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Data analysis is a major part of industries where data-driven decisions are made by using the information gathered. In data analysis, a large amount of data is collected, organised and evaluated to get clear insights, trends and patterns that are useful. McKinsey says, “The data-driven organisations are significantly more successful in customer acquisition and retention”. It is in demand and is taught as a subject in many degrees or as a specialised course. Many students need data analysis assignment help to navigate the challenges associated with it.
Students can solve many of their problems with the expert-crafted assignment on the data analytics process. The assignment assistance helps them understand concepts like big data, data cleaning and many more such complicated concepts.
Statista says, “The global data analytics market is rapidly growing, projected at $77.9 billion by 2026”. No wonder data analysis skills are in demand. As per Burtch Works, “the median salary for data scientists in the US is $146,000”. This is one of the major reasons why many students study data analytics. In this section, we will explain the detailed process of data analysis.
The 7 steps of data analysis are important for drawing meaningful insights from transforming raw data by implementing a systematic process. They are
The above steps systematically cover the entire process of data analysis. Our experts can help you with each of these steps.
Various types of data analytics methods are employed for different levels of tasks. We have discussed them in brief.
This is the simplest form of data analysis. It identifies trends, relationships and patterns by using current and past data. It helps businesses understand past events and inform them of the current trends.
It digs deeper into the patterns and trends and helps us understand why things happen. It compares input and output data to establish cause-and-effect relationships. It helps businesses avoid repeating mistakes.
It analyzes historical and current data and predicts, “What can we expect next”. It makes use of machine learning, data mining and statistical modeling. It uses techniques like forecast models, clustering, etc.
It is a step ahead of prediction. This is used to achieve desired goals by implementing actionable recommendations. The solutions are based on complex algorithms and models.
Based on the criteria of your research, you can use the preferred data analysis methods.
The 5 W’s are important to understand the context and to answer the basic questions before embarking on the data analysis process. See below:
Who
The first question to be asked is “ Who is the data about?”. By identifying the individuals, groups or entities, we understand where the focus should be. Those are the target audience.
What
Next up, try to understand what type of data you are working on. By knowing that, you can choose the ways or techniques to analyse it efficiently. For example, to analyse website traffic, you can use Google Analytics.
When
The time frame when the data was collected is very important in data analysis. As trends differ from time to time. Knowing the timing of the origin of data gives you more clarity to understand the patterns.
Where
It is crucial to know where the data came from to decide whether the source is reliable or not. Then, you can determine the accuracy of the data. There are many ways to gather data, like surveys, social media, government reports, etc.
Why
One of the main questions is “Why are we analysing the data?”. The reason is crucial to understand the purpose behind the data analysis process. For instance, a company might want to launch a new service and thus analyse market conditions.
By knowing these 5 W’s, you are on the right track to do a data analysis assignment.
Next up, we have the 5 Vs, which help data analysts work on big data. They need to comprehend these 5 V’s to assess the complexity, challenges and opportunities associated with the large dataset. They are -
Volume refers to the amount or size of the data. It can be regarded as big data if the size is huge enough. However, the exact parameters to be considered big data is relative and will change depending on the available computing power.
It refers to the speed at which data is generated and how fast it is processed. Organisations need to flow their data quickly so that they can make quick business decisions based on data. For instance, stock markets require data to be available at the right times for quick decisions.
This defines the types of data. Data can be structured like databases or unstructured such as images, videos or semi-structured like web pages. There are different techniques to handle various types of data.
Veracity means to know the trustworthiness of data. The quality and accuracy of data is measured, and data is cleaned. It is important to remove all the errors, biases and inconsistencies. Example - Fake news can be misleading.
The value means the meaningful insights and the benefits you can derive from the data. The data that can improve business processes and bring more customers is the priority of organisations.
You should understand these basics behind the data analysis process well so that you can master the advanced concepts. Our experts have mastery over the entire data analysis process. So, take data analysis assignment help from them if you are stuck.
There are many data analysis tools available which you can use to work efficiently. These are mentioned below in the table.
| Type of assignment | Used For | Recommended Tools |
|---|---|---|
| Basic analysis and visualisation | For simple tasks like making charts, diagrams, organising, collecting and updating data, pivot tables, etc. | Google sheets, Ms Excel |
| Web analytics | To understand user engagement and website traffic. Used for checking performance metrics. | Google analytics |
| Business intelligence | Widely used for visualising data and making interactive dashboards. | Power BI, Tableau |
| Programming and advanced analysis | To do statistical analysis and more programming-based data analysis. | R, Python, SPSS, Stata, SAS |
These tools are widely used by our experts while offering data analysis assignment help to students. You can make use of these tools to work on your data analysis assignment and enhance its quality.
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use discountIf you use ChatGPT for a data analysis assignment, then your assignment might be rejected on the grounds of plagiarism. So it’s better to avoid depending entirely on AI tools. However, you can use them for understanding and learning difficult concepts.
Firstly, you should use the right tools for that, like R, Python, Excel, Power BI, Tableau, SQL, Tableau, etc. Then, you should focus on cleaning and arranging data properly. Incorporate a structured process like a 7-step data analysis process. All these things make data analysis easier.
They are defining the problem, collecting data, data preprocessing, analysing the data, evaluating outcomes, data visualising, and making data-driven decisions. If you need help with any of the steps, get our data analysis assignment help from experts.
Descriptive, diagnostic, predictive and prescriptive analytics are the 4 areas of data analysis. They have different use cases. For instance, predictive analytics focuses on forecasting future outcomes using machine learning and statistical models.
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