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Tag: Data science course

Do Data Scientist Use Object Oriented Programming?

Posted on by Imarticus Learning

It is estimated that there are 2.5 quintillion bytes of data produced every day in our world. In this data-driven world, the career opportunities for a skilled data scientist are endless. With the data production rate predicted to go higher than of now, the career opportunities for those who can manage data are not going anywhere. This article discusses whether data scientists are using Object-Oriented Programming. Let’s find out.

What is Object-Oriented Programming
Object-Oriented Programming or OOP is a model of the programming language organized around objects rather than the actions. It also emphasizes data rather than the logic. Traditionally, a program is considered to be a logical procedure that converts input data into output.

In such cases, the challenge was to come up with a logic that works. The OOP model redefined that concept. It takes the view that we should care more about the objects we are trying to manipulate rather than the logic we use. These objects could be anything from humans defined by names and addresses to little widgets such as buttons on the desktop.

The main advantages of OOP are:
• Programs with a clearer modular structure.
• Codes are reusable through inheritance.
• Flexibility through polymorphism.
• Very effective problem-solving.

Object-Oriented Programming in Data Science
Using Object-Oriented Programming for data science may not always be the best choice. As we said, the OOP model cares more about the objects than the logic. This type of approach is most suited for GUI, interactive application, and APIs exposing mutable situations. When it comes to data science, functional programming is preferred more due to superior performance than compared to the OOP model. The advantage of better maintainability offered by OOP is sacrificed in the data science for the sake of performance.

Polymorphism is an important feature of OOP. It allows a loosely coupled architecture, where the same interface can be easily substituted for different implementations. This feature is very helpful when dealing with applications of large size. However, data scientists seldom use large codebase. They always use small scripts and prototypes. So, OOP would be far too much overhead with no significant benefits.

Although, machine learning libraries are a must needed thing for data scientists. Most of these libraries make use of object-oriented programming, at least the ones in Python. Machine learning libraries such as Scikit-learn heavily make use of OPP. Data scientists who work with R and SQL will never have to use OOP.

Conclusion
It is clear that even though Object-Oriented Programming Offers a lot of benefits, it is not exactly what data science need. So in general, object-oriented programming is seldom used by the data scientists.

If the data science career seems to suit you, wait no more. Imarticus is offering courses on data science prodegree, which will provide you with all the skills and knowledge to excel in your career. This Genpact data science course allows you to start your journey on the right foot with placement assistance at so much more.

What is a full-stack Data Scientist?

Posted on by Imarticus Learning

 

The world of facts, figures, data, numerics, statistics and other technical information needs an artful collection, collaboration, processing, collating and analysis.

Full stack data is what happens when any data gets collected, analysed and applied for all purposes.  The process helps in visualizing the entire stack of data is a systematic manner.

Data stack science is a broad field where statistics and other kinds of information get scientifically analysed and applied. This field is substantially used to management, business and scientific or technological dealings and aspects.

A person with specialist knowledge of numbers, data collection and research can be generally called data scientist.

Data sourcing, researching, stacking, systematization and application is also applied in fact analysis, machine learning, engineering and other technical studies and training.

Imarticus provides high-quality training and education in all fields requiring full data stacking science. The essential ropeworks are taught in a large number of courses offered both online and on-campus sites, such as Pune. 

Data science stacking training can help students get training and specialization in several management and technical skills. Firstly, the learner can understand the basics of management and business.

He can get to understand action taking, decision making and applying execution skills to the application of data. He can be trained to utilize information to maximize profits, to make smarter uses of information and to select and analyze information with relevance and accuracy. 

Data science knowledge is crucial in setting up new business ventures or making new deals. Action plans need to be made with efficiency and with originality. With fact and data stacking, and without a proper understanding of data collection and research, everything can be futile.

Imarticus takes pride in helping students begin their careers in technology, data science and other related fields, The institute offers projects, mentorships and other opportunities as well. The institute believes in the motto ‘learning by doing’. In other words, training is being provided in a practical and hands-on manner.  

The Data Science course is offered in collaboration with Genpact, the Global leader in analytics. The online and offline classroom experience includes 200 hours of training, work experience with renowned companies and projects and other opportunities. Courses cover all topics relevant to the data science, statistics and technological data analysis.

