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Big data assignment help for college and grad school students
Big data is just moving large amounts of data around. This can be done with the internet or other digital networks and it also can be a way to describe the data itself. Big Data is also used in many fields of study such as medicine and social science, but for this post, I will be focusing on big data related to marketing and advertising.
Big data comes from different places. For example, an internet search engine can track all of the searches someone does on their site. Then with this information, the company can target users by showing them ads related to their searches. All of the companies that are connected and share data on the internet are creating more big data every day.
Big Data also has many threats when it comes to privacy issues. According to a recent report from the World Economic Forum, “users are not always aware that their data is being used”.
The United States has laws protecting users from this type of invasion in the privacy, but many other countries don’t. The report also states that there are over ” 1,500 files containing health information pertaining to 500 million people” which can be accessed for no charge. Those people have no idea that their private medical records are on the internet.
Big data is used in many different fields such as:
- law enforcement,
But like any new technology, there are some pros and cons to it.
Pros of Big Data:
For businesses, big data can help them better understand their customers and how to build a better product or service.
With the internet, companies can collect information about you from the websites you visit, what sites you buy products from or what ads you click on.
This allows businesses to see where their strengths are in comparison with other products. Big data is also used for research purposes.
Big Data has many pros, but there are also some cons.
It can cost a lot of money to store and process big data. Plus it can sometimes take too long to figure out what is being asked. For example, if someone wanted the average age of people who bought this product or that one over the last five years, it would probably take quite a while to figure out.
Cons of Big Data:
There are also many threats when it comes to privacy issues about big data.
Often people do not know that they are being tracked online and their information is being collected by companies.
There have been some instances where government officials were using technology to track another country’s citizens, which was one of the main reasons for Facebook buying up WhatsApp and Viber.
According to a recent report from the World Economic Forum, “users are not always aware that their data is being used”.
This type of invasion into people’s privacy can cause serious issues in other countries where there are no laws protecting internet users. Even if you’re in the United States, there is no way to know if you are being tracked online.
To prevent this from happening companies such as Facebook and Google have services that will delete your search history or internet activity.
Maybe big data can make our lives easier in some ways, but it has its downsides too.
Who should be concerned about their privacy when collecting or using big data?
Everyone. However, if you don’t really care about your privacy, then you can do nothing and not worry; Big Data probably won’t affect you. But if you value your privacy and are concerned about how much information companies know about you, there are ways to protect yourself from being tracked.
You can use a Virtual Private Network or VPN to encrypt your internet traffic. This prevents companies from being able to see what you’re doing online, making it harder for them to collect any information on you.
There are also many ways to delete your information yourself like social media and search engines.
Big data is a very useful tool that can be used for many different things, but it also comes with its own privacy issues.
Characteristics Of Big Data in college assignments
Big data is a very broad term and there is no single definition of it. However, the concept of big data can be explained through various characteristics.
Historically, most databases were structured and the information collected was in mainly tabular form. This allowed for easy retrieval by defined key fields.
The type of analysis that could be carried out using this kind of database was generally limited to what could be achieved with simple tabulation e.g., averages and counts.
With big data, the amount of information being stored is so large that traditional methods for processing, analyzing and visualizing must be abandoned or augmented due to their inability to handle such massive amounts of information effectively/reliably
. By volume we mean the enormous amounts of diverse data sources that are continuously being generated by millions of devices and sensors.
By velocity we mean real-time access to large volumes of newly generated information which arrives at extreme speeds, due to which traditional technologies fail to handle such enormous speed effectively. With the advent of high frequency trading , the need to process extremely large volumes of data each nanosecond, and other real-time business applications, velocity has emerged as an important characteristic feature of Big Data.
By variety we mean that the amount of information being produced by various data sources is very vast due to which traditional technologies cannot be used effectively in analysis and visualization.
The sheer volume and rate at which digital information is growing makes big data a key factor for companies seeking competitive advantage. Businesses are drawing on all kinds of new data – from the sensors embedded in mobile devices or the Internet of Things (IOT) to “traditional” structured and unstructured transactional records stored in enterprise databases – to create comprehensive customer profiles, understand changing market conditions, anticipate shifts in demand and supply, engage with consumers on a one-on-one basis and empower employees to make smarter decisions.
