If you are a computer science student or someone who is in the field of development, you must have been aware that Data Science is bringing out a drastic paradigm shift in the world today. Data is also being referred to as the new oil, as it is so valuable. Thus, Developers and computer science students may get copious amounts of assignments of data analytics and data science. If you have been overwhelmed by the pressure of assignments, then My Assignment Services can connect you to Data Science experts that would provide you Data Analytics assignment help. Our experts are individuals that invest themselves in cutting edge research. Here, we connect you to these experts that would do your data analytics assignment for you.
Fundamentally, Data Science could be understood as the science of investigating, inspecting, cleaning and analysing the raw data to derive actionable knowledge out of it These techniques are called algorithms through which the programs automatically execute the actions on a raw data set to derive the knowledge for human consumption. For example, trends in business, economy or finance, or even implementing algorithms to predict the score of a football match.
These techniques of Data Analytics could potentially predict the kind of trends that would otherwise be impossible for human intelligence to notice. The derived information is finally used to optimize business processes, preventing risks and expediting growth.
For example, it is a routine procedure for a manufacturing company to often record the runtime, work queue or downtime for numerous machines and then this data is cleaned and filtered by an algorithm to further analyze it to generate reports. With these reports, machines can operate on peak capacity for an increased time. Data analytics can potentially do a lot more than just pointing out production bottlenecks. Gaming firms leverage algorithms of data analytics to create player-centric schedules that keep most players involved in the game. Many of the same data analytics algorithms are leveraged by firms like YouTube and Facebook to keep you clicking, watching, or re-organizing content to get another view or another click.
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Now that you have understood what is Data Analytics, experts that provide Data Analytics assignment help have provided the information below to help you understand the processes of Data Analytics. However, data analytics is a nuanced field, and it is always better to get it done by a professional. Experts at My Assignment Services are all professionals in their respective fields. You could connect with these experts via My Assignment Services and have them write a stellar assignment for you.
It is a process to identify, formulate, and prioritize the data that would be needed to fulfil the business objectives. The data that is necessary as inputs for the analysis process is based upon a business question or speculated results. With this process, we obtain the specific variables needed to be filtered out for further processing and modelling.
The method of collecting information on specific variables known as data requirements is data collection. The focus is on ensuring accurate and honest data collection. Data Collection ensures the accuracy of the data collected so that the related decisions are valid. Data collection offers both a measuring baseline and an enhancing target.
Data is supposed to be collected from numerous sources that may range from the organizational databases to the databases that index web pages. Thus, the data could be heterogeneous in nature and unstructured as well. To derive any knowledge out of the data, the data first needs to be smoothed out to a heterogeneous dataset. Thus, the data that gets collected must first be subjected to rigorous cleaning and processing.
The data collected would have to be analyzed or arranged for analysis. This includes structuring the data for the relevant Analysis algorithms as required. For example, it may be appropriate to position the data in rows and columns in a table within a Spreadsheet or Statistical application.
The data collected may be incomplete, have duplicates or may contain a few errors. Data Cleaning is the mechanism to prevent and fix these errors. There are several Data Cleaning types that depend on the data type. For example, some figures can be measured against accurate published statistics or specified thresholds when cleaning up the financial data.
After being passed through all of these stages of cleaning, organizing, and processing that data would be ready for analysis. Numerous data analysis algorithms can be leveraged to understand and derive conclusions or interpretable knowledge out of the data as per the requirement. The analysed data can then be presented in the form of graphical formats called data visuals and data maps that help generate further insights.
Statistical data models including Correlation, Regression Analysis can be used to classify data variables relationships. These models that visualize the data are useful in simplifying the analysis and in communicating the results. This step might require further cleaning, and thus, these processes are generally considered to be iterative in nature.
Data analytics is of paramount concern these days because it helps companies maximize their performance. Integrating it with the business model means that businesses can help numerous businesses cut costs by identifying optimized ways of operations and by storing large amounts of data. An organisation also uses this data to further finesse its business decisions and to draw their customer trends and track consumer behaviour.
Descriptive analytics primarily focuses on summarizing and compiling the activities of the organization. The algorithms would examine raw data to answer questions related to business intelligence and organizational management. For example, the credit risk of an organisation could be represented by Pie charts, line graphs, or by generated narratives. In such cases, banking firms leverage the data derived from the past of their client to estimate and predict loan risk and mitigate other portfolio risks. Descriptive analytics would also provide insights into the sales cycle such as categorizing customers based on their preferences
Where the majority of the data analytics tools and algorithms can provide oversight or an estimated outcome of an event, prescriptive analysis can provide an answer with laser-precision to the questions related to business intelligence. For example, one could get precise answers to the number of clinically obese patients that would probably develop diabetes in a year and further finesse the model to predict the number of patients that would seek medical help from a particular hospital and buy medicines from a particular store. With this kind of pinpoint accuracy, one can almost make risk-free decisions for the business. However, the development of these models consume time and it takes years to collect data for analysis.
Fundamentally, Exploratory analytics is an analytical approach that lays a prime focus on identifying patterns in un-processed data to identify outliers and characteristics that may otherwise, would not have been identified using other types of analytics. To use this strategy, you need to consider where the outliers exist, and how other environmental factors contribute to informed decision making. For example, when working with biological data, the scatterplots and correlation coefficients provide essential insights about the information on the relationship between all of the variables.
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