The Imarticus website (www.staging-imarticus.kinsta.cloud ) will provide all the details about the courses and how to apply. Several case studies have also been added for glimpses into what is expected to be taught and understood.

On completion, industry recognized certificates will be provided. The added advantage of being associated with Genpact can be the sure way to get into this field.  All professionals experienced and novice, are welcome into the programme. 

How do Data Scientists Use Programming?

Posted on by Imarticus Learning

 

There are few fields in the world which have grown in stature as data science has in recent years. As a discipline, the growth of data science has been unparalleled – machine learning and AI are now implemented in a host of large organizations around the world. 

With every passing second, new data is created by millions of users around the world. Companies try to leverage this data by analyzing it, and data science is used to get business value from the findings. This means that using data science as both a business tool and a product, a number of companies have made business decisions which have made them grow quicker than ever. 

With the rise of data science, the demand for capable data scientists has also improved. It is now one of the most lucrative career options and one which is extremely accessible too. However, how do you become a data scientist, and is programming truly required for being successful in the discipline?

The Importance of Programming for a Data Scientist

Programming remains to be one of the main technical skills which any data scientist requires to succeed. In the initial stages of learning, one does not need to know much programming. This is because the focus is one using the tools at hand to implement algorithms, in order to analyse the data at hand. 

However, when you grow in your career, you will find that you need to modify algorithms or even create new ones for analysis. This means that you will have to modify the codes behind them, or even write a new algorithm from scratch. This requires you to have a command over at least one programming language – be it Python, R or Java. 

Knowledge in programming and statistics can go a long way in helping you implement your own algorithms and analyse the data in a more efficient manner. It will help you manipulate the data set, and bring any unconventional ideas you have to life. If programming is something you can’t do, you will be forced to use the tools at hand without the option of trying to change those tools themselves. While you may be able to manage with this, it will have an impact on your career in the long run. 

Can Data Scientists Be Good Without Programming?

Of course, if you do not know how to program, the obvious thing would be to start learning it since it can improve your career over the long term. However, there are some other ways in which you can excel at your job too. You should try and master a GUI tool, like Tableau so that you are able to visualize your data in a much more efficient manner. You should become a storyteller, and must be able to convey the story the data tells you in a concise manner to the client. Excelling at sales would also help, since you may have to interact with the client a lot. 

If you find yourself interested in machine learning and data science, you should definitely check out the data science course at Imarticus Learning. It is one of the best data science training in Banglore. 

What is a Data Scientist Internship Like?

Posted on by Imarticus Learning

 

Over the past few decades, Data science has emerged as one of the most admired career fields in the world. Currently, it is estimated that 2.5 quintillions bytes of data are produced every day. The value is expected to keep growing and growing in the future. With such a forecast, the high demand for skilled data scientists is expected to stay. There are various sources to acquire data science skills and knowledge. But it is always better to have a real-life experience before the actual job.

The internships are the best source of real-life experience for any profession and the same goes for data science too. This article describes a typical data scientist internship and provides you with a basic idea about it. The actual experience may vary according to the company you go for.

Messy and Complicated Real Life Data Mining Projects
 If you get to work on any data mining project, don’t expect it to be anywhere near the problems you faced in the classroom. The projects you get will be messy and complicated, unlike the controlled environment in the university lecture.

However, with the help of your teammates, you will be able to do all the complex work. It will help you improve your mining skills and provide you with a taste of real-life mining problems. Before heading to an internship, make sure you are equipped with the right level of skills for such messy data.

Being A Trusted member of the Team
 Most companies provide you with excellent exposure and guidance. You will be taken to many meetings and entrusted with various details. This is intended to inspire you to perform better. The meetings you attend within the several departments will help you to understand how the business runs and how departments are interacting with each other. This knowledge is vital to business understanding.

Developing the Essential Data Science Skills
 You will be facing numerous challenges at each stage of every project. It will lead you towards the skills paramount for a career in data science. You will be required to engage with various staff for information and it adds to your communication skill. Through various projects, you will gain experience in many aspects of organisational operations and project management. Some of them are listed below.

• The need for business understanding
• Importance of feasible project plans and aims
• Value of correct data collection methods
• The need for documentation of a project to make it transparent and repeatable
• Importance of having iteration and feedback from the team to ensure the project progress.