Big data is not just about capturing every detail of an organization’s operations; it is also a valuable source for intelligence gathering on competition and the outside market. With more organizations collecting more kinds of information than ever before, companies are gaining deep insight into customer behavior, pricing trends, purchasing practices and distribution channels. The ability to analyze this new breed of big data quickly leads to actionable intelligence that can be used by any business from Wall Street banks to grocery stores.
Another important characteristic feature of Big Data is variety. For example, the amount of information being produced by various data sources is very vast due to which traditional technologies cannot be used effectively in analysis and visualization.
Examples of big data assignment help projects
An example of variety is the massive amounts of information generated in social networks, where there are numerous interactions between users (known as friend requests or updates) and each user has a rich set of interests reflected by the pages to which he/she subscribes and posts. The task of understanding such datasets requires novel techniques that extend traditional data mining methods.
Another example is video surveillance , where even when a video provides only partial views on an event, we still need to understand what happened before and after these events to either confirm or reject them. Traditional algorithms for mining structured patterns from time-stamped sequences do not easily extend to semi-structured Big Data because temporal ordering is one way they are able to ensure unique patterns.
A store of unstructured data, such as a video surveillance dataset, does not easily fit into the existing database model because it is semi-structured and therefore cannot be described by the schema that we have been using for databases since 1970. Furthermore, a single image may contain multiple regions of objects where each region constitutes a separate event of interest. The same applies to text documents (digital books are composed of chapters and paragraphs), which need to be parsed before they can be analyzed. This means all Big Data should be stored in forms that make them easy to process.
But then how do we deal with so much variety?
We cannot use conventional techniques given their lack of scalability when processing big. In addition, we will need to deal with uncertainty of information, particularly if there are gaps in data.
To be able to extract knowledge from this vast amount of data we need methods that can scale and provide us with answers even when only partial information is available.
Scalability is one of the most problematic issues faced by those managing big data.
Data mining today mostly relies on existing database technologies (relational databases) and parallel/grid-computing frameworks such as MapReduce, which have been designed with transaction processing in mind for querying large amounts of structured data.
Such a framework cannot support the variety of Big Data or its associated uncertainty since it places severe restrictions on what models can be used (predicate logic) and how they should be expressed (relational algebra) and processed (sequential scan).
Furthermore, existing statistical methods have been designed to deal with traditional data sets, which can fit into memory or are at least not too large that they cannot be stored on a single computer system.
Many of these methods scale poorly with increasing dimensionality , so as information grows larger it becomes more difficult to apply them effectively. In addition, most approaches assume independence between attributes in the data when this assumption is violated by big data or structure in the underlying dataset we need new models and algorithms .
Finally, many common mathematical tools for processing scientific data such as matrix algebra and complex numbers cannot work with Big Data since some of their operations require linear scaling with respect to input size. and therefore cannot work with Big Data since some of their operations require linear scaling with respect to input size.
Big data is therefore defined as three V’s:
- volume (of the data),
- variety (in how it is stored and presented)
- velocity (how fast it changes).
However, they are not independent since there are often significant constraints on the values that might be associated with a specific piece of information.
These distinctions arise because in many situations we do not know what kind of data we will end up with, even though we may know its overall structure or use cases.
As new types of sensors evolve over time such as RFID tags , cameras, smartphones and tablets can collect increasingly more data from different parts of the world in real-time or near real-time. This increases variety over time and adds to the volume of data.
Despite their large size, many big data sets are not static but have a ‘streaming’ nature with continuous updates as in Twitter feeds and stock market prices.
In this case the velocity is so high that we cannot store all the information in memory or even on disk, which means that we need to design algorithms and systems that can process it effectively when there is no guarantee of completeness of information.
Data from different sources often exist independently, sharing only some characteristics such as format or structure.
For example, information stored in flat files (CSV/XML), relational databases (SQL) or NoSql databases like Hbase or MongoDB. The concept of dimensionality in big data arises if the various data sources have different ways of representing them. To be effective we need to transform data from a variety of sources into a single unified form such that they can then be processed together by the same machine learning methods , which are often represented in vector space .
How do we cope with uncertainty?
Since many big data sets are incomplete, noisy and temporally dynamic, it is only natural that our model’s predictions should also exhibit those characteristics. It is impractical to assume that measurements are precise, particularly since many sensors gather their information remotely from locations which may not always provide accurate readings. Furthermore, changing circumstances might cause models to become obsolete quickly so there needs to be way to incorporate new data in a timely fashion, whether they are accurate or not.
