides a central place from which to post updates to Facebook and Windows Live in one step.

*Pictures. This hub makes it easy to share pictures and video to a social network in one step. Windows Phone 7 Series also brings together a user’s photos by integrating with the Web and PC, making the phone the ideal place to view a person’s entire picture and video collection.

* Games. This hub delivers the first and only official Xbox LIVE experience on a phone, including Xbox LIVE games, Spotlight feed and the ability to see a gamer’s avatar, Achievements and gamer profile. With more than 23 million active members around the world, Xbox LIVE unlocks a world of friends, games and entertainment on Xbox 360, and now also on Windows Phone 7 Series.

* Music + Video. This hub creates an incredible media experience that brings the best of Zune, including content from a user’s PC, online music services and even a built-in FM radio into one simple place that is all about music and video. Users can turn their media experience into a social one with Zune Social on a PC and share their media recommendations with like-minded music lovers. The playback experience is rich and easy to navigate, and immerses the listener in the content.

* Marketplace. This hub allows the user to easily discover and load the phone with certified applications and games.

* Office. This hub brings the familiar experience of the world’s leading productivity software to the Windows Phone. With access to Office, OneNote and SharePoint Workspace all in one place, users can easily read, edit and share documents. With the additional power of Outlook Mobile, users stay productive and up to date while on the go.


Founded in 1975, Microsoft (Nasdaq “MSFT”) is the worldwide leader in software, services and solutions that help people and businesses realize their full potential.

New Nvidia Shield Tv Hands

New NVIDIA SHIELD TV hands-on at CES 2023: all about that controller

My initial experience with NVIDIA SHIELD 2023 was a positive one. While the device does not – at first – seem to be all that different from its predecessor, several key distinctions make the release of 2023’s SHIELD an important one. One of these is the dedication to legacy users NVIDIA is showing with this new product – all software features and connectivity to new devices will be given to the previous model – it’ll work just as well as this new one.

I’m excited about getting to use this new NVIDIA SHIELD device at home, even if it has the same processor, RAM, storage, and connectivity as the previous model. This comes from my personal perspective as an every-single-day SHIELD user – I’ve been using the original NVIDIA SHIELD Android TV device since before its public release. After I reviewed the original NVIDIA SHIELD Android TV, I got one connected to my living room TV and haven’t stopped using it since.

This new model is not one that an original SHIELD owner will need to upgrade to – not unless they like the idea of a slightly smaller piece of hardware to set beside their television. Instead, people who already own an NVIDIA SHIELD Android TV will want to consider buying the new gaming controller on its own.

The new NVIDIA SHIELD may seem the same, spec-wise, but the hardware is quite a bit different. The device is now shipped with both the TV remote and the game controller – the new game controller. It’s important to note that the old NVIDIA SHIELD Android TV will work with the new controller, the new software, and the same apps and streaming capabilities.

So what’s new? The device is smaller – it’s about the size of a

journal now instead of the size of a DVD case. It’s right around the same thickness as its predecessor at its thickest point, and it retains the signature touch-sensitive on button and light-up SHIELD green checkmark on the box. The base is a bit different – it looks really similar as the design aesthetic of the hardware is similar, but the base is more rubbery-coated heavy plastic instead of metal.

This new controller now has vibration to roll with gaming immersion – stuff that rumbles in a game now rumbles on the controller. The controller now has an IR blaster to change volume on the base set, and all capacitive controls have been changed to analog, now there’s just capacitive volume control. The touchpad has been removed – since apparently not a whole lot of people used it.

This new controller no longer uses an LED behind its SHIELD button to notify the user when it’s active. The touch-sensitive SHIELD button has been replaced with an analog push button, and the controller is now turned active when the user lifts it from the table. The controller is said by NVIDIA to be “always on” when it’s working with Google Assistant – it’ll listen for your keywords “OK Google”, but otherwise it’s running with very little battery drain.

Of course we’ll have to test battery life for ourselves when we get our own review unit, but we’re crossing fingers for longevity. The device detects when the user has picked it up with an accelerometer.

Playing Titanfall II streamed from a high-powered gaming PC in the same room made for an impressive demo. The streaming capabilities of this console are just as excellent as they were with the previous NVIDIA SHIELD Android TV unit. The big difference here is in the controller – with Titanfall II, the controller rumbles.

LEARN MORE: NVIDIA SHIELD TV 2023 refresh detailed

Rumbling in the controller is a subtle difference makes the SHIELD feel a significant amount more like an Xbox One or a PlayStation 4. I hesitate to say it makes the SHIELD more like a “real” gaming console because the SHIELD is a very different monster. Every other feature will be available for the original NVIDIA SHIELD Android TV device because NVIDIA is cool like that.

The new controller will cost $60 USD purchased with NVIDIA online or through one of their retail partners. This device is currently on pre-order through NVIDIA – as is the first of two NVIDIA SHIELD consoles. The NVIDIA SHIELD TV device will come with both the standard controller and the gaming controller and will cost a cool $200 USD. There’ll be a larger version of the console for $300 that’ll have more internal storage as well – that’ll be available later this year.

Update the detailed information about C – It Role To Hands on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!