During a Data Science Internship, you will gain very valuable technical experience in various segments of data science. The consultancy experience you obtain through tackling real-life problems is also very vital to your data science career. To equip you with all the necessary skill sets to take on such a career, Imarticus is providing a data science prodegree. This Genpact data science course will help you start the data science career on the right foot.

How Can You Start Learning Data Science and Become a Master in it?

Posted on by Imarticus Learning

 

Being a new and fast-growing field, Data Science is in desperate need of skilled individuals. With lucrative opportunities and pay scales, enterprises around the globe have been in search of skilful professionals to work for them.

You too can make use of this possibility and have a career of your dreams. But becoming a data scientist isn’t an overnight thing. It takes time and effort. So, How do we start to learn data science at right foot? We will find out.

Following are the few steps you could follow to learn data science.

  1. Find If Its Right for You
    Before fixing on to this career choice, you have to make sure you are totally interested in this. You can ask following questions yourself to find if its right for you.
    • Do you really enjoy programming and statistics?
    • Are you willing to work in a field where you have to learn about the new techniques and technologies constantly?
    • Are you okay with job titles like Data Analyst, business analyst etc. ?
    If you have yes for an answer, then you can start learning Data Science right away.
  2. Mathematics
    You have to get familiar with a few topics in Maths in order to conquer data science. The main topic you need to study are the following
    • Probability – A lot of data science works are based attempting to measure the probability of events. Textbooks are a good source of information for this subject.
    • Statistics – This branch of mathematics deals with interpreting and analyzing the data. Fortunately, great textbooks are available online for you to refer.
    • Linear Algebra – This branch of maths covers the study of vector spaces and linear mapping among this space. Linear algebra is a must to understand how machine learning algorithms work.
    Once you are familiar with programming and various libraries, you may not have to dive deep into these mathematical details. But to understand them properly, you will need a sound base in these mathematical topics.
  3. The Programming
    Data Science community has chosen Python and R as their primary languages for programming due to various advantages. You have to learn and practice programming in these two languages at least for the following topics.
    • Data Analysis – NumPy and Pandas, are the two common libraries used for data analysis in Python. Tidyverse is a popular compilation of packages in R for data analysis.
    • Data Visualization – Matplotlib is the most used data visualization tool in Python. The most popular plotting library in R is ggplot2.
    • Machine Learning – Python mostly make use of SciKit-learn library to do the machine learning works. When it comes to R, it offers a huge variety of packages including CARET, PARTY, random forest and many other.
    When you complete these steps, you have a solid base required for a Data Scientist. Even if you find it hard to learn all this stuff on your own, the courses on data science prodegree by Imarticus is available to help you master the Data Science. The course provides comprehensive coverage of statistics and data science along with hands-on training on the leading analytical tools. so, stop wasting your time start preparing for your data science career right away.

How is MySQL Used In Data Science

Posted on by Imarticus Learning

Data Science is considered to be the most sought-after profession of the 21st century. With lucrative opportunities and large pay scales, this profession has been attracting IT professionals around the world. Various tools and techniques are used in Data science to handle data. This article talks about MySQL and how it is used in data science.
What is MySQL
In short words, MySQL is a Relational Database Management System or RDBMS that use Structured Query Language (SQL) to do so. MySQL is used for many applications, especially in web servers. Websites with pages that access data from databases use MySQL. These pages are known as “Dynamic Pages” since their contents are generated from the database as the page loads.
Using MySQL for Data Science
Data science requires data to be stored in an easily accessible and analyzable way. Even though there are various methods to store data, databases are considered to be the most convenient method for data science.
A database is a structured collection of data. It can contain anything from a simple shopping list to a huge chunk of data of a multinational corporation. In order to add, access and process the data stored in a database, we need a database management system. As mentioned MySQL is an open-source relational database management system with easier operations enabling us to carry out data analysis on a database.
We can use MySQL for collecting, Cleaning and visualizing the data.  We will discuss how it is done.
1. Collecting the Data
The first part of any data science analysis is collecting the massive amount of data of data. The Sheer volume of data often causes some insights to be lost or overlooked. So, it is important to aggregate data from various sources to facilitate fruitful analysis. MySQL is capable of importing data to the database from various sources such as CSV, XLS, XML and many more. LOAD DATA INFILE and INTO TABLE are the statements mostly used for this purpose.
2. Clean the Tables
Once the data is loaded to the MySQL database,  the cleaning process or correcting the inaccurate datasets can be done. Also deleting the dirty data is also part of this step. The dirty data are the incomplete or irrelevant parts of the data.
The following SQL functions can be used to clean the data.