How do we deal with volume in big data assignments?
Big Data are so large that traditional systems cannot handle them and thus need to be processed on different architectures.
We also generally have no idea how many iterations will be needed to reach an optimal level of accuracy since in practice it is hard to test our current rules against future data. This leads us to the consideration of parallel processing.
However, most traditional computing environments such as personal computers and mainframes force us into serial execution which limits our ability to exploit these resources efficiently., and therefore cannot work with Big Data since some of their operations require linear scaling with respect to input size.
Therefore, we need more powerful machines like clusters or even grids of processors. In addition we need to re-imagine the algorithms and software that do not rely on sequential programming but can work in parallel as well.
Another concern is that if we make use of big cluster computers, we effectively shift the issue of scalability from one level – data – to another – compute time or number of nodes.
Cluster computing offers much better price performance but requires careful execution so that all processors are used effectively since idle resource will incur unnecessary costs.
Finally, modern applications require a new type of hardware called GPUs (Graphical Processing Units) which are designed for high throughput computations. They have hundreds to thousands of relatively simple processing cores that perform arithmetic operations on large sets of data simultaneously without requiring any kind of memory access which makes them more efficient for parallel computing.
In general, GPUs are much better at performing some types of computation than CPUs (Central Processing Units) since they can execute these operations faster because the memory is attached to the core rather than being external like in CPUs..
How do we extract meaningful features of big data?
The entire premise of Big Data analysis is that correlations between different variables might not be apparent using traditional methods or databases which have limited capacity and rely on queries that reveal only one variable per query.
Many real-world data sets contain various kinds of relationships such as dependencies between attributes or even higher-order interactions which indicates that certain combinations can lead to a particular behavior or attribute.
This implies the need to identify a set of important features from a big data set before further analysis can be performed.
Many new kinds of data mining algorithms have been proposed to deal with this problem.
Cheap Big Data Analytics Assignment Help By Professionals
Big data refers to the combination of structured, semi-structured and unstructured, yet large volumes of data. This data can be mined by companies and used for their decision making processes.
The mined data is analyzed in a process known as big data analytics. The need for more professional to help organizations with their big data issues has seen a rise in number of students.
To adequately prepare them to help businesses with their decisions, professors give big data analytics assignments. These papers are not very easy to handle due to the dynamic technological advancements.
Learners end up looking for big data analytics assignment help to boost their skills and knowledge on the same. More importantly, they need good grades and ample time to practice and participate in other activities.
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Hiring someone to complete your assignment should be a careful undertaking. It can either build or destroy you depending on the personnel you use. However, we never take chances with any of our clients. We work with the most qualified and highly trained data scientists. When it comes to them, there is no guesswork involved, since they understand the concepts well. Our big data analytics assignment services are always available to the customers who seek them. We have samples that can help you see the type of papers you get from our experts. No topic is ever too difficult, nor data too complex for us.
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To understand why your professor assigns you some tasks, your knowledge on importance of big data is crucial. You may even get a question that asks you to describe these uses. Below, we highlight a few of them;
- Business development
For any business to continue operating indefinitely, it has to make the right decisions. The customers, suppliers, distributors and even community have to be well understood and taken care of. Information on all these parties could comprise the big data that these companies keep re evaluating. Our big data analytics assignment help equips you with the skills applicable in real life situations.
- Individual development
Even with its immense impact to organizations, big data analytics is also applicable on individual levels. Healthcare, financial institutions and travel companies collect information on individuals. This data is useful in planning and managing the individual needs of each of these persons.
- Improving government services
The government needs big data analytics to evaluate and make decisions on various issues. They can identify if there is need for social amenities, like transport systems, hospitals and schools. Large sets of information are stored about a country’s individuals and welfare. You can ask for our big data analytics assignment help for more information regarding the uses, application and importance.
Get help with big data assignment projects in different techniques
Database, dataset, and algorithms are some techniques we can offer big data analytics services. We also have experienced scientist to handle IDE tools, Hadoop, file systems, computing and management. By virtue of volume, velocity and variety, we help you determine whether your data set qualifies to be classified as big data. Contact us with any assignment that needs expert opinion.