  • LIKE() – the simple pattern matching
  • TRIM() – Removing the leading and trailing spaces.
  • REPLACE() – To replace the specified string.
  • CASE WHEN field is empty THEN xxx ELSE field END  – To evaluate conditions and return value when the first one is met.

3. Analyze and visualize data
After the cleaning process, it is time to analyze and visualize the meaningful insights from the data. Using the standard SQL queries, you can find relevant answers to the specific questions.
Some analysis examples are given below:

  • Using query with a DESC function, you can limit the results only to the top values.
  • Display details of sales according to the country, gender or product.
  • Calculate rates, evolution, growth and retention.

If you would like to know more about MySQL and its use in Data Science join the data science course offered by the Imarticus. This Genpact data science course offers a great opening to the career opportunities in Data Science. Check out the course and join right away.

Is Data Analytics An Interesting Career Field?

Posted on by Imarticus Learning

One of the biggest job sectors of the last few years, data analytics is seen as one of the most lucrative career options today. In the United States, an estimated 2.7 million jobs are predicted to be taken by data science and analytics by 2020. The value that big data analytics can bring companies is being noticed and companies are looking for talented individuals who can unearth patterns, spot opportunities and create valuable insights.

If you’re someone who’s good at coding and looking to make the next jump from a career perspective, then data science could be your calling. Here are a few reasons you should look out for a career in data analytics:

Higher Demand, Less Skill:
India has the highest concentration of data scientists globally, and there is a shortage of skilled data scientists. According to a McKinsey study, the United States will have 190,000 data scientist jobs vacant due to a lack of talent, by 2019. This opens the door for a good data analyst not just to make money, but own the space.

Good data analysts can take complete control of their work without having to worry about interference. As long as you can provide crucial insights which contribute to the company’s business, you’ll find yourself moving up the ladder faster than expected.

Top Priority in Big Companies:
Big data analytics is seen as a top priority in a lot of companies, with a study showing that at least 60% of businesses depend on it to boost their social media marketing ability. Companies vouch by Apache Hadoop and its framework capabilities to provide them data which can be used to improve business.

Analytics is being seen as a massive factor in shaping a lot of decisions taken by companies, with at least 49% believing that it can aid in better decision making. Others feel that apart from key decisions, big data analytics can enable Key Strategic Initiatives among other benefits.

Big Data Is Used Almost Everywhere:
Another great reason to opt for big data or data analytics as a career option is because they are used pretty much everywhere! With the highest adopters of the technology being banking, other sectors which depend on big data include technology, manufacturing, consumer, energy and healthcare among others.

This makes big data an almost bulletproof option because of the wide range of applications it can be used for.

Most Disruptive Technology In The Next Few Years:
Data analytics is also considered as one of the most disruptive technologies to influence the market in the next few years. According to the IDC, the big data analytics sector is touted to grow to up to $200 billion by 2024.

Thus, big data analytics is going to be the future of computing and technology. The sector is seeing massive growth and a lot of demand. The more you’re able to provide insights that can make a difference in this sector, the higher are your chances of getting a lucrative job.
Whether it’s a data analytics course in Bangalore or any other city, Imarticus will be able to provide you with the right kind of training and knowledge with data analytics courses to help your career soar.

7 Awesome Lessons You Can Learn From Studying Data Science

Posted on by Imarticus Learning

Data archives have increased exponentially, and it’s posing great challenges to various industries. Fortunately, they have gone beyond ‘What is Data Science?’ to finally adopt Data Science analysis, to derive something meaningful and productive from the existing pile of information.
Today there are many takers for data science training, and people have learnt some important lessons. Let’s discuss some of them:   
It’s important to understand business as a whole
At times people give too much emphasis on technical knowledge and out the domain knowledge on the backburner. This way they end up creating a sophisticated model without really understanding the business needs. Such models don’t add much value to the business, regardless of their accuracy.
As a data scientist, one needs to understand a business through the eyes of data. Only having the technical knowledge won’t help you articulate your ideas to colleagues in the context of business. So, besides Jargons, it’s important to learn the commonly used terms pertaining to a business.
It’s important to have a penchant for details
Data scientists can’t carry out data cleansing and transformation without having an eye for details. Data in real-world scenarios is never arranged perfectly, and one needs to isolate a lot of noise from it, to arrive at something meaningful. So, a detail-oriented mindset is a must to succeed in Data Science. Without that, you may not derive insightful results from your Exploratory Data Analysis. You may put your heart and soul into the data cleaning process, but still the data might not be reliable enough to be used by your Model.
Framing logic and designing an experiment
Machine learning problems are not that complicated, as you just need some data for training purpose in order to build your model. In case of Data science, there is a well-structured workflow that provides a larger picture of the undergoing processes (Data cleaning to Model interpretations). There is a component called Experiment which is a part of the workflow. It includes the logic for hypothesis testing and Model building.
Therefore, data science helps in framing a logic and designing an experiment for real-world scenarios, to test certain assumptions and evaluate the Model. You can understand more about this aspect by opting for a Data Science course.
Communication skills
If you are a Data Scientist, you better enhance your communication skills, as it will help you sail through. As mentioned earlier in the write-up, there is no point in acquiring the technical knowledge and crunching the data all day long, if you can’t communicate your ideas to stakeholders in a business-friendly language. This affects your credibility as well as your professional relationships. In short, it’s a lose-lose situation.
As a data scientist, your biggest challenge is to put forward your most complex ideas and insights in a layman’s language, so that even a 15 years old can understand them. Your language should make your colleagues feel empowered, so that they can invest emotionally and intellectually.
Art of Storytelling
If you think that Data Science is all about crunching data and building models, then you are mistaken. It’s also about weaving a compelling story based on data analysis, to indulge the stakeholders. Depending on the project goals, the story should cover the following questions:
> What’s the reason to analyze the data?
> What insights can be obtained from the results?
> Can any action plans be derived out of the analysis?
Often the art of storytelling is ignored over data-driven analysis. Lousy storytelling or boring presentations, can greatly undermine the valuable results from even some of the best models.
It’s important to set a benchmark for comparison
It’s naïve to assess the efficiency of a Model without comparing it with other models. Without a benchmark, it always difficult to define ‘What is good’, and the results can’t be fully trusted.
Art of Risk management
Every Model is built, keeping in mind the best and the worst-case scenarios. You are required to explain your model’s limitations to stakeholders, and how much risk can the company potentially bear if the model goes to production. This is where Risk Management comes into picture. If you understand what’s at stake and have a plan to minimize the risk involved, then only you can take stakeholders into confidence. To understand more about the art of risk management, you may opt for a Data science course.
Intensive Data Science training can help you realize the above-mentioned lessons. If you are an aspiring Data Scientist, we hope this write-up served the purpose.

Why is Data Science So Famous?

Posted on by Imarticus Learning

Data Science is clearly the way to the future and is revolutionizing a number of fields across industries. In just a few years, it has emerged as the most sought-after career route. In spite of all the hype, a lot of people end up asking ‘What is data science, exactly?’

Data analytics is basically the examination of data, and a data science course mainly equips young tech enthusiasts to sift through huge amounts of scrambled data to process them and extract information out of them.

From healthcare to politics to disaster management, data science is making way for breakthroughs all over. Celebrated computer scientist Jim Gray considered data science to be a fourth paradigm of science, and insisted that information technology is changing everything about science.

Decades after his prophecy, he stands corrected, and a career in data science is one of the most lucrative career aspirations you can have. But it is very important to know exactly what data analytics does, and how it is changing the world around us.

So, what is data science and why exactly is it the hottest career right now?

Did you know that according to the company review website Glassdoor, data science was the highest paid field in 2016? Glassdoor is basically a platform where employees can rate their workplace and its management. The 2016 report was actually based on reviews of the people in the data science field, and their income growth. The survey also took into account the possibilities for career growth of the people working in the field.

You must understand that a data science course consists of a number of skills which the aspirants must marvel at, like programming, statistics, coding etc, and this makes their skill set a very coveted one in the field of analytics. Data science training is still mainly about about figuring out trends and patterns based on statistics and jumbled data. More and more companies are hiring data scientists for strengthening their analytics team, which is why the data science field is such a lucrative one.

In the field of artificial intelligence (AI), especially, data science training is an invaluable asset. You must have heard about the exponentially growing influence of AI in today’s industries. Every major company is seeking data scientists who specialize in AI. To put it simply, a successful AI assignment is not possible without the right data, which is extracted by a data scientist and processed to their advantage.

Let’s look at other simple examples. Take for instance, companies like Google. Their operations and service depends almost entirely on successful data analysis. Are you aware that the HR department at Google has completely changed the game for other corporate companies, when it comes to perfecting work culture?

They have moved to a form of data-based employee management, where they sift through data and process it to make the company a better place to work for their employees. As a result, research has shown that Google is the best corporate company to work in the world right now.

Some of the best and the most successful companies in the world are investing millions of dollars to amp up their data science branch, and this hardly comes as a surprise in the era of information technology. Even small businesses need to study data and the statistics of the market before they can launch their products.

Not to mention a data scientist earns substantially better than his counterparts in other sectors, and it can only get better if the pattern is followed. Research has shown that the median salary of the data scientist is something around $110,000. In a couple of years, a data scientist with only a few years of experience will not have to look around for long to get better opportunities, considering the boom in the field and the overriding necessity of data analytics.

How Beneficial is Data Science Prodegree For Your Career?

Posted on by Imarticus Learning

Data Science is one of the most sought after career tracks at the moment. There is a reason that the hype on data science exists. The fundamental focus of data science is that it assists human being on taking better decisions, quicker decisions. And it’s not that this is a requirement of only a handful of industries from a particular segment. This is true across industries, even where decisions are automated for e.g. in online shopping, retail etc.,

There is a rapid growth in the data science field. Its prominence is directly proportionate to the record level of increase in the raw material i.e. structured and unstructured data.

There are a number of other factors that are adding significance to this field. The number of sensors that accumulate information like internet, phones etc.., along with advanced and sophisticated machine learning techniques that help give better insights with the help of better extraction algorithms.

All these forces are working in one direction, the direction to ensure that the skills of using available data to extract actionable insights for business to impact better decision making which in turn will impact the revenue of the company is here to stay. Recognising this most MBA’s have also introduced Data Science into their MBA curriculum.

What skills does one learn in order to become an effective Data Scientist?

Large bits of unstructured data are not easy to interpret, one needs a unique skill set, one needs to develop useful auxiliary skills, some technical attributes required to apply is the top line. One needs to create a perfect balance of various skills. Predictive modelling, analytics, organisation skills and above all communication skills.

Besides the above to be able to secure a lucrative job in the organisation of your choice one needs to develop excellent and valuable coding skills. Efficiency in SAS Statistical Analysis System, R programming language, Python programming language etc.., further aids your skills as a data scientist or analyst. It helps you to think logically in terms of algorithms, which in turn allows you to better manage irrelevant data.

Another additional set of skills that are essential to have academically and through experience are contextual understanding of possibly any given situation, skills in probability and statistics. And finally the most important of all the skills is the ability to communicate, explain, in the method and language of the audience, your findings. So storytelling and presentation skills become imperative.

Why Data Science Prodegree at Imarticus Learning?

To begin with the Data Science Prodegree at Imarticus is designed in association with Genpact as the knowledge partner. It essentially covers all foundational concepts and offers hands-on learning of leading analytical tools such as SAS, R, Python, Tableau etc., and the learning is integrated with relevant industry case studies and projects, which is essential in gaining in-depth problem-solving capabilities.

The course is divided into four semesters and is focused on ensuring that the candidate not only gain the theoretical knowledge of the tools but also learns best industry practices and business perspectives through live interaction with the gurus of the corporate world through guest lectures and regular project submission.

To ensure maximum learning efficacy the course ranges over 200 hours and is delivered in two modes, online and classroom. The course offers career readiness assistance too, at Imarticus the Career Assistance Services provides you customized industry specific mentorship, with assistance in resume building workshops and one on one mock interviews.

The Data Science Prodegree is a power packed course endorsed by Genpact, which has a comprehensive coverage aided by project based learning, with effective and efficient program delivery along with career assistance. Thus preparing you to confidently apply your newly learned skills and excel in your given role right from day one, making you a sought after data driven decision maker.

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